# Scalifi Ai – llms-full.txt Canonical: https://www.scalifiai.com Last-Updated: 2026-07-02 For quick index, see https://www.scalifiai.com/llms.txt --- ## Advanced Model Design and Export Capabilities | Scalifi Ai Source: https://www.scalifiai.com/model-catalog/features/advanced-model-design-and-export Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Advanced Model Design andExport Capabilities Customize and optimize AI models to their precise specifications and operational requirements. Rapidly prototype, test, and refine AI models within a no-code environment, significantly accelerating development cycles and promoting innovation through easy experimentation. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Create Design From Model Signature Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/model_catalog_service_create_design_from_model_signature_668fbe9b8a.svg) ![quick-test-of-ai-models-model-catalog-service.png](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2Fquick_test_of_ai_models_model_catalog_service_e20cd21b13.png&w=128&q=75) ##### Quick test of AI models Instantly test AI models against your requirements and enhance the outcome within a no-code AI platform. ![workload_reduction.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/workload_reduction_70ee6d5410.svg) ##### Reduction in workload The integration with [Model Building Service](https://www.scalifiai.com/ai-ml-model-building/) offers a streamlined AI model creation, minimizing an effort and workload in creating model design from scratch. ![focus-on-specific-requirements-model-catalog-service.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/focus_on_specific_requirements_model_catalog_service_e6008574d3.svg) ##### Focus on specific requirements Customize the AI model structure to a specific requirement to quickly test and verify it. ![Rapid Prototyping and Iteration - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/rapid_Prototyping_Mbs_b4256048eb.svg) ### Create model designs from registered model variants Model Catalog empowers data scientists to create new models with the integration of [Model Building Service](https://www.scalifiai.com/ai-ml-model-building/), which offers a fluid experience to create model designs from registered model variants. Quickly test, verify, and refine AI models using a no-code canvas. This will significantly accelerate development cycles and promote innovation through easy experimentation by customizing existing registered model variants. ![Rapid and Flexible Model Export - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/rapid_Model_Export_63e6f6ecda.svg) ### Export models to deploy in various environments Simplify your workflow and unlock the full potential of your models across multiple deployment scenarios. Easily exporting and deploying models across various environments ensures that models are not only tailored for performance but also for scalability and integration into production systems, enhancing the operational efficiency and effectiveness of AI initiatives. ![Customized Model Structure Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/customized_model_structure_model_catalog_service_4cbd27136d.svg) ### Customizing model structures Fine tune your model by adjusting the parameters using layer configuration within a no-code AI environment. With intuitive tools and a no-code interface, data scientists can modify existing model variants, seamlessly integrating diverse functionalities and refining performance and tailor your model to specific tasks or requirements. Customizing Model Structures feature promotes agility and creativity in model development. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Efficient AI Modeling Jobs | Scalifi Ai Source: https://www.scalifiai.com/ai-ml-model-building/features/efficient-ai-modeling-jobs Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Master AI Model Developmentwith MBS Model Jobs Elevate AI development with MBS's Model Jobs. Efficiently build, test, and analyse large models with Build Job, and seamlessly export them in various formats using the Export Job for versatile deployment. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Model Building Jobs - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/MBS_Efficient_Ai_Modeling_Job_7ca0a2d722.svg) ![streamline-model-building.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/streamline_model_building_282b2cecfa.svg) ##### Streamlined Model Building MBS's Build Job optimises large AI model construction and testing, enhancing team productivity and efficiency. ![export-integration.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/export_integration_25ce293cf8.svg) ##### Seamless Export and Integration Export Job in MBS enables flexible model exporting and easy integration across diverse platforms and environments. ![automate-time-execution.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/automate_time_execution_7758913aca.svg) ##### Automated and Timely Execution Schedule and automate MBS Model Jobs; receive timely updates via email, ensuring efficient project management. ![Enhanced Development Efficiency Model Building Job - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/optimize_Ai_Model_82a073ee98.svg) ### Enhanced Development Efficiency MBS’s Build Job feature significantly streamlines the AI model development process. By enabling efficient construction and testing of complex models, it reduces redundant activities, allowing your workforce to focus on innovation and strategic tasks. This leads to enhanced productivity and a more focused approach to AI development. ![Rapid and Flexible Model Export - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/rapid_Model_Export_63e6f6ecda.svg) ### Rapid and Flexible Model Export The Export Job in MBS redefines model deployment flexibility. It facilitates the quick export of AI models in various formats, easily integrating with different systems and platforms. This agility allows for faster response to customer queries and adaptability in diverse technological environments. ![Efficiently Meet Project Deadlines Model Build Job - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/project_Deadlines_d32bc7225b.svg) ### Efficiently Meet Project Deadlines MBS's scheduling feature for Model Jobs ensures timely execution and adherence to service level agreements (SLAs). Automated scheduling and email updates keep projects on track, guaranteeing efficient management and delivery of AI model development projects within set timelines. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Model Catalog | Scalifi Ai Docs Source: https://www.scalifiai.com/docs/model-catalog Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) menu\_open # Model CatalogDocumentation Hub The vault to explore all the features of Scalifi Ai’s Model Catalog. [Try for free](https://www.platform.scalifiai.com/register) [Book a demo](https://www.scalifiai.com/contact-us) ![Scalifi Ai Documentation Hub](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/iam_docs_image_4d46301902.svg) ## Overview Welcome 👋 to the Scalifi Ai’s Model Catalog Documentation Hub. A model catalog is essentially a central library for all the machine learning models used within an organization. It acts like an organized inventory, keeping track of various aspects of these models through metadata. This metadata can include details like versions, variants and its signature configuration, tags, description etc. ## Model Catalog Docs [view\_in\_arModel Managementeast](https://www.scalifiai.com/docs/model-catalog/model-management) [data\_objectMetadataeast](https://www.scalifiai.com/docs/model-catalog/metadata) [categoryModel Versioneast](https://www.scalifiai.com/docs/model-catalog/model-version) --- --- ## Low Code No-Code Ai Platform | ML Usecases | Transformers Ai Source: https://www.scalifiai.com/usecases Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) ![https://scalifiai-cms.nyc3.digitaloceanspaces.com/usecase_banner_left_56258f4ec9.svg](https://scalifiai-cms.nyc3.digitaloceanspaces.com/usecase_banner_left_56258f4ec9.svg) # Use Cases Unlock the potential of AI: Explore transformative use cases across industries with Scalifi Ai's seamless, no-code solutions. ![https://scalifiai-cms.nyc3.digitaloceanspaces.com/usecase_banner_right_274e389292.svg](https://scalifiai-cms.nyc3.digitaloceanspaces.com/usecase_banner_right_274e389292.svg) ![customer-churn-prediction.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/customer_churn_prediction_52c950f85f.svg) ##### Customer Churn Prediction Take proactive measures to re-engage buyers who are likely to churn [Read more](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) ![ai-in-cyber-security-and-mfa.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/ai_in_cyber_security_and_mfa_b3b2824058.svg) ##### AI in Cyber Security to Redefine the Security Posture Real-time Anomaly Detection and Authentication with AI and Multi-factor Authentication for Enhanced Cyber security [Read more](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) ![credit_card_fraud_1.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/credit_card_fraud_1_f3c7e18d4e.svg) ##### Credit Card Fraud Detection Prevent Modern Credit Card Frauds With the Power of AI and Regain Customer Trust & Loyalty [Read more](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) ![revenue_prediction_1.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/revenue_prediction_1_ee30c005fc.svg) ##### Revenue Prediction Unlock Precise Revenue Forecasting with Advanced AI Models Leveraging your Historical & Real-Time Data [Read more](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Model Version | Model Catalog Guide Source: https://www.scalifiai.com/docs/model-catalog/model-version Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # Model Version Model versions refer to distinct iterations of a machine learning model, each capturing specific configurations and training states. This versioning system facilitates model tracking, comparison, and reproducibility, allowing data scientists to experiment, optimize, and collaborate effectively in the dynamic field of AI development. Allowed operations that users can perform on the platform are: 1. Add Model Versions 2. Add Model variations 3. Add Model variations Tags 4. Delete Model variations Tags 5. Model variation Metadata 6. Delete Model variation 7. Remove Model Version But before moving on to the operations of model version lets see what is the difference between model version and model variation. ## insert\_linkModel Versions vs. Model Variations ### insert\_linkModel Version 1. A **model version** represents a specific iteration or generation of a model. It is typically a snapshot of the model at a particular point in its lifecycle, often marked by significant changes or improvements made to the model's architecture, training data, or hyperparameters. Each model version encapsulates the complete setup needed to replicate the model's performance. 2. **Example**: Consider a facial recognition model. The initial release, trained on a limited dataset, is designated as Version 1.0. Over time, as the training dataset is expanded with more diverse images, the model is retrained to improve accuracy and robustness, resulting in Version 2.0. 3. **Use Case**: Model versions are used to track the evolution of a model over time. This allows developers and data scientists to deploy specific versions based on performance, compliance with regulations, or compatibility with specific applications. ### insert\_linkModel Variation 1. A **model variation** refers to different configurations or customizations of a given model version within a specific framework. Variations might include changes in configuration settings, use of different layers or activation functions, or adjustments specific to deployment environments. Each variation within a version/framework combination is identified by a unique variation name. 2. **Example**: Continuing with the facial recognition model in Version 2.0, you might have different variations to address different operational needs: - **Variation A**: Configured for real-time processing on mobile devices, with a focus on speed over accuracy. - **Variation B**: Optimized for high accuracy in controlled environments, suitable for security systems. 4. **Use Case**: Model variations allow for fine-tuning and optimizing a model version to meet specific requirements or constraints of different deployment scenarios. They enable organizations to leverage the same underlying model architecture and training, while adapting to diverse needs and conditions. ## insert\_linkSupported Frameworks 1. A model version can have multiple frameworks, and each framework might consist of multiple variations with different configurations or customizations. 2. The platform currently facilitates a few frameworks, with additional ones currently in development and slated for imminent release. The frameworks list is: - Pytorch - Keras - Tensorflow - Scikit Learn \[Coming Soon\] - Transformers \[Coming Soon\] - Fastai \[Coming Soon\] - LightGBM \[Coming Soon\] - XGBoost \[Coming Soon\] - Prophet \[Coming Soon\] ## insert\_linkAdd model versions **Add versions using client library:** Please note, the creation of model versions is supported solely via our proprietary library or client library tool. ## insert\_linkAdd model variations **Add variations using client library:** Please note, the creation of model variations is supported solely via our proprietary library or client library tool. ## insert\_linkAdd model variation tags 1. Under the Model Version section, expand the **Model Variation Configuration** section. 2. Click on **Manage tags** ![Add Tags To Model Versions - Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/add_tags_model_versions_scalifi_ai_9f4c72a573.svg) 4. To add multiple tags, use the Add tag button. If any tag is not needed you can delete it by clicking on the delete button on the right side of the tag. ![Manage Variant Tags Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/manage_model_variant_tags_scalifi_ai_a7bd66300b.svg) 6. Finally, click on Submit to add the tags in a model. ## insert\_linkRemove model variation tags 1. Once the tags are removed from the model variation, you will no longer be able to categorize, search or filter that variation in relation to the tag key or values. Before deleting the tags make sure you no longer need those tags. 2. To remove model tags, simply click on the cross icon (X) of a tag. ![Remove Model Variant Tag - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/remove_model_variant_tag_scalifi_ai_c2790e2223.svg) 4. To remove all model tags at once, click on the Remove all tags button. 5. Confirm the removal process and click on Confirm ![Remove All Tags Model Variant - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/remove_all_tags_model_variant_scalifi_ai_c9170611c9.svg) ## insert\_linkModel variation metadata Refer the [metadata guide](https://www.scalifiai.com/docs/model-catalog/metadata) for managing the model variation metadata. ## insert\_linkDelete Model Variation 1. Select the variant to delete from the dropdown. Then click on the **Delete variant** button. ![Delete Model Variant Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/delete_model_variant_scalifi_ai_e334824033.svg) 3. Confirm the deletion process and click on **Confirm**. ![Confirm Model Variation Deletion Process Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/confirm_delete_model_variation_scalifi_ai_c9a1c9e91c.svg) ## insert\_linkDelete Model Version 1. Select the version to delete from the dropdown. Then click on the **Delete version** button. ![Delete Model Version Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/delete_model_version_scalifi_ai_009928c9b7.svg) 3. Confirm the deletion process and click on Confirm ![Confirm Model Version Deletion Process Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/confirm_delete_model_version_scalifi_ai_69a9f96c01.svg) Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [PreviouswestMetadata](https://www.scalifiai.com/docs/model-catalog/metadata) [NextUsageeast](https://www.scalifiai.com/docs/billing-and-usage/usage) ##### Content [Overview](https://www.scalifiai.com/docs/model-catalog/model-version#overview) [Model Versions vs. Model Variations](https://www.scalifiai.com/docs/model-catalog/model-version#model-versions-vs.-model-variations) [\- Model Version](https://www.scalifiai.com/docs/model-catalog/model-version#model-version) [\- Model Variation](https://www.scalifiai.com/docs/model-catalog/model-version#model-variation) [Supported Frameworks](https://www.scalifiai.com/docs/model-catalog/model-version#supported-frameworks) [Add model versions](https://www.scalifiai.com/docs/model-catalog/model-version#add-model-versions) [Add model variations](https://www.scalifiai.com/docs/model-catalog/model-version#add-model-variations) [Add model variation tags](https://www.scalifiai.com/docs/model-catalog/model-version#add-model-variation-tags) [Remove model variation tags](https://www.scalifiai.com/docs/model-catalog/model-version#remove-model-variation-tags) [Model variation metadata](https://www.scalifiai.com/docs/model-catalog/model-version#model-variation-metadata) [Delete Model Variation](https://www.scalifiai.com/docs/model-catalog/model-version#delete-model-variation) [Delete Model Version](https://www.scalifiai.com/docs/model-catalog/model-version#delete-model-version) * * * hide\_imageHide Images --- --- ## Cognis Ai (Beta) Update Note: New LLMs and Custom Configurations are Now Live on Cognis Ai Source: https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Cognis Ai (Beta) Update Note: New LLMs and Custom Configurations are Now Live on Cognis Ai 14 April 2026\|5 min read ![Test out custom configuration across reasoning levels, temperature and verbosity on Cognis Ai ,for models like GPT 5.2, Gemini 3 Pro, etc. ](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FCustom_Configuration_on_Cognis_Ai_ba4a7e6af4.png&w=3840&q=75) _Custom Configuration on Cognis Ai_ This week, we’ve added new LLMs, including GPT 5.1, GPT 5.2, Gemini 3 Pro, and Gemini 3 Flash. Now you can also use custom configurations to tune your LLMs' responses in terms of reasoning, summary, temperature, and verbosity. ## insert\_linkWhy Does Custom Configuration Matter? Across recent interactions we discovered a need for optimizing model and token usage across LLMs. While newer models like Gemini 3 Pro and GPT 5.2 outperform predecessors on benchmarks, the ability to expend all reasoning capabilities of such powerful models, is limited. When not using custom configuration it leads to: 1. Over-usage of reasoning capabilities, which leads to higher latency per response 2. Cost inflation due to higher levels of token usage both during reasoning and in generated outputs This difference is noticeable when executing heavier tasks using LLMs like Claude, Gemini, or GPT. **Using custom configuration allows you to bypass these bottlenecks and unlock cost savings of up to 40%**, depending on usage type and limits. ## insert\_linkHow Does Custom Configuration Work? Custom configuration allows you to edit a model’s internal tuning across six key areas: **1\. Response Mode:** Allows you to control how an LLM shapes its responses. You can change it across the following levels to achieve higher levels of precision or expressiveness: - **Focused:** Concise, direct, and highly deterministic answers. - **Accurate:** Prioritizes clarity and correctness with balanced detail. - **Balanced:** Natural and well-rounded responses, most suitable when executing tasks using LLMs. - **Imaginative:** Expressive, creative, and exploratory responses fit for brainstorming. _Custom Configuration on Cognis - Response Modes_ **2\. Reasoning:** Controls the level of internal reasoning effort expended by a model before answering any query. The two levels of reasoning, as follows, are best suited to handle particular scenarios: - **Low:** Best for low-level single-step tasks such as simple content generation or linear data analysis. - **High:** Best for multi-step tasks such as financial forecasting, heavy content generation, etc. Latency in this setting is generally higher due to the incremental number of reasoning cycles, depending on the complexity of tasks. **3\. Summary:** This feature tracks the internal reasoning of models. You can switch it on or off depending on the level of tasks that you assign to an LLM agent. We recommend keeping it turned on for complex multi-step tasks, allowing you to interrupt the model if it takes wrong steps. **4\. Temperature:** The temperature of a model varies between 0.0 and 2, as shown in the image below, with 0.0 being the most deterministic response level and 2 being the most creative and diverse response level. _Advanced Configurations for LLMs in Cognis Ai_ You can adjust the model temperature depending on the kind of task you’re undertaking. For instance, creative tasks like writing blogs tend to work better with temperatures above 1, while technical writing tasks work better with temperatures below 1. **5\. Verbosity:** Controls the level of detail the model includes in its responses. It has three levels depending on the length of output that you want: - **Low:** Produces short and concise answers. - **Medium:** Produces average responses as you get on native LLM interfaces. - **Balanced:** Natural and well-rounded responses, most suitable when executing tasks using LLMs. - **High:** Produces longer, more detailed answers. Best suited for long-format content or report generation. **6\. Thinking Budget\*:** This feature is only available for Gemini 2.5 Pro and governs how much internal planning the LLM performs before it begins generating a response. 128 is the lowest thinking budget, and 32,768 is the highest thinking budget available. ## insert\_linkNew Models on Cognis Ai and Custom Configuration The new LLM models added to Cognis Ai include: - GPT 5.2 - GPT 5.1 - Gemini 3 Pro - Gemini 3 Flash With the latest addition of models and the introduction of custom configuration, you can now optimize your token usage to spend less on task execution via LLMs on Cognis Ai. While custom configuration amongst models is pretty much standardized, they can vary by model generation. Below is a table for reference on the capabilities of each model. **Model Name** **Thinking Mode** **Response Mode** **Reasoning** **Summary** **Temperature** **Verbosity** **Thinking Budget** GPT 5.2 Yes Yes Yes Yes Yes Yes Not Supported by Model GPT 5.1 Yes Yes Yes Yes Yes Yes Not Supported by Model GPT 5 Always Enabled Not Supported by Model Always Enabled Yes Not Supported by Model Yes Not Supported by Model GPT 5-Mini Always Enabled Not Supported by Model Always Enabled Yes Not Supported by Model Yes Not Supported by Model GPT 5-Nano Always Enabled Not Supported by Model Always Enabled Yes Not Supported by Model Yes Not Supported by Model GPT o4-Mini Always Enabled Not Supported by Model Always Enabled Yes Not Supported by Model Not Supported by Model Not Supported by Model GPT 4.1 Not Supported by Model Yes Not Supported by Model No Yes Not Supported by Model Not Supported by Model GPT 4.1 Mini Not Supported by Model Yes Not Supported by Model Not Supported by Model Yes Not Supported by Model Not Supported by Model GPT 4.1 Nano Not Supported by Model Yes Not Supported by Model Not Supported by Model Yes Not Supported by Model Not Supported by Model GPT o3 Mini Always Enabled Not Supported by Model Always Enabled Not Supported by Model Not Supported by Model Not Supported by Model Not Supported by Model GPT o1 Always Enabled Not Supported by Model Always Enabled Not Supported by Model Not Supported by Model Not Supported by Model Not Supported by Model GPT 4o Not Supported by Model Yes Not Supported by Model Not Supported by Model Yes Not Supported by Model Not Supported by Model GPT 4o Mini Not Supported by Model Yes Not Supported by Model Not Supported by Model Yes Not Supported by Model Not Supported by Model GPT 3.5 Turbo Not Supported by Model Yes Not Supported by Model Not Supported by Model Yes Not Supported by Model Not Supported by Model Gemini 3 Flash Always Enabled Yes Yes Yes Yes Not Supported by Model Not Supported by Model Gemini 3 Pro Always Enabled Yes Yes Yes Yes Not Supported by Model Not Supported by Model Gemini 2.5 Flash Lite Yes Yes Yes Not Supported by Model Yes Not Supported by Model Not Supported by Model Gemini 2.5 Flash Lite Yes Yes Yes Not Supported by Model Yes Not Supported by Model Not Supported by Model Gemini 2.5 Pro Always Enabled Yes Yes Yes Yes Not Supported by Model Yes Gemini 2.0 Flash Lite Not Supported by Model Yes Not Supported by Model Not Supported by Model Yes Not Supported by Model Not Supported by Model Gemini 2.0 Flash Not Supported by Model Yes Not Supported by Model Not Supported by Model Yes Not Supported by Model Not Supported by Model ## insert\_linkExperience How LLM Configuration Works on Cognis AI (Arcade) Click here to watch the interactive demo Using Custom Configuration for Optimized Responses [![Using Custom Configuration for Optimized Responses](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/https://image.mux.com/CB00DTM01P14wuRCNipEMcqQmws8Pg2vTQe2dR8CiHirw/thumbnail.webp?time=0)](https://app.arcade.software/share/LIYwFCRczE0bcPwrkyVv?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Using Custom Configuration for Optimized Responses** **Check out how to use custom configuration on Cognis to generate varying levels of responses**](https://app.arcade.software/share/LIYwFCRczE0bcPwrkyVv?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ### Getting Started You can Get Started for free today with Cognis Ai’s Community Plan. Please visit [Cognis Ai](https://www.scalifiai.com/cognis-ai) and click on Get Started to begin your journey. Once you enter Cognis, you can use the quick setup to activate the popular LLMs in your account. Further, you can also configure more LLMs from the ‘Explore LLMs’ tab on the bottom left. ### Our Commitment to Human-AI Collaboration With Cognis Ai, we focus on developing a complementary AI system that helps you derive tangible results from AI. Our ethos while building Cognis is to advance the state of Human-AI collaboration to help people execute faster, remove redundancies, and focus on meaningful work that moves the needle forward. Our future roadmap intends to add features, integrations, and tools that help reduce the noise surrounding AI and focus on outcomes. For updates, follow us on [LinkedIn.](https://www.linkedin.com/company/scalifiai) #### Frequently Asked Questions ###### 1\. What is Cognis? Cognis is a workflow automation platform and AI chat assistant built to simplify your worklife. It uses Agentic AI and intelligent agents to automate repetitive tasks, create complete workflows, and act as your artificial intelligence personal assistant. With features like intuitive UI, near-zero hallucination for LLMs, and support for multiple AI models, Cognis combines the best of AI and automation into a single interface. ###### 2\. What is Custom Configuration? Custom configuration is a set of parameters that you can tweak to change an LLMs behaviour according to your requirement. There are 6 settings that you can change across all models: Response Mode, Reasoning, Summary, Temperature, Verbosity, and Thinking Budget. For every model you can adjust a few of these settings to get the desired response type. Refer to the [table above](https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI#new-models-on-cognis-ai-and-custom-configuration) to check the settings that are available for each model. ###### 3\. Why is Custom Configuration useful? Custom configuration is primarily useful because it helps you optimize your token usage. For example, if you're using a larger model like GPT 5.2 for menial tasks without custom configuration, you'll end up spending more because it might overthink the task at hand. Alternatively, it may also generate a longer response when not needed. This results in higher token usage. With custom configuration, you can turn on lower reasoning and verbosity settings to avoid such situations and optimize cost. ###### 4\. What LLMs can I use in Cognis AI? You can use multiple LLMs inside Cognis AI. The platform is designed as a multi-LLM orchestration layer, so you’re not locked into one provider. Supported LLMs include OpenAI models (GPT-5, GPT-4o, GPT-4 Turbo, GPT-3.5 series, o-mini family). Anthropic Claude models (Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku) Google Gemini models (Gemini 1.5 Pro, Gemini 1.5 Flash), DeepSeek (latest V3 and V3.1 reasoning models) and more models to be added soon. ###### 5\. What makes Cognis different from other AI apps? Most AI apps or automation apps are single-function. Cognis combines AI chat, agent software, and workflow automation platform capabilities in one. Instead of juggling multiple AI programs or AI tools free/paid, you get one artificial intelligence app where agentic AI manages context, memory, and execution/ ###### 6\. Who should use Cognis? Indie Builders who hate administrative work. Startups with small teams and big dreams. Professionals needing a chatgpt AI assistant or artificial intelligence personal assistant. Developers who want to create an AI or experiment with AI programs in one place. Enterprises wanting to level up by automating workflows without the hassle of 2 AM 'nothing's working with the other' calls. ###### 7\. Does Cognis address AI bias? Yes. Cognis includes monitoring to reduce AI bias across Chat GPT-4, Chat GPT-3.5, and other best AI models. It helps ensure fair, transparent outputs when you use it as your AI assistant or automation app. #### External References 1. Cognis Ai (Beta) is live now. Explore the power of a Multi-LLM Agentic Ai platform that reduces your work time by 5x. [Read the Launch Note.](https://www.scalifiai.com/blog/cognisailaunch) 2. [Explore](https://www.scalifiai.com/cognis-ai) the power of Multi-LLM Agentic Ai Related: Annoucements ##### In this blog [Why Does Custom Configuration Matter?](https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI#why-does-custom-configuration-matter) [How Does Custom Configuration Work?](https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI#how-does-custom-configuration-work) [New Models on Cognis Ai and Custom Configuration](https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI#new-models-on-cognis-ai-and-custom-configuration) [Experience How LLM Configuration Works on Cognis AI (Arcade)](https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI#experience-how-llm-configuration-works-on-cognis-ai-(arcade)) [FAQs](https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs Walkthrough: Content Writing Assistant on Cognis Ai [Read Moreeast](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis) Walkthrough: How to Use Google Sheets on Cognis [Read Moreeast](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis) Cognis Ai (Beta): The Ultimate Agentic AI for all Your Professional Needs [Read Moreeast](https://www.scalifiai.com/blog/cognisailaunch) ###### Explore other usecases Customer Churn Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) Revenue Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Complete User Guide | Scalifi Ai Docs Source: https://www.scalifiai.com/docs Cognis AiLive Now. 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Navigate through our step-by-step guides, and valuable documentation for a seamless learning experience of modules like IAM, Model Building Server, Billing and Usage Center, Data Ingestion, Data Analytics etc. Dive deeper into a world of knowledge designed to enhance your understanding of our services in a more efficient way. ## Module-wise Docs [people\_alt**Identity and Access Management** \\ Safeguard and manage user identities and access privileges within Scalifi Ai, ensuring secure and efficient control over who can see and do what.](https://www.scalifiai.com/docs/iam) [construction**Model Building** \\ Leverage a no-code canvas to construct, test, and refine machine learning models with ease, streamlining the journey from concept to deployment.](https://www.scalifiai.com/docs/model-building) [payment**Billing and Usage** \\ Monitor and manage your financial interaction with Scalifi Ai, tracking service usage, viewing billing history, and customizing subscription plans for optimized spending.](https://www.scalifiai.com/docs/billing-and-usage) [inventory\_2**Model Catalog** \\ Explore our comprehensive Model Catalog, a centralized repository for versioned machine learning models. Unlock collaboration, reproducibility, and streamlined deployment in your AI workflow.](https://www.scalifiai.com/docs/model-catalog) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Scalifi Ai Pricing Plans for Model Building Source: https://www.scalifiai.com/pricing/model_building Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) ##### Explore Our Planssell ### One Platform. Multiple AI Workspaces. ### Automate the Work That Slows You Down. Unlimited free trial No card needed Cancel plans anytime No forced contracts ### Community [Free Trial](https://www.platform.scalifiai.com/register) ### Essential [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=model_building&plan=essential¤cy=INR) ### Professional [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=model_building&plan=professional¤cy=INR) ### Enterprise [Contact Us](https://www.scalifiai.com/contact-us) #### Modules [**Model Building**](https://www.scalifiai.com/pricing/model_building) [**Model Catalog**](https://www.scalifiai.com/pricing/model_catalog) [**Cognis Ai**](https://www.scalifiai.com/pricing/cognis_ai) Indian rupee (₹) Model Building Community [Free Trial](https://www.platform.scalifiai.com/register) Essential [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=model_building&plan=essential¤cy=INR) PopularProfessional [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=model_building&plan=professional¤cy=INR) Enterprise [Contact Us](https://www.scalifiai.com/contact-us) Price (per organization/month) Free ₹4000 ₹8000 Custom Included seats 1 3 6 Custom Max seats 1 7 12 Custom Price per additional seat Not applicable ₹900 ₹700 Custom Model Designs 4 40 100 Custom Model Jobs Stored 15 100 300 Custom Credits Allocated 10 150 800 Custom Note: - All listed prices are exclusive of applicable taxes. Please check with your bank regarding any forex charges. - All listed prices are only for monthly billing cycles. - † Included at no additional cost, subject to reasonable use under our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy). #### Frequently Asked Questions ![scalifi ai faqs](https://www.scalifiai.com/_next/static/media/faqCircle.3d68e398.svg) ## What is the Model Building Service? Scalifi Ai’s Model Building Service helps you streamline the process of constructing machine learning and AI models, saving time and resources. The Model Building Suite comes with a unique Drag and Drop canvas with Rapid Prototyping that allows you to discover model job requirements prior to submitting a build job. It also detects up to 99% semantic errors automatically. ## How does the Model Building Service pricing work? The prices mentioned above are monthly subscription costs for the Model Building Service. Our pricing is distributed into three pricing tiers. The Community plan is the free tier. This is a completely free trial, and you needn’t enter any payment details for this either. For indie/small teams, we recommend the Essential Plan. The Professional plan is generally recommended for mid-sized/small-enterprise teams. Please refer to the pricing table to explore all the features. In case the above-mentioned plans do not meet your needs, we offer custom pricing for enterprises depending on your usage limits. Please [Contact Us](https://www.scalifiai.com/contact-us) or email us at helpdesk@scalifiai.com. _\*Note: All the figures mentioned in the pricing table are excluding taxes._ ## Can I switch plans at any time I want? Yes, you can switch plans at any time that you want. Billing will be adjusted pro rata based on the current usage cycle. You can either upgrade or downgrade your plans. Upgrading a plan refers to when you move from a lower-value plan to a higher-value plan. Example: You move from Essential to Professional. When you upgrade, your billing cycle also changes to the same date of the request, and credits are allocated pro rata. Please refer to the rollovers and limits mentioned in the pricing table for more information. A downgrade refers to when you move from a higher value plan to a lower value plan. Example: You move from Professional to Essential. For downgrades, you have two options: an Immediate Downgrade starts the new, lower value plan right away. This changes your billing date to the day of the downgrade request, and you get no refund for the current cycle. Alternatively, you can choose an End-of-Cycle Downgrade to finish your current cycle, and the new plan will start on your next billing date, in which case your credits for the current month remain unchanged. ## What happens if my payment is overdue? If your payment is overdue, create/update actions are blocked after 4 days from the payment date of your recurring billing cycle. After 8 days, you are automatically shifted to the Community Plan, and all the features and resources tied to your current plan are deleted and reset. ## How is billing processed? When you submit a Quota Request, you have to make payments through the Razorpay gateway. A payment window of up to 72 hours is available to complete your payment. ## In what currency do I have to make the payment? You can make the payment in your local currency. Currently, all the prices mentioned for all the plans are in Indian Rupees (INR) and are \*exclusive of taxes and any \*\*forex charges. _\*In case of a detailed breakdown for taxes, please view your invoice._ _\*\*In case of a detailed breakdown of forex charges, kindly get in touch with your banking partner._ ## How many IAM policies can I create? Currently, we do not have any limits on the number of policies that can be created. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. ## How many third-party accounts (TPAs) can I configure? Currently, we do not have any limits on the number of third-party accounts that can be connected. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. ## How many data sources can I connect? Currently, we do not have any limits on the number of data sources that can be connected. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. ## How many API Keys can I connect to my account? Currently, we do not have any limits on the number of API keys that can be connected. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. Experience the next gen platform ## Schedule a demo and witness the future [Request a Demo](https://www.scalifiai.com/contact-us) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Scalifi Ai Pricing Plans for Cognis Ai Source: https://www.scalifiai.com/pricing/cognis_ai Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) ##### Explore Our Planssell ### One Platform. Multiple AI Workspaces. ### Automate the Work That Slows You Down. Unlimited free trial No card needed Cancel plans anytime No forced contracts ### Community [Free Trial](https://www.platform.scalifiai.com/register) ### Essential [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=cognis_ai&plan=essential¤cy=INR) ### Professional [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=cognis_ai&plan=professional¤cy=INR) ### Enterprise [Contact Us](https://www.scalifiai.com/contact-us) #### Modules [**Model Building**](https://www.scalifiai.com/pricing/model_building) [**Model Catalog**](https://www.scalifiai.com/pricing/model_catalog) [**Cognis Ai**](https://www.scalifiai.com/pricing/cognis_ai) Indian rupee (₹) Cognis Ai Community [Free Trial](https://www.platform.scalifiai.com/register) Essential [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=cognis_ai&plan=essential¤cy=INR) PopularProfessional [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=cognis_ai&plan=professional¤cy=INR) Enterprise [Contact Us](https://www.scalifiai.com/contact-us) Price (per organization/month) Free ₹5000 ₹9500 Custom Included seats 1 3 6 Custom Max seats 1 7 12 Custom Price per additional seat Not applicable ₹1000 ₹850 Custom Credits Allocated 1000 35000 75000 Custom Credits rollover Not applicable 50% 65% Custom Credits rollover limit Not applicable 17500 48750 Custom Multi-LLM Intelligence− 20+ Major LLMs Supported Live Now: Open AI & Gemini Models Coming Soon: Claude, Deepseek and more. done done done done Advanced In-Chat Branching− Use different LLMs for different tasks in the same chat window done done done done Maintain complete branch history done done done done Adaptive UI Engine− Direct in-chat action buttons done done done done Live previews done done done done Dynamic and contextual UI components done done done done Instant Execution− Prompt to execution within seconds done done done done Team Collaboration− Scale from personal to organization-wide use done done done done Granular access control (per integration, per LLM, per feature) done done done done Bring Your Own API− Connect and use own API keys done done done done Direct billing control for LLM usage done done done done Data Integration Sources− Number of Data-Sources Included (high-cap)† Included (high-cap)† Included (high-cap)† Included (high-cap)† Identity & Access Management− Policies Included (high-cap)† Included (high-cap)† Included (high-cap)† Custom Third Party Accounts Included (high-cap)† Included (high-cap)† Included (high-cap)† Custom Max Policy Attachment Per User Included (high-cap)† Included (high-cap)† Included (high-cap)† Custom Platform API Keys Included (high-cap)† Included (high-cap)† Included (high-cap)† Custom Universal Integrations− Live now: Google Sheets Coming Soon: Mailchimp, Zoho, Google Workspace, and 500 more. Coming Soon Coming Soon Coming Soon Coming Soon Dynamic Observability - 100% Context Control− Context Summarization + Selection Coming Soon Coming Soon Coming Soon Coming Soon Step Tracker Coming Soon Coming Soon Coming Soon Coming Soon Persistent Memory View Coming Soon Coming Soon Coming Soon Coming Soon Workflow Automation− Multi-step workflows condensed into single prompts Coming Soon Coming Soon Coming Soon Coming Soon Stateful Memory− Persistent context awareness Coming Soon Coming Soon Coming Soon Coming Soon Learns from user interactions Coming Soon Coming Soon Coming Soon Coming Soon Eliminates inconsistencies across sessions Coming Soon Coming Soon Coming Soon Coming Soon Note: - All listed prices are exclusive of applicable taxes. Please check with your bank regarding any forex charges. - All listed prices are only for monthly billing cycles. - † Included at no additional cost, subject to reasonable use under our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy). #### Frequently Asked Questions ![scalifi ai faqs](https://www.scalifiai.com/_next/static/media/faqCircle.3d68e398.svg) ## What is Cognis? Cognis is an Agentic Orchestration platform that serves as an AI assistant that's built to simplify your worklife. It uses Agentic AI and intelligent agents to automate repetitive tasks, so that you get time to focus on what actually matters. With features like an Interactive Rich UI, 100% context control for LLMs, and support for multiple AI models, Cognis combines the best of AI and automation into a single interface. Cognis also serves as a central hub or command center for your work life. ## How does Cognis’ pricing work? The prices mentioned above are monthly subscription costs for Cognis. Our pricing is distributed into three pricing tiers. The Community plan is the free tier. This is a completely free trial, and you needn’t enter any payment details for this either. For indie/small teams, we recommend the Essential Plan. The Professional plan is generally recommended for mid-sized/small-enterprise teams. Please refer to the pricing table to explore all the features. In case the above-mentioned plans do not meet your needs, we offer custom pricing for enterprises depending on your usage limits. Please [Contact Us](https://www.scalifiai.com/contact-us) or email us at helpdesk@scalifiai.com. _\*Note: All the figures mentioned in the pricing table are excluding taxes._ ## Can I switch plans at any time I want? Yes, you can switch plans at any time that you want. Billing will be adjusted pro rata based on the current usage cycle. You can either upgrade or downgrade your plans. Upgrading a plan refers to when you move from a lower-value plan to a higher-value plan. Example: You move from Essential to Professional. When you upgrade, your billing cycle also changes to the same date of the request, and credits are allocated pro rata. Please refer to the rollovers and limits mentioned in the pricing table for more information. A downgrade refers to when you move from a higher value plan to a lower value plan. Example: You move from Professional to Essential. For downgrades, you have two options: an Immediate Downgrade starts the new, lower value plan right away. This changes your billing date to the day of the downgrade request, and you get no refund for the current cycle. Alternatively, you can choose an End-of-Cycle Downgrade to finish your current cycle, and the new plan will start on your next billing date, in which case your credits for the current month remain unchanged. ## What happens if my payment is overdue? If your payment is overdue, create/update actions are blocked after 4 days from the payment date of your recurring billing cycle. After 8 days, you are automatically shifted to the Community Plan, and all the features and resources tied to your current plan are deleted and reset. ## How is billing processed? When you submit a Quota Request, you have to make payments through the Razorpay gateway. A payment window of up to 72 hours is available to complete your payment. ## In what currency do I have to make the payment? You can make it in your local currency if your bank supports the same. Currently, all the prices mentioned for all the plans are in Indian Rupees (INR) and are \*exclusive of taxes and any \*\*forex charges. _\*In case of a detailed breakdown for taxes, please view your invoice._ _\*\*In case of a detailed breakdown of forex charges, kindly get in touch with your banking partner._ ## What are the LLM providers that I can currently access on Cognis? Currently, you can access OpenAI models like GPT-5, 4o, etc., and Google’s Gemini models, including 2.5 Pro, 2.5 Flash, etc. We also plan to keep adding the best models to ensure that you can leverage the right kind of intelligence for correlating tasks. We also plan to keep adding the best models to ensure that you can leverage the right kind of intelligence for correlating tasks. ## How many IAM policies can I create? Currently, we do not have any limits on the number of policies that can be created. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. ## How many third-party accounts (TPAs) can I configure? Currently, we do not have any limits on the number of third-party accounts that can be connected. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. ## How many data sources can I connect? Currently, we do not have any limits on the number of data sources that can be connected. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. Experience the next gen platform ## Schedule a demo and witness the future [Request a Demo](https://www.scalifiai.com/contact-us) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Quick Build AI | Advanced Analytics Source: https://www.scalifiai.com/ai-ml-model-building/features/quick-build-advantage-advanced-analytics Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Effortless AI Model Analysiswith MBS Experience the future of AI model development with Scalifi Ai's Model Building Service (MBS). Our platform revolutionises the way you interact with and optimise your AI models, offering real-time insights and rapid prototyping capabilities. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Effortless AI Model Analysis with MBS - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/MBS_Quick_Build_Advantage_a58975e872.svg) ![productivityIcon.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/productivity_Icon_d62744d92a.svg) ##### Accelerated AI Development MBS rapidly accelerates AI development with instant insights and swift model iteration, enhancing efficiency. ![cost_optimization.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/cost_optimization_413b55ff03.svg) ##### Optimization in Operational Costs MBS's Quick Build feature reduces computational needs, slashing operational costs in AI model development. ![workload_reduction.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/workload_reduction_70ee6d5410.svg) ##### Reduction in Workload MBS streamlines AI creation, minimising manual effort and workload, allowing focus on strategic innovation. ![Rapid Development with Quick Build - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/rapid_Dev_Quick_Build_c8a3e35345.svg) ### Rapid Development with Quick Build MBS’s Quick Build feature is pivotal in accelerating AI model development. It enables swift prototyping and testing, reducing the time from concept to deployment. This rapid iteration process ensures your projects stay agile, responsive, and competitive in the fast-paced world of AI technology. ![Optimise Models with Instant Analytics - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/optimize_Model_Analytics_afd33ffa6f.svg) ### Optimise Models with Instant Analytics MBS's real-time analytics facilitate immediate model optimization. This instant feedback ensures your AI models are fine-tuned for accuracy and effectiveness, significantly enhancing the overall quality and reliability of your AI solutions, which is crucial for tackling complex tasks and meeting evolving project demands. ![Enhanced Productivity and Decision Making - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/enhance_Product_Decision_b5a0f62d88.svg) ### Enhanced Productivity and Decision Making Combining MBS’s real-time analytics with the Quick Build feature results in a powerful toolset for AI development. This integration not only streamlines the workflow but also empowers teams with quick, data-informed decision-making capabilities, thereby enhancing productivity and ensuring your projects consistently meet or exceed their goals. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Model Building | Scalifi Ai Docs Source: https://www.scalifiai.com/docs/model-building Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) menu\_open # Model BuildingDocumentation Hub The vault to explore all the features of Scalifi Ai’s Model Building Service. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Scalifi Ai Documentation Hub](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/iam_docs_image_4d46301902.svg) ## Model Building Scalifi Ai's Model Building Service (MBS) will help to streamline the process of constructing machine learning models, saving time and resources. With MBS, you will get the right tools that are always at your fingertips to effortlessly construct advanced AI model designs. ## Model Building Docs [drawModel Designseast](https://www.scalifiai.com/docs/model-building/model-design) [work\_historyModel Jobseast](https://www.scalifiai.com/docs/model-building/model-job) --- --- ## Track Usage | Billing & Usage Guide Source: https://www.scalifiai.com/docs/billing-and-usage/usage Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # Usage Dive into the heart of Scalifi Ai's Billing and Usage Center through our Usage Documentation section. Here, you'll find a comprehensive overview of your engagement across Scalifi Ai's diverse services. This documentation meticulously details your interactions, presenting a clear view of your module and service-wise usage with detailed statistics and insightful graphs. It's designed to provide a thorough understanding of your resource consumption and activity patterns, empowering you to optimize your Scalifi Ai journey. ## insert\_linkUsage Statistics 1. Navigate to **Billing and Usage** from the profile menu. ![Billing And Usage Nav Bar Navigation - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_1d74d4ccc1.svg) 3. To view the detailed quota usage, go to **Usage** tab. 4. To view overall quota usage, expand **Overall Usage** section. ![Overall Usage Graph Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_overall_usage_f04ebf04f9.svg) 6. To view module-wise quota usage, for example IAM usage, go to the **Per Module Usage** section and expand **Identity & Access Management (IAM) Usage**. ![Per Module Usage Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_per_module_usage_2b4c34714d.svg) 8. To filter the quota usage, use **Filter usage by month** and select the month and year. 9. You can view the quota details using the **View Quota** button in the top section. Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [PreviouswestModel Version](https://www.scalifiai.com/docs/model-catalog/model-version) [NextQuotaeast](https://www.scalifiai.com/docs/billing-and-usage/quota) ##### Content [Overview](https://www.scalifiai.com/docs/billing-and-usage/usage#overview) [Usage Statistics](https://www.scalifiai.com/docs/billing-and-usage/usage#usage-statistics) * * * hide\_imageHide Images --- --- ## Low Code No Code Contact Us – Scalifi Ai Source: https://www.scalifiai.com/contact-us Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Open the doors to seamless No-Code AI ##### Contact Us First name Last name Work email Phone number +91 - Afghanistan+93 - Albania+355 - Algeria+213 - American Samoa+1 - Andorra+376 - Angola+244 - Anguilla+1 - Antigua & Barbuda+1 - Argentina+54 - Armenia+374 - Aruba+297 - Ascension Island+247 - Australia+61 - Austria+43 - Azerbaijan+994 - Bahamas+1 - Bahrain+973 - Bangladesh+880 - Barbados+1 - Belarus+375 - Belgium+32 - Belize+501 - Benin+229 - Bermuda+1 - Bhutan+975 - Bolivia+591 - Bosnia & Herzegovina+387 - Botswana+267 - Brazil+55 - British Indian Ocean Territory+246 - British Virgin Islands+1 - Brunei+673 - Bulgaria+359 - Burkina Faso+226 - Burundi+257 - Cambodia+855 - Cameroon+237 - Canada+1 - Cape Verde+238 - Caribbean Netherlands+599 - Cayman Islands+1 - Central African Republic+236 - Chad+235 - Chile+56 - China+86 - Christmas Island+61 - Cocos (Keeling) Islands+61 - Colombia+57 - Comoros+269 - Congo - Brazzaville+242 - Congo - Kinshasa+243 - Cook Islands+682 - Costa Rica+506 - Croatia+385 - Cuba+53 - Curaçao+599 - Cyprus+357 - Czech Republic+420 - Côte d’Ivoire+225 - Denmark+45 - Djibouti+253 - Dominica+1 - Dominican Republic+1 - Ecuador+593 - Egypt+20 - El Salvador+503 - Equatorial Guinea+240 - Eritrea+291 - Estonia+372 - Eswatini+268 - Ethiopia+251 - Falkland Islands+500 - Faroe Islands+298 - Fiji+679 - Finland+358 - France+33 - French Guiana+594 - French Polynesia+689 - Gabon+241 - Gambia+220 - Georgia+995 - Germany+49 - Ghana+233 - Gibraltar+350 - Greece+30 - Greenland+299 - Grenada+1 - Guadeloupe+590 - Guam+1 - Guatemala+502 - Guernsey+44 - Guinea+224 - Guinea-Bissau+245 - Guyana+592 - Haiti+509 - Honduras+504 - Hong Kong+852 - Hungary+36 - Iceland+354 - India+91 - Indonesia+62 - Iran+98 - Iraq+964 - Ireland+353 - Isle of Man+44 - Israel+972 - Italy+39 - Jamaica+1 - Japan+81 - Jersey+44 - Jordan+962 - Kazakhstan+7 - Kenya+254 - Kiribati+686 - Kosovo+383 - Kuwait+965 - Kyrgyzstan+996 - Laos+856 - Latvia+371 - Lebanon+961 - Lesotho+266 - Liberia+231 - Libya+218 - Liechtenstein+423 - Lithuania+370 - Luxembourg+352 - Macau+853 - Madagascar+261 - Malawi+265 - Malaysia+60 - Maldives+960 - Mali+223 - Malta+356 - Marshall Islands+692 - Martinique+596 - Mauritania+222 - Mauritius+230 - Mayotte+262 - Mexico+52 - Micronesia+691 - Moldova+373 - Monaco+377 - Mongolia+976 - Montenegro+382 - Montserrat+1 - Morocco+212 - Mozambique+258 - Myanmar (Burma)+95 - Namibia+264 - Nauru+674 - Nepal+977 - Netherlands+31 - New Caledonia+687 - New Zealand+64 - Nicaragua+505 - Niger+227 - Nigeria+234 - Niue+683 - Norfolk Island+672 - North Korea+850 - North Macedonia+389 - Northern Mariana Islands+1 - Norway+47 - Oman+968 - Pakistan+92 - Palau+680 - Palestine+970 - Panama+507 - Papua New Guinea+675 - Paraguay+595 - Peru+51 - Philippines+63 - Poland+48 - Portugal+351 - Puerto Rico+1 - Qatar+974 - Romania+40 - Russia+7 - Rwanda+250 - Réunion+262 - Samoa+685 - San Marino+378 - Saudi Arabia+966 - Senegal+221 - Serbia+381 - Seychelles+248 - Sierra Leone+232 - Singapore+65 - Sint Maarten+1 - Slovakia+421 - Slovenia+386 - Solomon Islands+677 - Somalia+252 - South Africa+27 - South Korea+82 - South Sudan+211 - Spain+34 - Sri Lanka+94 - St Barthélemy+590 - St Helena+290 - St Kitts & Nevis+1 - St Lucia+1 - St Martin+590 - St Pierre & Miquelon+508 - St Vincent & Grenadines+1 - Sudan+249 - Suriname+597 - Svalbard & Jan Mayen+47 - Sweden+46 - Switzerland+41 - Syria+963 - São Tomé & Príncipe+239 - Taiwan+886 - Tajikistan+992 - Tanzania+255 - Thailand+66 - Timor-Leste+670 - Togo+228 - Tokelau+690 - Tonga+676 - Trinidad & Tobago+1 - Tunisia+216 - Turkey+90 - Turkmenistan+993 - Turks & Caicos Islands+1 - Tuvalu+688 - US Virgin Islands+1 - Uganda+256 - Ukraine+380 - United Arab Emirates+971 - United Kingdom+44 - United States+1 - Uruguay+598 - Uzbekistan+998 - Vanuatu+678 - Vatican City+39 - Venezuela+58 - Vietnam+84 - Wallis & Futuna+681 - Western Sahara+212 - Yemen+967 - Zambia+260 - Zimbabwe+263 - Åland Islands+358 Company Job title Please Select Scalifi Ai Modules that interests you\* Model BuildingModel CatalogData ETLModel TrainingData AnalyticsModel AnalyticsModel ServingCognis Ai Use case description I consent to receiving necessary updates about my submission. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice)& [Terms](https://www.scalifiai.com/legal/website/terms-and-conditions) I consent to receiving emails from Scalifi Ai, including responses and marketing updates. Opt-out anytime. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice)& [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details. Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Programmatic Access with Python Library | Scalifi Ai Source: https://www.scalifiai.com/model-catalog/features/programmatic-access-with-python-library Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Programmatic Access withPython Library Unlock the power of automation and model manipulation with Programmatic Access using Scalifi Ai’s Python Library. Seamlessly integrate your Python scripts with external systems and APIs, enabling streamlined model retrieval, and manipulation. With intuitive syntax and robust functionality, our library empowers developers to automate tasks, and build scalable AI solutions with ease. Experience the efficiency of programmatic access, revolutionizing your workflow and maximizing productivity. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Programatic Access Python Library Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/programatic_access_python_library_8ed024d3cd.svg) ![improved-efficiency-model-catalog-scalifi-ai.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/improved_efficiency_model_catalog_scalifi_ai_11f515b840.svg) ##### Improved Efficiency Automate tasks to save significant time and effort compared to manual management through Scalifi Ai’s Model Catalog Service user interface. This allows data scientists to focus on more strategic work. ![enhanced-developer-experience-model-catalog-scalifi-ai.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/enhanced_developer_experience_model_catalog_scalifi_ai_782a3ad29c.svg) ##### Enhanced Developer Experience The library provides a familiar Python interface for interacting with the Model Catalog, improving developer productivity. ![boost-productivity-model-catalog-service-scalifi-ai.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/boost_productivity_model_catalog_service_scalifi_ai_2b78f9717e.svg) ##### Boost productivity With intuitive syntax and powerful functionality, unleash the full potential of automation to tackle complex processes swiftly and effectively. ![Register Fetch Manage Models Programmatically Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/register_fetch_manage_models_programmatically_scalifi_ai_92f9162cb6.svg) ### Register, Fetch, and Manage Models Programmatically Provide developers with seamless access to the Model Catalog's functionalities through code and systematically utilize comprehensive, reliable, and relevant information derived from various models to make sound and well-founded decisions. By understanding how different models were built and what data they were trained on, you can make more informed decisions about which model to use for a particular task. ![Customized Model Structure Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/customized_model_structure_model_catalog_service_4cbd27136d.svg) ### Integration with Workflows Integrate model management tasks with your existing Python workflows, streamlining the entire machine learning pipeline. By integrating programmatic access, developers can streamline model deployment, enhance collaboration, and ensure scalability across projects, all while maintaining flexibility and control over their model assets. This ensures a cohesive and efficient integration of AI capabilities into your organization's operations. ![Task Automation Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/model_catalog_automation_scalifi_ai_8236391da8.svg) ### Automation Automate repetitive tasks associated with model management, such as: registering new models with their metadata and artifacts, creating and managing different versions of models, triggering deployments of models to various environments, downloading specific model versions for further analysis. By automating these essential processes, data scientists can streamline their workflow, enhancing productivity and allowing more time for valuable innovation. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Secure Deployment: Model Governance and Access Control Source: https://www.scalifiai.com/model-catalog/features/model-governance-and-access-control Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Model Governance and Access Control forSecure Deployment Systematic management and regulation of machine learning models to ensure their reliability, accountability, and security throughout their lifecycle. This encompasses a set of processes, and policies aimed at governing the development, deployment and monitoring of models within an organization's infrastructure. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Model Governance Access Control - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/model_governance_access_control_scalifi_ai_5009e62eb1.svg) ![reduced-risk-of-model-misuse-model-catalog.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/reduced_risk_of_model_misuse_model_catalog_339d569aa8.svg) ##### Reduced Risk of Model Misuse Prevent unauthorized access and deployment, mitigating the risk of biased, inaccurate, or malicious models being used in production. ![improved-model-fairness-and-transparency-scalifi-ai.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/improved_model_fairness_and_transparency_scalifi_ai_01aaa169a3.svg) ##### Improved Model Fairness and Transparency Governance policies promote fairness by encouraging bias detection and mitigation throughout the model lifecycle. Access control ensures only authorized personnel can modify models. ![cost_optimization.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/cost_optimization_413b55ff03.svg) ##### Cost-Effectiveness Efficient access control minimizes the risk of errors or security breaches, potentially reducing operational costs. ![Granular User Permissions Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/granular_user_permissions_model_catalog_service_333bc63574.svg) ### Granular User Permissions Define various granular permissions for each user. This allows for a flexible and secure approach based on user responsibilities. Control who can register new models or create versions of the existing models in the system. Permissions might range from "view only" to "create and edit" models, ensuring only authorized users can introduce new models. With customizable access settings, organizations can confidently manage their resources and achieve a productive research environment. ![Approval Workflows Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/approval_workflows_scalifi_ai_model_catalog_service_2af4caf8cd.svg) ### Approval Workflows Integrate approval workflows into the deployment process. Specific roles might need to approve deployments before they go live, adding an extra layer of control. By integrating approval workflows into the deployment process, organizations can enforce governance, mitigate risks, and maintain control over the models deployed within their infrastructure, ultimately promoting trust, transparency and confidence in the AI ecosystem. ![Deployment Environment Permissions Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/deployment_environment_permissions_scalifi_ai_model_catalog_service_543626cfd2.svg) ### Deployment Environment Permissions Define who can deploy models to different environments (e.g., development, testing, production). This restricts unauthorized deployments, ensures models go through proper testing stages and allows an organization to have a controlled model deployment ecosystem. These permissions ensure secure and efficient deployment processes, allowing teams to streamline workflows, mitigate risks, and maintain compliance standards. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## High-Performance Management: Scalable and Secure Infrastructure Source: https://www.scalifiai.com/model-catalog/features/scalable-and-secure-infrastructure Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Scalable and Secure Infrastructure forHigh-Performance Management Manage vast amounts of data and ensure its security. A scalable and secure infrastructure for high-performance management provides the backbone for handling large volumes of data and demanding workloads while ensuring robust security measures. Advanced security protocols, access controls, and monitoring systems, safeguard sensitive data and mitigate cyber security risks. This infrastructure empowers organizations to manage complex operations seamlessly, supporting growth, and compliance with regulatory requirements. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Scalable And Secure Infrastructure Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/scalable_and_secure_infrastructure_scalifi_ai_model_catalog_6f123db1af.svg) ![scalable-data-architecture-model-catalog.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/scalable_data_architecture_model_catalog_fce7d5eb44.svg) ##### Scalable Data Architecture A scalable infrastructure starts with a robust data architecture designed to handle large volumes of data efficiently. ![reduced-risk-of-errors-model-catalog.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/reduced_risk_of_errors_model_catalog_09010093ee.svg) ##### Reduced Risk of Errors A secure infrastructure minimizes the risk of unauthorized access or manipulation of models. This ensures data integrity and the reliability of your model catalog, leading to more trustworthy results. ![cost_optimization.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/cost_optimization_413b55ff03.svg) ##### Cost-Effectiveness Our scalable infrastructure adapts to your growing needs, avoiding the need for frequent upgrades or over-provisioning resources. This will save significant cost in the long run. ![Efficient Model Storage And Retrieval Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/efficient_model_storage_and_retrieval_model_catalog_service_bfc82caea5.svg) ### Efficient Model Storage and Retrieval High-performance management allows fast access and retrieval of a number of models created by the organization. This facilitates faster iteration and reduces wait times for data scientists to streamline workflows. By implementing advanced indexing and compression techniques, it maximizes resource utilization while minimizing latency. This feature promotes efficient utilization of computational resources. ![Focus And Business Continuity Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/focus_and_business_continuity_model_catalog_8aec08a1b1.svg) ### Focus and Business Continuity Ensure business continuity and mitigate the risk of data loss with Scalifi Ai’s reliable and secure infrastructure mitigating the security risks. This facilitates the organization to focus on building innovative AI solutions by adapting to market changes and mitigates potential disruptions without infrastructure concerns and its failover. This feature promotes resilience by enabling efficient resource allocation and strategic planning, fostering a robust foundation for long-term success amidst evolving business landscapes. ![Federated Learning Support Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/federated_learning_support_model_catalog_3cef11cb14.svg) ### Federated Learning Support Facilitate secure collaboration on models across different organizations or departments while protecting sensitive data privacy, reduced communication costs, and the ability to leverage distributed datasets. Data scientists can effortlessly orchestrate model building and training across disparate devices and platforms while preserving data privacy and security. By facilitating the integration of federated learning techniques, the Model Catalog fosters efficient model iteration and refinement, driving advancements in decentralized AI research and deployment. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Model Metadata | Model Catalog Guide Source: https://www.scalifiai.com/docs/model-catalog/metadata Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # Metadata Metadata encapsulates crucial information about a machine learning model, including hyperparameters, training data details, and evaluation metrics. This concise summary enhances model understanding, reproducibility, and collaborative efficiency. Allowed operations that users can perform on the platform are: 1. Add Metadata 2. Preview Metadata 3. Update Metadata 4. Delete Metadata But before moving on to the operations of metadata lets see what are the different types of metadata. **Note:** Metadata of models and model versions can be accessed from their respective metadata browser. ## insert\_linkTypes of metadata In Scalifi Ai's Model Catalog Service (MCS), metadata enriches model management by attaching additional, relevant information to models or model variations. MCS supports two primary types of metadata: **Simple Type** and **Storage Type**. Each type caters to specific needs and accommodates different use cases. ### insert\_linkSimple Type Metadata 1. **Simple Type Metadata** is designed to store basic information in key-value pairs where the value is a primitive data type. This metadata type is optimal for labelling models with straightforward data that aids in categorization, search, and brief descriptions. 2. Characteristics: - Data Types Supported: Strings, integers and floats. - Use Cases: - Adding key performance metrics like accuracy, precision, recall, F1 score etc. - Adding version/variation descriptions. 4. Example: - A model could have simple metadata with the key version and a string value 1.0, or accuracy with a float value 0.95. ### insert\_linkStorage Type Metadata 1. **Storage Type Metadata** allows for the attachment of files and complex data structures, providing a robust means to include supplementary materials essential to the model. This metadata type supports various file formats, enhancing the model's utility with rich, accessible content. 2. Characteristics: - Data Types Supported: All file types including images, PDFs, Markdown, HTML, audio, and video files. - Preview Functionality: Scalifi Ai's MCS offers preview capabilities for most common file types, allowing users to view content such as HTML graphs directly on the platform or through the command line interface (CLI). This feature is particularly useful for visualizing model outputs, reviewing documentation, or examining model configurations without downloading files. - Use Cases: - Attaching training data, model weights, or configuration files that are crucial for the model’s operation. - Storing and previewing graphical outputs as HTML, instructional videos, audio commentary, or annotated images that describe or demonstrate the model's use. 4. Example: - A machine learning model used for image processing might include metadata with an .html file showing visualizations of the model’s performance on test data. Alternatively, a speech recognition model could have an .mp3 file attached as metadata, demonstrating its output capabilities. ## insert\_linkAdd Metadata 1. To add metadata, click on Upload metadata under Metadata. ![Upload Metadata Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/upload_model_metadata_scalifi_ai_ca63a89ce3.svg) 3. Scalifi Ai supports String, Integer, Floating point and file data type. 4. To add String, Integer or Floating point type metadata: - Select the required data type from the dropdown menu. - Add metadata name and value. - Click on **Add metadata** button. ![Add String Metadata Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/add_model_metadata_string_type_1a5adc1ba0.svg) 7. Tp add file type metadata: - Select file type from the dropdown menu. - Browse / drag-and-drop the required files. - Click on **Upload file** button. ![Upload Metadata File Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/upload_file_model_metadata_scalifi_ai_2b333f4751.svg) ## insert\_linkPreview metadata 1. To instantly preview the metadata, enable Instant file preview on click from the toolbar and click on the metadata to preview its properties. ![Instant Preview Of Metadata Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/instant_metadata_preview_scalifi_ai_19ca1df268.svg) 3. Else, right click on the metadata. 4. Click on **Preview** action. ![Preview Metadata Actions Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/metadata_preview_action_scalifi_ai_7fff276aab.svg) 6. To view/hide the content, click on **View/Hide content** button. 7. To download the metadata, click on **Download file** in properties section or right click on metadata and then click on **Download Metadata** option. ![Preview Metadata Properties Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/metadata_preview_properties_scalifi_ai_e938973542.svg) ## insert\_linkUpdate metadata 1. Right click on the metadata. 2. Click on **Update metadata** action. ![Update Model Metadata Action Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/update_model_metadata_action_scalifi_ai_6ae6892132.svg) _Update Model Metadata Action Model Catalog - Scalifi Ai_ 4. Change data type as per the need and enter the updated value. ![Update Model Metadata Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/update_model_metdata_modal_scalifi_ai_46bb481841.svg) _Update Model Metadata Model Catalog - Scalifi Ai_ 6. Click on **Update**. ## insert\_linkDelete metadata 1. Right click on the metadata. 2. Click on **Delete metadata** action. ![Delete Metadata Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/delete_metadata_scalifi_ai_71196ac0b0.svg) Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [PreviouswestModel Management](https://www.scalifiai.com/docs/model-catalog/model-management) [NextModel Versioneast](https://www.scalifiai.com/docs/model-catalog/model-version) ##### Content [Overview](https://www.scalifiai.com/docs/model-catalog/metadata#overview) [Types of metadata](https://www.scalifiai.com/docs/model-catalog/metadata#types-of-metadata) [\- Simple Type Metadata](https://www.scalifiai.com/docs/model-catalog/metadata#simple-type-metadata) [\- Storage Type Metadata](https://www.scalifiai.com/docs/model-catalog/metadata#storage-type-metadata) [Add Metadata](https://www.scalifiai.com/docs/model-catalog/metadata#add-metadata) [Preview metadata](https://www.scalifiai.com/docs/model-catalog/metadata#preview-metadata) [Update metadata](https://www.scalifiai.com/docs/model-catalog/metadata#update-metadata) [Delete metadata](https://www.scalifiai.com/docs/model-catalog/metadata#delete-metadata) * * * hide\_imageHide Images --- --- ## Identity & Access Management | Scalifi Ai Docs Source: https://www.scalifiai.com/docs/iam Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) menu\_open # Identity and Access ManagementDocumentation Hub The vault to explore all the features of Scalifi Ai’s Identity and Access Management. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Scalifi Ai Documentation Hub](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/iam_docs_image_4d46301902.svg) ## Overview Welcome 👋 to the Scalifi Ai’s Identity and Access Management (IAM) Documentation Hub. Our IAM Documentation Hub serves as a comprehensive guide to understanding and implementing robust Identity and Access Management solutions like user management, manage policies, linking a third party account, manage profile and a lot more. ## IAM Docs [manage\_accountsUser Managementeast](https://www.scalifiai.com/docs/iam/user-management) [policyPolicy Managementeast](https://www.scalifiai.com/docs/iam/policy-management) [people\_altThird Party Accountseast](https://www.scalifiai.com/docs/iam/third-party-accounts) --- --- ## Model Management | Model Catalog Guide Source: https://www.scalifiai.com/docs/model-catalog/model-management Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # Model Management A model catalog is essentially a central library for all the machine learning models used within an organization. It acts like an organized inventory, keeping track of various aspects of these models through metadata. This metadata can include details like versions, variations and its signature configuration, tags, description etc. To view/create a model, go to [Model Catalog](https://platform.scalifiai.com/model-catalog) from the dashboard. Allowed operations that users can perform on the platform are: 1. Create Model 2. Add Model Tags 3. Remove Model Tags 4. Model Metadata 5. Update Model 6. Delete Model ## insert\_linkCreate Model 1. To create a model, click on the Create Model button. ![How To Create A Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/create_model_catalog_scalifi_ai_2d83437c49.svg) 3. Enter the Model name, description, language, task you are creating this model for and click on Create Model. ![Enter Model Catalog Details - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/create_model_catalog_details_scalifi_ai_74706e3cad.svg) 5. After successfully creating the model, you will be redirected to the per model page where all the model details are displayed. ![Per Model Catalog Page - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/per_model_catalog_scalifi_ai_f06f08826e.svg) ## insert\_linkAdd Model Tags 1. Adding tags to models help in searching, filtering or categorizing different models based on various criterias. These tags are added in a key value pair. 2. To add tags to a model, go to the [Model Catalog List](https://platform.scalifiai.com/model-catalog) and click on the model which you have created. ![Model Catalog List - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/model_catalog_list_scalifi_ai_44f97835b1.svg) 4. Next, you will see the per model page where all the details like model name, description, tags and metadata information, etc is displayed. 5. Click on Manage Tags, and add the key:value data of tags. 6. To add multiple tags, use the Add tag button. If any tag is not needed you can delete it by clicking on the delete button on the right side of the tag. ![Add Tag Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/add_model_tags_scalifi_ai_620ad3878f.svg) 8. Finally, click on Submit to add the tags in a model. ## insert\_linkRemove Model Tags 1. Once the tags are removed from the model, you will no longer be able to categorize, search or filter that model in relation to the tag key or values. Before deleting the tags make sure you no longer need those tags. 2. To remove model tags, simply click on the cross icon (X) of a tag. ![Remove Tag Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/remove_model_tag_scalifi_ai_eac338d1cf.svg) 4. To remove all model tags at once, click on the Remove all tags button. 5. Confirm the removal process and click on Confirm ![Remove All Tags Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/remove_all_model_tags_scalifi_ai_1099ef2629.svg) ## insert\_linkModel Metadata Refer the [metadata guide](https://www.scalifiai.com/docs/model-catalog/metadata) for managing the model metadata. ## insert\_linkUpdate Model 1. On the per model catalog page, click on **Edit Model**. ![Per Model Catalog Page - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/per_model_catalog_scalifi_ai_f06f08826e.svg) 3. Update the necessary details and then click on **Update Model**. ![Update Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/update_model_catalog_scalifi_ai_f947987b2e.svg) ## insert\_linkDelete Model 1. There are 2 ways of deleting a model. 1st way is from the Model Catalog List table. Select the model from the list and click on **Delete** option under **Actions** dropdown. ![Delete Model From List Model Catalog Page - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/delete_model_list_model_catalog_scalifi_ai_43c8020264.svg) 3. Confirm the removal process and click on Confirm ![Confirm Delete Model From List Model Catalog Page - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/confirm_delete_model_list_model_catalog_scalifi_ai_888d5c65d1.svg) 5. 2nd way is from per model page. Select the **Delete Model** option under the **Actions** dropdown. ![Delete Model From Per Model Page Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/delete_model_per_model_page_scalifi_ai_faf92b2813.svg) 7. Confirm the removal process and click on Confirm ![Confirm Delete Model From Per Model Page Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/confirm_delete_model_per_model_page_scalifi_ai_bca616266b.svg) Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [PreviouswestModel Jobs](https://www.scalifiai.com/docs/model-building/model-job) [NextMetadataeast](https://www.scalifiai.com/docs/model-catalog/metadata) ##### Content [Overview](https://www.scalifiai.com/docs/model-catalog/model-management#overview) [Create Model](https://www.scalifiai.com/docs/model-catalog/model-management#create-model) [Add Model Tags](https://www.scalifiai.com/docs/model-catalog/model-management#add-model-tags) [Remove Model Tags](https://www.scalifiai.com/docs/model-catalog/model-management#remove-model-tags) [Model Metadata](https://www.scalifiai.com/docs/model-catalog/model-management#model-metadata) [Update Model](https://www.scalifiai.com/docs/model-catalog/model-management#update-model) [Delete Model](https://www.scalifiai.com/docs/model-catalog/model-management#delete-model) * * * hide\_imageHide Images --- --- ## Human-in-the-Loop (HITL) Systems: The Future of AI Source: https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Human-in-the-Loop (HITL) Systems: The Future of AI 16 January 2026\|8 min read For a long time, AI was framed like a choice between two extremes: humans do everything, or machines do everything. But the real shift happening in practice is collaborative intelligence. The system does the heavy lifting, and a person stays intentionally involved at the moments that matter. That's what human in the loop design is aiming for. Not slower automation. Smarter automation. In this guide, you'll learn what Human-in-the-Loop (HITL) actually means, how it differs from other oversight models, and why it becomes even more important once you start using agentic AI (systems that plan and execute multi-step actions). You'll also see what this looks like when it's enforced in a real platform, including how Cognis AI supports centralized oversight, controlled knowledge grounding, and permissioned approvals so you can keep automation moving without losing control. **Sidenote:** When AI sounds confident, it's easy to assume it's correct. HITL exists to keep that confidence from turning into irreversible mistakes. ## insert\_linkWhat is Human-in-the-Loop (HITL)? Human-in-the-Loop (HITL) is a strategic design approach that intentionally embeds human judgment, expertise, and moral discernment across the machine learning lifecycle. That includes training, evaluation, validation, and real-time deployment. **The key idea is simple: the automated system cannot proceed to its final action until a human has explicitly reviewed, approved, or modified the proposed output.** So the AI isn't treated like an infallible component. It's treated like a capable partner. ![A diagram showcasing how humans in the loop systems work with human and ai.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FWhat_is_HITL_Section_2e6efbe15d.png&w=3840&q=75) _Human-in-the-Loop_ ### Where does the human loop actually happen? HITL gets operationalized at four critical junctures: - **Data annotation and curation** to create ground truth. - **Model training and tuning** using feedback methods like Reinforcement Learning from Human Feedback (RLHF), where people rank or score outputs to teach nuanced qualities (like tone or helpfulness). - The message is automatically resent to the new model and Cognis creates a demarcation of the moment in the chat where you branched with numbered options for accessing each respective branch - **Inference oversight**, where humans validate model outputs before they're acted on in production. A safety net against hallucinations. - **Edge-case and exception handling** where out-of-distribution scenarios are escalated for human adjudication. ### DID YOU KNOW? HITL isn't just a button you click at the end. It's a governance and operating model for human-in-the-loop systems. And it shows up everywhere, from human in the loop machine learning practices (labeling, feedback, evaluation) to how you design approvals and accountability for human in the loop artificial intelligence in production. This is also why platforms like Cognis AI focus on making the loop enforceable (and reviewable), not just optional. ## insert\_linkWhy Human-in-the-Loop Matters in AI & ML If you're building or deploying AI in high-stakes, ambiguous, or ethically sensitive environments, mostly right isn't good enough. HITL exists because humans bring contextual understanding, moral reasoning, and domain judgment that purely statistical systems don't have. ### 1) Better accuracy and reliability Humans catch mislabels, anomalies, and edge cases that quietly degrade model performance. That matters in training. And it matters in production. In document processing, combining AI with human verification can reach accuracy rates up to 99.9% [\[1\]](https://parseur.com/blog/human-in-the-loop-ai). ### 2) Bias mitigation and ethical alignment Training data can encode historical bias. Human oversight is the lever for spotting and mitigating that bias before it becomes a harmful output ### 3) Transparency, explainability, and auditability HITL reduces the black box risk by making decisions reviewable and attributable. That supports audit trails. It aligns with oversight expectations in frameworks like the EU AI Act (Article 14) and the NIST AI Risk Management Framework [\[2\]](https://www.ibm.com/think/topics/human-in-the-loop). ### 4) Trust and adoption People adopt AI faster when they know a person is still responsible. That's the emotional logic behind humans in the loop ai. You keep the speed of automation. But you also keep accountability. Practically, it's easier to do well when you run your work through structured human-in-the-loop workflows that capture approvals, overrides, and corrections as real operational data. That’s where systems like [Cognis AI](https://www.scalifiai.com/cognis-ai) tend to shine: if you centralize oversight, capture human decisions, and control what knowledge gets used, you make trust scalable, not fragile. ## insert\_linkHuman-in-the-Loop vs. Fully Automated Systems Fully automated systems are fast and scalable.But speed is not the same thing as safety.In ambiguous situations, small mistakes can become big outcomes. So the real question isn't automation or no automation? It's, **where do you put human authority in the pipeline?** ### The control hierarchy matters These models are primarily distinguished by two things: - **Control hierarchy:** who has final authority. - **Proximity to execution:** how close the human is to the final action. Here's a map: 1. **Human-in-the-Loop (HITL):** A human must review, approve, or modify before final action. 2. **Human-on-the-Loop (HOTL):** The AI runs mostly independently; a human monitors and intervenes by exception. 3. **Human-in-Command (HIC):** The human holds total authority; AI is decision support. 4. **AI-in-the-Loop (AITL):** AI augments a mostly human process, accelerating routine work. ### Why this difference matters in practice? If you want the machine to move quickly but still stay accountable, you need clear handoffs. That's exactly what human-in-the-loop workflows provide. They let the **AI do the repetitive and computational parts** and they **reserve human judgment for the points where errors, ethics, or ambiguity matter most.** This is also why teams often invest in a centralized workspace and permissioned approvals (features you see emphasized in platforms like Cognis AI): the loop isn't just about review, it's about who can approve what, and when. ### Where do humans in the loop show up most? Typically, at the moments closest to irreversible execution: approving a high-impact decision, validating a risky output, or adjudicating an exception. That's also the core mindset behind human in the loop machine learning when you zoom out: the human doesn't just label data once, they steer learning and action over time. ## insert\_linkChallenges of HITL Systems HITL is powerful. But it's not free from caveats. If you design it casually, you can end up with a slow, expensive, inconsistent process. So let's name the trade-offs clearly. ### 1) Scalability and cost Human labor is expensive. And it doesn't scale linearly with data volume. If you require human review for everything, humans become the bottleneck. Humans introduce delay. That can be a problem for time-sensitive tasks like autonomous driving or high-frequency trading. ### 3) Human error and inconsistency Humans get tired. They get distracted. And they bring subjective variance. That can create noisy labels and inconsistent standards unless you add quality controls. ### 4) Automation bias There's a real risk that reviewers start rubber-stamping AI outputs. That defeats the purpose of oversight. ### 5) Privacy risks If sensitive information is shown during review, you increase the security and compliance burden. This is where governance becomes practical, not theoretical. You need controlled access. You need careful data handling. And you need a way to prove who saw what and who approved what, especially when you're deploying human in the loop artificial intelligence in regulated contexts. **The silver lining:** all these challenges are manageable. But only if you design the loop intentionally (with clear roles, triggers, and guardrails), not as an afterthought. ## insert\_linkBest Practices to Implement HITL in AI/ML Projects 01. **Start with ground truth. Build data annotation and curation into the plan.** Use domain experts to label and structure raw data so the model learns from high-quality examples, not noisy assumptions. 02. **Use human feedback during training and tuning.** Apply Reinforcement Learning from Human Feedback (RLHF), where humans rank or score outputs to teach subtle concepts (like tone, empathy, or helpfulness) that are hard to encode algorithmically. 03. **Design inference oversight as a real workflow.** In production, humans need to act as active validators who review AI-generated predictions before they are acted upon as a direct safety net against hallucinations. 04. **Plan for edge cases.** When data is out-of-distribution, escalate it for human adjudication. Humans apply common sense and adaptability, where purely statistical models often fail. 05. Define strategic trigger points. Use confidence-based routing (for example, flagging outputs below a threshold such as 80%) and **risk-based gates** for actions with significant financial or legal impact. 06. **Make the handoff easy to execute.** Provide the reviewer with the model's reasoning and the specific evidence that triggered escalation, so humans can decide quickly without unnecessary cognitive load. 07. Capture every correction. Every human override, approval, or modification should become a new labeled signal for continuous evaluation and retraining. This creates a learning flywheel and helps address concept drift over time. 08. **Build quality controls for human reviewers.** Reduce inconsistency with approaches like consensus scoring and inter-rater reliability checks, especially when labeling or validating at scale. 09. **Reduce fatigue on purpose. HITL fails when reviewers are exhausted.** Systems that use a familiar interface (for example, a Rich UI that mirrors workplace apps, as Cognis AI does) can reduce cognitive load and improve review quality. 10. **Enforce governance with permissions.** Use granular Identity Access Management (IAM) so only qualified humans can approve sensitive actions, and keep an auditable chain of responsibility. ## insert\_linkHow does Cognis Reinforce Humans in the Loop If HITL is the philosophy, enforcement is the hard part. Cognis AI relates to this problem as a multi-LLM agentic automation platform that structures oversight into the way work actually runs. Not as an add-on. As part of the operating system. ### Centralized workflow oversight Cognis AI combines generative AI-led automation with a centralized workspace. That matters because fragmented tools create fragmented oversight. A central command-and-control view makes it easier to see what the AI is doing and where human approvals should happen. ### Custom memory for data control and grounding Cognis leverages custom memory as a central knowledge base. Humans control what data is included or excluded from chats. That helps ground outputs in human-verified information and reduces hallucinations. It also uses its own processing and custom memory to bypass vulnerable push-and-pull data transfer mechanisms found in standard Model Context Protocols (MCP), strengthening enterprise-grade data governance. Click here to watch how Custom Memory works ### Rich UI to reduce fatigue Human review is only as good as human attention. Cognis uses a Rich UI that mirrors commonly used workplace apps inside the chat window. That familiarity is designed to reduce fatigue and increase productivity during review and approval. Click here to see how Rich UI Works ### Granular IAM so the right human approves the right thing Not every decision should be reviewed by someone. It should be reviewed by a qualified person. Cognis offers granular Identity Access Management (IAM) that mirrors organizational hierarchies, so sensitive decisions land with the right approver. In practice, that's what makes collaborative intelligence scalable: humans stay in control, but they don't have to micromanage every token. ## insert\_linkFuture of Human-in-the-Loop AI As AI becomes more agentic, oversight stops being a nice-to-have. It becomes a design requirement. Agentic AI refers to autonomous, goal-directed systems capable of creating context-specific plans and executing multiple steps across various applications. And that autonomy raises the risk of drift and overreach. ### Why the loop matters more for agents Agents can call tools. They can query APIs. They can modify systems. So a single bad assumption can cascade into a series of bad actions. That's why modern agentic frameworks rely on: - **Strategic trigger points** where an **agent must pause and ask permission** before high-impact actions (like deleting data or approving financial transactions). - **Interrupt protocols** where **execution pauses mid-workflow until a human reviews context** and approves or redirects. - **Human-as-a-tool** patterns where the **agent explicitly queries a human for context**, fact-checking, or clarification. If you've seen how langgraph human in the loop patterns work, this will feel familiar. Frameworks like LangGraph can pause execution via interrupt() so a human can approve or redirect the plan. When you combine that with controlled knowledge grounding and permissioned roles (the kind of guardrails Cognis AI emphasizes), you get a future where agents can move fast without operating unchecked. Collaborative intelligence becomes the default. Not because it's trendy. Because it's the safest way to scale real-world autonomy. #### Frequently Asked Questions ###### What is the difference between HITL, HOTL, HIC, and AITL? HITL requires human approval before final action; HOTL is human monitoring by exception; HIC keeps the human in total authority while AI supports decisions; AITL uses AI to augment mostly human workflows. ###### Where do you place humans in the lifecycle? At four critical junctures: data annotation/curation, training and tuning via human feedback (including RLHF), inference oversight in production, and edge-case/exception handling for out-of-distribution scenarios. ###### How do you decide which actions require approval? Use strategic trigger points: confidence thresholds and risk gates for actions with significant financial, legal, or operational impact. ###### How do you reduce latency without losing safety? Don't route everything to humans. Route high-risk, low-confidence, or out-of-distribution cases to humans, and let low-risk automation run with monitoring where appropriate. ###### How do you avoid automation bias? Design the review so humans get the context and evidence they need, reduce fatigue, and keep clear accountability (so approvals aren't just rubber stamps). ###### How does LangGraph support human approvals in agentic flows? In langgraph human in the loop setups, interrupt protocols (like interrupt()) can pause execution mid-run so a human can approve or redirect the agent's next step. ###### How do custom memory and IAM help with privacy and governance? Custom memory helps control what information is included or excluded when grounding outputs; granular IAM ensures only qualified humans can access sensitive data and approve sensitive actions, and that decisions are reviewable and attributable (useful for audit trails and compliance). ###### Is HITL about blocking automation? No. It's about choosing the right control points, so automation scales without losing human judgment and responsibility. #### External References 1. What is human-in-the-loop? IBM. [Read the article here.](https://www.ibm.com/think/topics/human-in-the-loop) 2. Human-in-the-Loop AI (HITL) - Complete Guide to Benefits, Best Practices & Trends for 2026. Parseur. [Read the article here.](https://parseur.com/blog/human-in-the-loop-ai) 3. Marry Jeffy (2025). Human-in-the-Loop vs. Full Autonomy: Striking the Balance in AI-Driven RPA. [Read the article here.](https://www.researchgate.net/publication/391653519_Human-in-the-Loop_vs_Full_Autonomy_Striking_the_Balance_in_AI-Driven_RPA) 4. Kaufmann et al. (2025). A Survey of Reinforcement Learning from Human Feedback. [Read the article here.](https://arxiv.org/pdf/2312.14925) 5. AI in the Loop vs Human in the Loop: A Technical Analysis of Hybrid Intelligence Systems. IBM Community. [Read the article here.](https://community.ibm.com/community/user/blogs/anuj-bahuguna/2025/05/25/ai-in-the-loop-vs-human-in-the-loop) Related: Generative AI AI Ethics AI Innovation Machine Learning ##### In this blog [What is Human-in-the-Loop (HITL)?](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai#what-is-human-in-the-loop-(hitl)) [Why Human-in-the-Loop Matters in AI & ML](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai#why-human-in-the-loop-matters-in-ai-and-ml) [Human-in-the-Loop vs. Fully Automated Systems](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai#human-in-the-loop-vs.-fully-automated-systems) [Challenges of HITL Systems](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai#challenges-of-hitl-systems) [Best Practices to Implement HITL in AI/ML Projects](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai#best-practices-to-implement-hitl-in-aiml-projects) [How does Cognis Reinforce Humans in the Loop](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai#how-does-cognis-reinforce-humans-in-the-loop) [Future of Human-in-the-Loop AI](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai#future-of-human-in-the-loop-ai) [FAQs](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs The Key to Removing AI Slop - Memory Managed AI Agents [Read Moreeast](https://www.scalifiai.com/blog/agentic-ai-ai-slop) Cognis Ai (Beta): The Ultimate Agentic AI for all Your Professional Needs [Read moreeast](https://www.scalifiai.com/blog/cognisailaunch) Walkthrough: How to Use Google Sheets on Cognis [Read Moreeast](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis) ###### Explore other usecases Credit Card Fraud Detection [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) Customer Churn Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) AI in Cyber Security to Redefine the Security Posture [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Model Version and lineage tracking | Scalifi Ai Source: https://www.scalifiai.com/model-catalog/features/model-version-and-lineage-tracking Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Model Version andLineage Tracking Empower your organization with Scalifi Ai’s comprehensive model lineage tracking for rigorous experimentation and optimization cycles. Our solution enables clear documentation of model iterations, supporting enhanced model governance, auditability, and reproducibility. With robust version control, ensure regulatory compliance and reliability in your AI applications. Stay confident in your model's evolution with transparent lineage tracking from development to deployment. Enhance your machine learning workflows with precision and accountability at every step. Experience seamless collaboration and confident decision-making with our advanced lineage management tools. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Model Version And Lineage Tracking Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/model_version_and_lineage_tracking_5cdfa84052.svg) ![reusability-scalifi-ai-model-catalog-service.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/reusability_scalifi_ai_model_catalog_service_0f42352644.svg) ##### Reproducibility When recreating a model, you can rest assured that the same data, code, and environment as before are being utilized. ![debugging-scalifi-ai-model-catalog-service.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/debugging_scalifi_ai_model_catalog_service_b6717bd86f.svg) ##### Debugging Track down the root cause by looking at the lineage of the model when the performance of the model is poor. ![governance-and-auditing-model-catalog-service.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/governance_and_auditing_model_catalog_service_c4a1535030.svg) ##### Governance and auditing Track how models are changing over time and ensure that they are being developed and used in a responsible manner. ![Enhanced Productivity and Decision Making - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/enhance_Product_Decision_b5a0f62d88.svg) ### Informed Decision Making Systematically utilize comprehensive, reliable, and relevant information derived from various models to make sound and well-founded decisions. By understanding how different models were built and what data they were trained on, you can make more informed decisions about which model to use for a particular task. This enables analyzing information effectively, identifying patterns, and generating actionable insights, ultimately enhancing the accuracy and effectiveness of decision-making across various domains. ![Faster Experimentation And Interaction Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/faster_experimentation_and_interation_model_catalog_service_3c6c1b9561.svg) ### Faster Experimentation and Iteration In the dynamic landscape of data science and machine learning, the ability to rapidly experiment and iterate upon models is paramount for innovation and progress. With Scalifi Ai , you can easily track changes and revert to previous versions and experiment with different model configurations more quickly. This will guarantee the acceleration of the development process and help you find the optimal model. ![Enhanced Collaboration Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/enhanced_collaboration_model_catalog_scalifi_ai_3f0f09b70f.svg) ### Enhanced Collaboration Collaboration is fundamental to accelerating innovation in machine learning. By providing shared access to model variants and facilitating real-time collaboration features, such as commenting, version tracking, and collaborative editing, teams can effectively collaborate on model development projects. Model Lineage tracking allows team members to understand the history and rationale behind different models. This fosters better communication and collaboration within the data science team. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## An Introduction to NLP: Unlock It's Power and Potential Source: https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Understanding Natural Language Processing (NLP) Essentials 20 April 2024\|5 min read ## insert\_linkIntroduction to Natural Language Processing This article aims to cover what natural language processing is in layman's terms with visual and code examples. We will have a different approach in understanding NLP unlike other articles out there for NLP basics. Let’s start with the human approach to understanding things and gradually get on with mathematical concepts in depth in a series of articles rather than directly jumping into terminologies or use cases of NLP in the Machine Learning and statistical computing domains. ## insert\_linkWhat is Natural Language Processing(NLP)? Few definitions of NLP: > Natural language processing (NLP) refers to the branch of computer science — and more specifically, the branch of [artificial intelligence or AI](https://www.ibm.com/topics/artificial-intelligence) — concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. (Source: [https://www.ibm.com/topics/natural-language-processing)](https://www.ibm.com/topics/natural-language-processing) > Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language.Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language. (Source: [https://aws.amazon.com/what-is/nlp/)](https://aws.amazon.com/what-is/nlp/) In the most basic terminology, Humans use multiple languages to communicate in terms of text and speech. However, still, machines are not so advanced (still far from representing human consciousness mathematically) to understand these languages natively or learn them as humans do. Natural Language Processing, is a branch of Artificial Intelligence that provides methods via which machines can make some sense of those native languages via mathematical equations and advanced machine learning techniques. ## insert\_linkHow does NLP actually work? ![How Natural Language Processing(NLP) Works? - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/how_does_nlp_work_ae722d0e0d.svg) _How Natural Language Processing(NLP) Works? - Scalifi Ai_ Let us start by thinking about how did we learn the language that we are communicating in? There are different strategies for learning and understanding a language depending on if you already know any other language and your age group. To better understand NLP let's consider how a child learns and starts understanding a particular language: 1. Children start understanding a language through interaction - not only with their parents and other adults but also with other children. All normal children who grow up in normal households, surrounded by conversation, will acquire the language used around them. 2. Our brain continuously tries to understand the meaning of words, what they mean, and what they represent. Over a period of time, this collective knowledge helps us in forming meaningful sentences and express our thoughts. As machines work on pure numbers and understand only numbers we need a way to convert our language, words, and their correlation into a numerical representation. This is done by building Word Embeddings which try to capture the meaning of words and how they are related to other words. ## insert\_linkIntroduction to Neural Word Embeddings in NLP ![Word Embedings in NLP - Scalifi Ai](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2Fword_embedings_in_nlp_cb3b0464f0.webp&w=3840&q=75) ( _[Source](https://www.analyticsvidhya.com/blog/2021/06/practical-guide-to-word-embedding-system/)_) Word embeddings are vector representations of a collection of words (vocabulary) which are then used to create a high-level representation of any sentence using those vectors. The most common word embeddings include Glove and Word2Vec. These embeddings are pre-trained on a large corpus of data with different training methods and models like Bag of Words (BOW) and Skip N-Gram training methods. (In this article we will not get into the depth of how these word embeddings are created and trained) > This means that each word is represented by an array of numbers, if we calculate the cosine similarity between two words that gives us what their semantic similarity is. > > Eg: Based on pretrained embedding [“glove-twitter-25”](https://nlp.stanford.edu/projects/glove/) > > If we calculate the cosine similarity between the words “cat” and “dog” it comes out to be 0.95908207 > > And if we calculate the cosine similarity between the words “cat” and “france” it comes out to be 0.36348575 > > This shows the semantic similarity between the chosen words and in turn helps any machine learning model to understand the English language better semantically. We can clearly see that the value for words “cat” and “dog” is way higher (as they are both animals that too four legged) and the value between “cat” and “france” is way lower (as one is an animal and one is a country) Following are the vector representations of the words discussed above: ![Vector representation for word cat NLP - Scalifi Ai](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2Fvector_representation_for_word_cat_nlp_f7e88bc1f6.webp&w=3840&q=75) _Vector representation for word cat NLP - Scalifi Ai_ ![Vector representation for word dog NLP - Scalifi Ai](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2Fvector_representation_for_word_dog_nlp_d01805a7ad.webp&w=3840&q=75) _Vector representation for word dog NLP - Scalifi Ai_ ![Vector representation for word France NLP - Scalifi Ai](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2Fvector_representation_for_word_france_nlp_262df0761b.webp&w=3840&q=75) _Vector representation for word France NLP - Scalifi Ai_ Let's better understand the above examples with a 3D visualization. ![3D Visualization Illustration - Scalifi Ai](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2F3d_visualization_df88a52473.webp&w=3840&q=75) I hope the above plot gives a better understanding of what the vectors represent and how similar words can be easily differentiated if the word embedding is trained properly on a clean and well-processed corpus > The above illustration is just for understanding purposes and has been generated by compressing each word vector to have only 3 values (x, y, z) so that we can visualize it. > > This visualization gives us a better understanding of the vector overall despite having only 3 dimensions, hence any machine learning model utilizing pre trained word embeddings gets better semantic understanding of words as they have even more dimensions / granularity to play with. Few pre trained word embeddings go up to 300 values per vector for better granularity and performance. ## insert\_linkConclusion I hope this article serves as an overview of how machines are able to understand natural languages and perform complex calculations over them for applications like Named Entity Recognition, Auto Correct and Autocomplete tasks, Text generation based on prompts, etc. In the upcoming articles, we will see how we can train our own embeddings, different ways of training (their pros and cons), and use them in real-life applications. To have your own NLP space and to get a better understanding of AI Concepts without the compulsion of writing code, you can head onto Scalifi Ai, which is a No-Code platform built for creating artificial intelligent pipelines and services. #### External References 1. [What is Natural Language Processing](https://monkeylearn.com/natural-language-processing/) 2. [What is Word Embeding?](https://www.ibm.com/topics/word-embeddings) 3. [What is Named Entity Recognition](https://www.techtarget.com/whatis/definition/named-entity-recognition-NER) 4. [What is Sentiment Analysis](https://aws.amazon.com/what-is/sentiment-analysis/) 5. [Master NLP in Python](https://www.analyticsvidhya.com/blog/2021/06/part-2-step-by-step-guide-to-master-natural-language-processing-nlp-in-python/) 6. [NLP Roadmap](https://www.analyticsvidhya.com/blog/2022/01/roadmap-to-master-nlp-in-2022/) Related: Natural Language Processing NLP AI in Education AI in Healthcare AI Innovation ##### In this blog [Introduction to Natural Language Processing](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp#introduction-to-natural-language-processing) [What is Natural Language Processing(NLP)?](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp#what-is-natural-language-processing(nlp)) [How does NLP actually work?](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp#how-does-nlp-actually-work) [Introduction to Neural Word Embeddings in NLP](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp#introduction-to-neural-word-embeddings-in-nlp) [Conclusion](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp#conclusion) [FAQs](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs The Beginner's Guide to AI Models: Understanding the Basics [Read moreeast](https://www.scalifiai.com/blog/what-is-an-ai-model) In-Depth Study of Large Language Models (LLM) [Read moreeast](https://www.scalifiai.com/blog/what-is-large-language-model-llm) ###### Explore other usecases AI in Cyber Security to Redefine the Security Posture [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Customer Churn Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) Revenue Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) --- --- ## LLM Conversation Branching on GPT, Gemini, Claude Native Interfaces vs. Cognis Ai Source: https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # LLM Conversation Branching on GPT, Gemini, Claude Native Interfaces vs. Cognis Ai 3 February 2026\|10 min read Most AI chats still run in a straight line: prompt, answer, prompt, answer. When you want a new direction, you often restart and rebuild context. That’s the friction LLM conversation branching removes. One chat becomes a tree, with a stable trunk and many paths. In practice, you create branch conversations to test alternatives side by side. You keep the best result and discard the rest. This is nonlinear prompting with structure. It’s not just “more messages.” The payoff is practical. You get clearer context memory and fewer wrong turns. Branching also gives you a record of how decisions happened. That’s why the chat GPT branch feature is often treated like version control for AI chat. In today’s guide, you’ll learn what branching is, how it works, why it helps, and how to leverage branching for your benefit. You’ll also see why LLM conversation branching is increasingly treated as a workflow primitive, not a novelty. ## insert\_linkWhat Is Chat Branching (and How It Differs From Normal Chat)? Chat branching is the ability to fork a conversation at a specific message into a new, independent thread, while inheriting all context up to that split. ![LLM Conversation Branching vs. Single Chat](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FGemini_Generated_Image_lrtq66lrtq66lrtq_4f2830d219.png&w=3840&q=75) _Chat Branching vs. Single Chat_ ### Linear vs. multi-path A normal chat is a single lane. As you troubleshoot and explore side questions, the transcript accumulates extra tokens that don’t serve the main task. That bloat can become context pollution and eventually context rot.. Branching changes the structure. Each fork shares the same roots and then stays isolated afterward. That isolation improves context memory because each path only “sees” the shared prefix plus its own turns. ### Context inheritance vs. isolation A branch inherits the shared history before the split. After the split, it stays separate. This supports context isolation in LLMs and reduces topic bleed. ### The version-control framing A useful mental model is version control for AI chat: keep a stable baseline, then explore alternatives as separate paths you can compare. Research framed as ContextBranch formalizes this with primitives and shows why the chat GPT branch feature can reduce wasted effort. Done intentionally, LLM conversation branching also helps manage multi‑turn conversation degradation by preventing unrelated explorations from contaminating the trunk. ## insert\_linkWhy Conversation Branching Matters: Human-Aligned Thinking and Better Outputs People don’t think in straight lines. We revisit assumptions, test “what-if” paths, and compare options. If you want a quick mental model: branching helps you think in alternatives first, then converge. That convergence step is where teams save time, reduce duplicate work, and produce outputs that feel more consistent. ![An image showcasing comparison of linear chat as a tangled flowchart vs. LLM Conversation Branching as a clean flowchart.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FWhy_LLM_Conversation_Branching_Matters_03494e371e.png&w=3840&q=75) _Why LLM Conversation Branching Matters_ People don’t think in straight lines. We revisit assumptions, test “what-if” paths, and compare options. If you want a quick mental model: branching helps you think in alternatives first, then converge. That convergence step is where teams save time, reduce duplicate work, and produce outputs that feel more consistent.LLM conversation branching fits real work. It lets you explore breadth-first, without corrupting a baseline. Your brief cites ContextBranchfindings with measurable gains. Response quality improves by ~2.5% overall, and up to 13.2% in complex scenarios It also reports context-size reductions around ~58.1%. That improves focus and context awareness. The practical outcome is cleaner context memory. You can compare options without rereading a tangled transcript. Branching also changes how you instruct models. Instead of layering conflicting instructions, you keep the core stable. This is the operational meaning of the advantages of conversation branching. You create branch conversations, then choose a winner. With disciplined nonlinear prompting, you keep experiments comparable. You also reduce drift that can worsen multi‑turn conversation degradation. That’s when the chat GPT branch feature becomes a decision workflow. And that’s when version control for AI chat stops being a metaphor. ## insert\_linkHow Chat Branching Works in Practice: Fork, Navigate, Compare To make branching usable over time, treat navigation as part of the workflow.Rename branches by intent: - tone - constraint - audience - hypothesis add a short note about what changed, and decide upfront what will count as “good enough” to keep and easy to overuse. ![A diagram showing stages of LLM Conversation Branching as a flowchart in 3 sections. ](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FLLM_Conversation_Branching_in_Practice_ca9c0c387e.png&w=3840&q=75) _LLM Conversation Branching in Practice_ The core loop is: fork, label, compare, then converge. A common trigger is a real strategy shift. You edit an earlier message and choose the ChatGPT branch in the new chat. Both histories remain intact. You can return to the core and try another option with a branch in a new chat. ## insert\_linkCognis Ai’s True Multi-LLM, Infinite-Branch Workspace [Cognis AI](https://www.scalifiai.com/cognis-ai) is designed for teams who want branching to feel like a real workspace, not a pile of disconnected threads. [Cognis AI brings together all OpenAI models and Google Gemini models into a single chat window.](https://www.scalifiai.com/blog/cognisailaunch) You can switch between models from different providers seamlessly while creating infinite branches and sub-branches with complete traceability and tracking through a rich UI. That matters because comparison workflows are where branching either stays organized or turns into scattered tabs. Cognis AI keeps the tree visible and navigable so experiments can be revisited and audited. **For example:** If you’re comparing results from Gemini versus other outputs, Cognis AI keeps the shared core intact. If you’re comparing outputs from Gemini AI across subtasks, Cognis AI keeps those branches connected to the same core message. If you’re evaluating ChatGPT vs Gemini or running a Google Gemini vs ChatGPT workflow, Cognis AI keeps the decision trail unified while you switch models. Cognis AI combines ease of use and advanced memory management unlike any other available tool, via: - Keeping things organized in a single chat window - Unifying OpenAI and the Google Gemini ecosystems - Enabling seamless provider switching and infinite branching - Ensuring complete traceability and tracking ## insert\_linkBranching on ChatGPT vs. Claude vs. Google Ai Studio vs. Cognis Ai **Claude** **ChatGPT** **Cognis AI** **Parameters** **Google AI Studio** ❌ One AI that’s great at writing + reasoning ❌ One AI you chat with ✅ A system that uses multiple AIs together What is it? ❌ Google’s AI tool ❌ You can fork into a new chat thread. You lose track of versions easily. ❌ Branches into new threads. A messy experience because you lose track of branched versions which have identical names. You can only use ChatGPT models. ✅ Infinite branches in one chat, clearly separated, with separate memory for each branch. You can branch across different models using a combination of models like GPT, Gemini and Claude without worrying about missed context or context overlap. How does branching work? ❌ Branching allows you to move into a new thread. Only applicable for coding tasks. Works only with Google models and ecosystem. ✅Very strong writing and thoughtful answers. ❌ Does not negate bias. ✅Good answers, one at a time.❌ Does not negate bias. ✅Faster + better final output because it runs multiple options at once. Answers generated negate bias because multiple models are being used. What's your get as the final benefit? ✅Useful for some Google/multimodal stuff. ❌ Does not negate bias. ❌ Yes ❌ Yes ✅ No, you get GPT + Claude + Gemini, etc. Do I only get 1 AI? ❌ Yes ❌You ask again 10 times ❌You ask again 10 times ✅You branch and get versions quickly in clicks If I want 10 versions, how hard is it? ❌You ask again 10 times ⚠️ Kind of but version control becomes messy ⚠️ Kind of but version control becomes messy ✅ Yes - each idea gets its own branch Can I explore multiple directions without messing up the chat? ⚠️ Kind of but version control becomes messy ❌ Most of the time ❌ Most of the time ✅ No - context carries over automatically Do I have to repeat the context again and again? ❌ Most of the time ❌ No. Multiple browser tabs need to be open. ❌ No. Multiple browser tabs need to be open. ✅ Yes - side-by-side in one chat window Can I compare answers easily? ❌ No. Multiple browser tabs need to be open. ⚠️ You retry and risk losing history ⚠️ You retry and risk losing history ✅ You instantly check with another AI without losing previous history If one AI gives a bad answer, what happens? ⚠️ You retry and risk losing history ⚠️ Only Writing + deep thinking ⚠️ Only general tasks ✅ Getting general + writing and deep thinking more work done in less time Best for ⚠️ Multimodal + Google ecosystem ❌ One-model limitation. More bias. Difficult to use. ❌ One-model limitation. More bias. Difficult to use. ✅ None of the 'single AI' problems Main weakness ❌ One-model limitation. More bias. Difficult to use. ## insert\_linkBranching on GlobalGPT vs. TypeMind vs. T3.Chat vs. Cognis Ai Model aggregators often aim to reduce friction across models, but branching can still feel inconsistent. The key questions are: can you branch cleanly, see the tree, navigate fast, and keep experiments traceable? In a true branching workflow, LLM conversation branching is not just a retry button. It’s a structured tree where branched conversations remain connected to their parent branches. **T3 Chat** **TypeMind** **Cognis AI** **GlobalGPT** **Parameters** A fast chat app to switch between models A “better ChatGPT UI” where you can use many models A multi-llm + independent memory system built to help you get more work done, faster One app that gives you lots of AI models What is it? You can quickly switch between models You can use many models in one place You can access multiple models to create many versions/ideas without losing track You get access to multiple models Main benefit ✅ Somewhat ✅ Somewhat ✅ A lot ⚠️ Not much Does it actually help me do work faster? ❌ Yes ❌ Yes ✅ No - it’s structured to keep things clean ❌ Yes Do I still have to manage everything manually? ❌ Often ❌ Often ✅ Rarely / almost never ❌ Often Do I need to copy-paste prompts between models? ⚠️ Possible, but messy ⚠️ Possible, but messy ✅ Built for it ⚠️ Possible, but messy Can I generate 10–30 versions fast? ⚠️ Somewhat but easy to lose track ⚠️ Somewhat but easy to lose track ✅ Yes ❌ No Does it keep different ideas separate so I don’t mix things up? ⚠️ Kind of, but highly disorganized and time taking ⚠️ Kind of, but highly disorganized and time taking ✅Yes - properly ❌ Not really Can I run multiple “directions” at the same time? ✅ Mostly ✅ Mostly ❌ No - it’s a workflow system ✅ Yes Is it just “multi-model access”? People who just want speed + model switching Power users who like custom AI setups Sales + marketing + ops teams who need volume + clarity, or, indvidual power users automating work at scale People who want cheap multi-model access Who is it best for? Fast, but still “just chat” Great tool, but still manual work None Gives models, not a method Biggest weakness AI model aggregator + chat interface AI model aggregator + UI AI model aggregator + Rich UI + Memory + Advanced Branching. Turn 1 chat into 20 outputs without chaos. AI model aggregator In one sentence ## insert\_linkReal‑World Use Cases: How Teams Use LLM Conversation Branching **Team** **Use Case** **Description** **Why is branching an advantage?** Sales Prospect says 'we’re exploring' That sentence can mean different things: they are at an early stage, they already picked a competitor and want pricing, they do not have a budget, or they want to build internally. Each scenario needs a different strategy. Branching keeps them separate so the AI does not mix advice or produce confused follow ups. Marketing TOFU, MOFU, BOFU content One campaign needs different content for awareness, consideration, and conversion. In one chat, messaging starts blending and becomes repetitive. Branching keeps each funnel stage clean while still using the same campaign context. It improves consistency and saves time. Performance marketing A/B testing variants One concept needs many variants such as hooks, angles, CTAs, creatives, and audiences. One chat quickly becomes messy. Each branch becomes one test track so versions do not get mixed. Makes experimentation organized and repeatable. Social and content Same topic in multiple tones You need the same idea written in different styles like serious, educational, funny, contrarian, or data-based. One chat blurs tones. Branches keep tone consistent per version and prevent “half funny half formal” writing. Customer success Quiet customer and churn risk Silence can mean champion left, usage dropped, internal re-org, vendor switch, or procurement delay. Branching allows different retention plans without mixing assumptions. Makes outreach and internal notes clearer. Support and CX Same complaint, different root causes 'App is slow' could be device issue, bad network, outage, misconfiguration, API problem, or database load. Branching keeps troubleshooting paths separate, which reduces confusion in escalation and handoffs. Product Feature request interpretation 'Need integrations' could mean compliance requirement, automation, reporting, or competitor parity. Each has different roadmap impact. Branching helps explore each interpretation separately with clear requirements and tradeoffs. Founder and strategy Choosing positioning You may be considering premium, niche wedge, category creation, or low cost positioning. One thread makes you drift into vague middle ground. Branching keeps each option coherent so you can compare properly and choose one strategy. Talent acquisition Role definition is unclear 'Growth marketer' can mean paid ads, lifecycle, content, or generalist. Each needs a different interview loop. Branching helps create different JDs, scorecards, and interview plans without mixing evaluation criteria. Ops and finance Scenario planning Budget decisions change based on conditions like conservative plan, aggressive growth, downturn, or hiring freeze. Branching keeps scenarios separate while sharing baseline assumptions. Makes comparison easier. ## insert\_linkRisks of Branching and How Cognis Ai's Unified Interface Mitigates Risk Exposure Branching is useful, but it brings new problems once you start using it every day. **Risk one is sprawl.** Too many forks turn into clutter, and you waste time choosing between half-finished paths. There are limits too. Context caps are real, and you can’t “undo” them, and running many branches costs more compute. That’s why LLM conversation branching needs restraint. It’s also why the interface matters. One simple anti-sprawl rule is a branch limit per decision. Explore a few serious options, then merge and move on. Risk two is inconsistency. Every branch copies the starting context, including old or wrong assumptions. To reduce inconsistency, keep the shared context short and precise. If the base is wrong, every branch is wrong in the same way. **Risk three is privacy.** Sensitive details in the shared context can get duplicated across many forks. To reduce privacy exposure, check what’s inside the shared context before creating new branches. Cognis AI’s approach is to keep branches and sub-branches clearly linked to their parent threads. The UI keeps them easy to follow and easy to audit. Cognis also keeps model switching inside the same chat thread. That reduces switching overhead, prevents confusion, and avoids losing quality across long multi-turn conversations. Used properly, Cognis AI makes branching feel structured, not messy. ## insert\_linkFuture Evolution: Visualization, Selective Merging, Agents, and Multimodal Workspaces Branching is already useful, and it’s going to get more useful in a few clear ways. 1. **It’ll become more visual.** Instead of long scrolls, you’ll be able to see the whole conversation as a map. That makes it easier to understand where things split and why. 2. **Merging will improve.** Right now, combining two branches usually means copy-paste. In the future, you’ll be able to bring back only the specific points you want, not entire blocks of text. 3. **The system will suggest branching automatically.** If your chat starts going in two directions, the tool will nudge you to split it so things stay organized. 4. **Different branches may use different models.** One branch might use a model better at writing, another better at reasoning, another better at coding — depending on what you're doing. 5. **Branching will move beyond text.** It won’t just be chat threads. It’ll become a workspace where you can branch with images, notes, tables, and diagrams on the same canvas. Overall, branching will feel less like managing multiple chats, and more like navigating a structured workspace. And merging back the useful parts will become a normal step in the process. #### Frequently Asked Questions ###### How does chat branching work? In practice, people often edit an earlier message and use an action like the ChatGPT branch in a new chat to explore an alternate path. ###### What are branch conversations? They are parallel paths from the same baseline that remain independent after the split. ###### Why use chat branching? It reduces duplicate effort by letting you explore alternatives from the same baseline without copy-paste. It also creates a clearer decision trail because you compare outcomes side by side. ###### How is LLM conversation branching different from normal linear chat? Normal chat is one lane, so detours pile up. Branching isolates detours into separate paths, preventing context pollution and topic bleed. ###### Why does branching improve output quality? You compare alternatives side-by-side while keeping the baseline clean. Less drift, fewer wrong turns, and clearer instructions per path. ###### What’s the simple workflow to use branching? Fork when direction changes, label the branch by intent (tone/constraint/audience), add a note, compare outputs, then converge on one winner. ###### Why pick Cognis AI over native branching? Native tools split into messy threads and single ecosystems. Cognis keeps the full tree visible, supports infinite branches, and lets you switch models/providers cleanly. #### External References 1. Zang et al., (2025). A Survey on Parallel Reasoning. [Read here.](https://arxiv.org/pdf/2510.12164) 2. Building Autonomous, Resilient, and Intelligent Agentic AI Systems. Infosys Foundation. [Read here](https://www.infosys.com/techcompass/documents/building-autonomous-aria-systems.pdf) 3. Zhou et aL. (2025). CARD: A Cache-Assisted Parallel Speculative Decoding Framework via Query-and-Correct Paradigm for Accelerating LLM Inference. [Read here](https://arxiv.org/pdf/2508.04462) 4. B. C. Nanjundappa & S. Maaheshwari (2025). Context Branching for LLM Conversations: A Version Control Approach to Exploratory Programming. [Read here.](https://www.arxiv.org/pdf/2512.13914) 5. K. Salahi & P. Gurusankar (2025). More Effectively Searching Trees of Thought for Increased Reasoning Ability in Large Language Models. Stanford University. [Read here](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1244/final-projects/KamyarJohnSalahiPranavGurusankarSathyaEdamadaka.pdf) Related: AI Innovation Generative AI LLM ##### In this blog [What Is Chat Branching (and How It Differs From Normal Chat)?](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#what-is-chat-branching-(and-how-it-differs-from-normal-chat)) [Why Conversation Branching Matters: Human-Aligned Thinking and Better Outputs](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#why-conversation-branching-matters:-human-aligned-thinking-and-better-outputs) [How Chat Branching Works in Practice: Fork, Navigate, Compare](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#how-chat-branching-works-in-practice:-fork-navigate-compare) [Cognis Ai’s True Multi-LLM, Infinite-Branch Workspace](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#cognis-ai's-true-multi-llm-infinite-branch-workspace) [Branching on ChatGPT vs. Claude vs. Google Ai Studio vs. Cognis Ai](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#branching-on-chatgpt-vs.-claude-vs.-google-ai-studio-vs.-cognis-ai) [Branching on GlobalGPT vs. TypeMind vs. T3.Chat vs. Cognis Ai](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#branching-on-globalgpt-vs.-typemind-vs.-t3.chat-vs.-cognis-ai) [Real‑World Use Cases: How Teams Use LLM Conversation Branching](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#realworld-use-cases:-how-teams-use-llm-conversation-branching) [Risks of Branching and How Cognis Ai's Unified Interface Mitigates Risk Exposure](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#risks-of-branching-and-how-cognis-ai's-unified-interface-mitigates-risk-exposure) [Future Evolution: Visualization, Selective Merging, Agents, and Multimodal Workspaces](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#future-evolution:-visualization-selective-merging-agents-and-multimodal-workspaces) [FAQs](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs Cognis Ai (Beta): The Ultimate Agentic AI for all Your Professional Needs [Read Moreeast](https://www.scalifiai.com/blog/cognisailaunch) Human-in-the-Loop (HITL) Systems: The Future of AI [Read Moreeast](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai) The Key to Removing AI Slop - Memory Managed AI Agents [Read Moreeast](https://www.scalifiai.com/blog/agentic-ai-ai-slop) ###### Explore other usecases AI in Cyber Security to Redefine the Security Posture [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Revenue Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Low Code No-Code Ai Platform | Transformers Ai – Scalifi Ai Source: https://www.scalifiai.com/ Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Scalifi Ai - A Next-Generation ## Low-Code No-CodeAI Platform Increase the productivity of your workforce with our next-generation AI platform because it is no-code & low-code with easy drag & drop features [Try for free](https://www.platform.scalifiai.com/register) [Book a demo east](https://www.scalifiai.com/contact-us) ###### Global Scale ### Scalifi Ai Transforming Modern Businesses It is very difficult to integrate modern AI technologies to increase the efficiency of different business processes because one needs to have explicit & specialized technical knowledge. But not anymore, because Scalifi Ai's no code low code platform makes AI deployment a cakewalk! 99% Accuracy 87% Productivity 74% ROI 5x Faster Deployment ![transform business with scalifi ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/global_Section_Circle_fab6cc4af6.svg)![modern artificial intelligence technology](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/global_Section_Img_116df26432.svg) ### Unlimited Possibilities of Scalifi Ai Solutions auto\_awesome ##### Cognis Ai Suite Automate entire workflows from a single prompt [Know more](https://www.cognis-ai.com/) account\_tree ##### Model Building Build AI models in a No Code & Low Code Environment with Model Building [Know more](https://www.scalifiai.com/ai-ml-model-building) inventory\_2 ##### Model Catalog Easily Store & Manage AI Models with Model Catalog [Know more](https://www.scalifiai.com/model-catalog) dns ##### Model Training Easily Train AI Models Periodically with Model Training [Know more](https://www.scalifiai.com/contact-us) dns ##### Data Ingestion Ingest Data from any Storage System with Data Ingestion [Know more](https://www.scalifiai.com/contact-us) pie\_chart ##### Data Analytics Visualize your Data Effectively with Data Analytics [Know more](https://www.scalifiai.com/contact-us) ![deployment](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/deployment_641534a8c8.svg) ### Effortless Deployment of AI Solutions The complexity, cost, and time-consuming nature of traditional AI solutions are making it harder for businesses to use them. But Scalifi Ai solves this problem for modern businesses by providing scalable AI solutions that are affordable, easy to use, and can be deployed quickly to meet the needs of any business Deployment Cost Reduced by 10x ### No-Code & Low-code Platform Technical expertise is often required to build and deploy AI flows, making it difficult for non-technical people to automate their processes. But the Scalifi Ai’s no-code & low-code platform is designed to eliminate the need for technical expertise, allowing anyone to create powerful AI solutions without writing a single line of code. Time Saved Per Employee Daily by 5+ Hours ![no code and low code platform](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/nocode_4884b81de2.svg) ![Optimized Training of AI Models](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/scale_47abeefc16.svg) ### Highly Scalable AI Solutions Scaling AI solutions can be a challenge, especially for businesses that are rapidly growing or have a fluctuating demand for their services. With Scalifi Ai, scaling AI solutions is easy, as our platform is built to accommodate businesses of all sizes and industries. Our AI models scale with the flow of your growing business. AI Models can be Scaled by 99% ### Resource-Optimized Training of AI Models Training and tuning AI models can be a laborious and resource-intensive process, often requiring significant investment in time, money, and resources. Scalifi Ai’s platform automates the training and tuning of AI models, saving time, costs, and resources for organizations and letting them focus on other aspects of the business. Resource-Optimized Training of AI Models 6x ![Optimized Training of AI Models](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/ai_Model_7f59e7cbb2.svg) #### Integrate with Your Technology Stack Maximize your efficiency and streamline your workflows with Scalifi Ai's compatibility with the tools you already use and trust. Enhance your existing technology stack with the added power of AI. ![aws](https://www.scalifiai.com/_next/static/media/awsLogo.18ddbdca.svg) ![mySql](https://www.scalifiai.com/_next/static/media/mySql.f1a4a93f.svg) ![pgSql](https://www.scalifiai.com/_next/static/media/pgSql.e0f9dc44.svg) ![google sheet](https://www.scalifiai.com/_next/static/media/googleSheet.c6b81f5b.svg) ![one drive](https://www.scalifiai.com/_next/static/media/oneDrive.cc2620f1.svg) ![google drive](https://www.scalifiai.com/_next/static/media/googleDrive.a2eaf842.svg) ![ec2](https://www.scalifiai.com/_next/static/media/ec2.6cbeed40.svg) #### How Scalifi Ai Makes Your Work Effortless code\_off ### Low-Code & No-Code Platform We offer pure No-Code Low-Code services for building and integrating ML and DL models. tune ### Highly Customizable The architecture of Scalifi Ai is multi-layered which makes it easier to customize frontend and backend services. extension ### Seamless Integration We provide custom connectors & plugins to integrate our solutions with any existing apps you are using. extension ### Easy Drag & Drop Feature Anyone can use our platform to build & train AI models with just drag & drop and without writing a single line of code. linear\_scale ### Scale As You Grow Our solutions automatically scale as your company grows and adapts to changing situations & requirements swiftly. rocket\_launch ### Flexible Deployment Options You can deploy Scalifi Ai on cloud, on-premise & hybrid as per your business requirements. #### Find Solution For Different Use Cases ![ai in churn prediction](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/churn_Prediction_70f6a66d27.svg) ### Customer Churn Prediction Learn how the Churn Prediction solution by Scalifi Ai helps retain churning customers! [Read More](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) ![ai in cyber security](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/cyber_Security_03ff6d08aa.svg) ### AI in cyber security When loopholes in traditional security make businesses vulnerable, AI comes to the rescue! [Read More](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) ![credit card fraud detection](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/credit_card_fraud_1_f3c7e18d4e.svg) ### Credit card fraud detection How Scalifi Ai’s Credit card fraud detection is helping businesses to validate credit card and avoid risk! [Read More](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) ![revenue prediction](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/revenue_Prediction_4a8107fe98.svg) ### Revenue Prediction Scalifi Ai’s Revenue Prediction solution helps business leaders make informed decisions based on accurate predictions of the revenue! [Read More](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) #### Frequently Asked Questions ![scalifi ai faqs](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/faq_Circle_c5b0483a96.svg) ## Do I need to have advanced coding knowledge for building AI Models? Our platform offers no-code & low-code features and if you have basic knowledge of machine learning then you can easily drag and drop the frameworks and build the required AI model as per your requirement. You don’t have to write a single line of code, which means that both your time & efforts are optimized. In case, you don’t have knowledge of machine learning, then you can use our auto ML feature where the entire process is automated. With auto ML, you can automate the selection, composition, and parameterization of ML models. Hence, that’s why we say that anyone can use Scalifi Ai’s platform to create robust AI models. ## What is the difference between low-code & no-code? Low code platforms mean that you will get a visual development environment that will allow you to build AI models with minimal hand-coding. It typically involves a drag-and-drop interface and pre-built components. Basic coding knowledge will be required to operate in low-code platforms. Whereas a no-code platform will enable you to create AI models using visual interfaces, drag-and-drop functionality, and pre-configured components. No coding knowledge is required for operating in no-code platforms. Scalifi Ai provides both low-code & no-code features in the platform to enable both technical & non-technical users to build AI models while saving valuable time & effort. ## Where is my data stored? You can choose where you want to store your data. You can keep your data in the Virtual Private Cloud (VPC) or any other database that you are using. Scalifi Ai won’t store or copy any of your data without your consent. You can also choose to store your valuable data on Scalifi Ai’s secure servers as well. We ensure high-level data confidentiality in all our databases, clouds & servers. You can also leverage Scalifi Ai’s specialized feature for Data Security called Data Confidentiality, which is designed to keep all your data safe & protected. ## Can I train the AI models on our organization’s VPC (Virtual Private Cloud)? Yes, you can easily train the AI models in your organization's VPC (Virtual Private Cloud), and Scalifi Ai won’t store your data or make a copy unless you choose to do so. You can sync your VPC & databases to feed the AI models with relevant data for training purposes. Scalifi Ai provides a flexible platform for no code model building & training which does not require you to copy or store any of your data in Scalifi Ai’s servers unless you wish to use our secure servers and clouds to store your data securely. We focus on data confidentiality and enforce policies and special measures to ensure the privacy of our clients. ## How AI Models can optimize my business processes? AI models offer numerous benefits in optimizing business processes. Through automation, they can streamline repetitive tasks, freeing up valuable time for employees to focus on more strategic activities. Additionally, AI models excel at analyzing vast amounts of data, enabling informed decision-making and uncovering valuable insights that may otherwise go unnoticed. By harnessing the power of AI, businesses can deliver personalized experiences to their customers. AI models analyze customer data to understand preferences, behaviors, and purchase history, enabling targeted recommendations, offers, and marketing campaigns. This personalized approach enhances customer satisfaction, and engagement, which will drive more conversions & repeat sales. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Scalifi Ai Pricing Plans for Model Catalog Source: https://www.scalifiai.com/pricing/model_catalog Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) ##### Explore Our Planssell ### One Platform. Multiple AI Workspaces. ### Automate the Work That Slows You Down. Unlimited free trial No card needed Cancel plans anytime No forced contracts ### Community [Free Trial](https://www.platform.scalifiai.com/register) ### Essential [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=model_catalog&plan=essential¤cy=INR) ### Professional [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=model_catalog&plan=professional¤cy=INR) ### Enterprise [Contact Us](https://www.scalifiai.com/contact-us) #### Modules [**Model Building**](https://www.scalifiai.com/pricing/model_building) [**Model Catalog**](https://www.scalifiai.com/pricing/model_catalog) [**Cognis Ai**](https://www.scalifiai.com/pricing/cognis_ai) Indian rupee (₹) Model Catalog Community [Free Trial](https://www.platform.scalifiai.com/register) Essential [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=model_catalog&plan=essential¤cy=INR) PopularProfessional [Get Started](https://www.platform.scalifiai.com/subscription-plans/checkout?module=model_catalog&plan=professional¤cy=INR) Enterprise [Contact Us](https://www.scalifiai.com/contact-us) Price (per organization/month) Free ₹5600 ₹14000 Custom Included seats 1 4 7 Custom Max seats 1 8 15 Custom Price per additional seat Not applicable ₹1400 ₹1200 Custom Simple Metadata Entries 20 1000 8000 Custom Storage (GB) 5 (GB) 550 (GB) 1800 (GB) Custom Tags (Model) 10 50 200 Custom Note: - All listed prices are exclusive of applicable taxes. Please check with your bank regarding any forex charges. - All listed prices are only for monthly billing cycles. - † Included at no additional cost, subject to reasonable use under our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy). #### Frequently Asked Questions ![scalifi ai faqs](https://www.scalifiai.com/_next/static/media/faqCircle.3d68e398.svg) ## What is the Model Catalog Service? Scalifi Ai’s Model Catalogue Service acts as a central library for all the machine learning models used within your organization. It acts like an organized inventory, keeping track of various aspects of these models through metadata. This metadata can include details like versions, variants, and its signature configuration, tags, description, etc. ## How does the Model Catalog Service pricing work? The prices mentioned above are monthly subscription costs for the Model Catalog Service. Our pricing is distributed into three pricing tiers. The Community plan is the free tier. This is a completely free trial, and you needn’t enter any payment details for this either. For indie/small teams, we recommend the Essential Plan. The Professional plan is generally recommended for mid-sized/small-enterprise teams. Please refer to the pricing table to explore all the features. In case the above-mentioned plans do not meet your needs, we offer custom pricing for enterprises depending on your usage limits. Please [Contact Us](https://www.scalifiai.com/contact-us) or email us at helpdesk@scalifiai.com. _\*Note: All the figures mentioned in the pricing table are excluding taxes._ ## Can I switch plans at any time I want? Yes, you can switch plans at any time that you want. Billing will be adjusted pro rata based on the current usage cycle. You can either upgrade or downgrade your plans. Upgrading a plan refers to when you move from a lower-value plan to a higher-value plan. Example: You move from Essential to Professional. When you upgrade, your billing cycle also changes to the same date of the request, and credits are allocated pro rata. Please refer to the rollovers and limits mentioned in the pricing table for more information. A downgrade refers to when you move from a higher value plan to a lower value plan. Example: You move from Professional to Essential. For downgrades, you have two options: an Immediate Downgrade starts the new, lower value plan right away. This changes your billing date to the day of the downgrade request, and you get no refund for the current cycle. Alternatively, you can choose an End-of-Cycle Downgrade to finish your current cycle, and the new plan will start on your next billing date, in which case your credits for the current month remain unchanged. ## What happens if my payment is overdue? If your payment is overdue, create/update actions are blocked after 4 days from the payment date of your recurring billing cycle. After 8 days, you are automatically shifted to the Community Plan, and all the features and resources tied to your current plan are deleted and reset. ## How is billing processed? When you submit a Quota Request, you have to make payments through the Razorpay gateway. A payment window of up to 72 hours is available to complete your payment. ## In what currency do I have to make the payment? You can make the payment in your local currency. Currently, all the prices mentioned for all the plans are in Indian Rupees (INR) and are \*exclusive of taxes and any \*\*forex charges. _\*In case of a detailed breakdown for taxes, please view your invoice._ _\*\*In case of a detailed breakdown of forex charges, kindly get in touch with your banking partner._ ## How many IAM policies can I create? Currently, we do not have any limits on the number of policies that can be created. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. ## How many third-party accounts (TPAs) can I configure? Currently, we do not have any limits on the number of third-party accounts that can be connected. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. ## How many data sources can I connect? Currently, we do not have any limits on the number of data sources that can be connected. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. ## How many API Keys can I connect to my account? Currently, we do not have any limits on the number of API keys that can be connected. However, the limits are subject to our acceptable usage policies. Please refer to our [Acceptable Use Policy](https://www.scalifiai.com/legal/platform/acceptable-use-policy) or contact us at helpdesk@scalifiai.com for more information. Experience the next gen platform ## Schedule a demo and witness the future [Request a Demo](https://www.scalifiai.com/contact-us) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Third Party Accounts | IAM Guide Source: https://www.scalifiai.com/docs/iam/third-party-accounts Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # Third Party Accounts There are cases when you have data on a platform and you need to pick/share that data in a service/application. In such cases you can link your account as a third party to Scalifi Ai's platform. Third party accounts lets you connect your external accounts to the platform and share your data between services/platforms. Allowed operations that users can perform on the platform are: 1. Connect Third Party Account 2. Link Google Account 3. Link AWS Account 4. Delete Third Party Account ## insert\_linkConnect Third Party Account 1. To go to the third party account page, click on the profile icon in the top right corner. Select Third Party Account account from the dropdown list. ![Third Party Account Nav Bar Navigation - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Third_Party_Account_In_Nav_Bar_4bcc76a73d.svg) 3. Click on the Add Account button to connect a third party account. ![Add Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Add_Third_Party_Account_c2752ebe30.svg) 5. To add a new user to the platform, go to the IAM module. ![Select Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Select_Third_Party_Account_c8fac96d05.svg) 7. This guide will walk you through the configuration process of both Google and AWS. You can directly skip to the desired provider if you need. ## insert\_linkLink Google Account 01. From the list of Providers, select Google. Then click on the Link Account button. ![Add Google Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Add_Third_Party_Account_Google_090a7dfe58.svg) 03. You will be redirected to the google sign in screen. If you have your account signed in then select the account. ![Authorize Google Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Authorize_Account_Google_900d9e0aaf.svg) 05. Once you connect the account you will see the third party account in the table. ![Linked Google Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Linked_Third_Party_Accounts_Google_649895f29c.svg) 07. Select the account, click on the **Actions** dropdown button and click on **Re-authorize**. ![Re Authorize Google Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Re_Authorize_Third_Party_Account_Google_9b103312a2.svg) 09. You will see a list of permissions that can be granted to the platform. Select the desired permissions and click on **Submit**. ![Re Authorize Google Third Party Account Permissions - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Re_Authorize_Permissions_Google_357d5249ad.svg) 11. You will be redirected to google login screen to select the account. Select the account. ![Re Authorize Account Select Google - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Re_Authorize_Account_Select_Google_cd0862820e.svg) 13. Google will ask you to verify the permissions that are requested. You will see all those permissions that you have selected. ![Re Authorize Verify Google Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Re_Authorize_Verify_Google_dc707abcdf.svg) 15. Once you allow the permissions you will be redirected to the platform. You can verify in the table if the permissions are added. 16. Congratulations you have linked and authorized your google account to the Scalifi Ai platform. ## insert\_linkLink AWS Account 01. From the list of Providers, select **AWS**. You will need the Access Key and Secret Key of your AWS account to link the account. If you don't know how to get the keys you can check the AWS guide. Enter the Access key and Secret key and click on the **Link Account** button. ![Add AWS Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Add_Third_Party_Account_AWS_8eb920191d.svg) 03. Once you link the account you will see your AWS account in the link. Select the account, click on the **Actions** dropdown button and **Re-authorize**. ![Re Authorize AWS Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Re_Authorize_Third_Party_Account_AWS_c35b0c8e12.svg) 05. Select the permissions you want the platform to have of the AWS account and click confirm. ![Re Authorize AWS Permissions - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Re_Authorize_Permissions_AWS_df1c99bf3b.svg) 07. You will see a list of AWS options and corresponding resources with a checkbox. All the checkboxes are by default checked. If it is checked the resource will be (\*) for that option. 08. If you want to add the custom resources, you uncheck the checkbox and add your custom resources. Once done click on Submit. ![Re Authorize AWS Third Party Account Options - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Re_Authorize_Options_AWS_e8c6692c78.svg) 10. Congratulations, you have successfully linked and authorized your AWS account. ![Linked Third Party Accounts Updated Permissions AWS - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Linked_Third_Party_Accounts_Updated_Permissions_AWS_9d706e810a.svg) ## insert\_linkDelete Third Party Account You can delete any third party account if you don't need it any time. But you can delete only 1 account at a time. To delete an account, select the account, click on the **Actions** dropdown button and click on the **Delete** button. If all the checks pass, type delete in the input field and click on the **Confirm** button and your account will be unlinked from the platform.. ![Delete Third Party Account - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Delete_Third_Party_Account_d58fd7e13d.svg) Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [PreviouswestPolicy Management](https://www.scalifiai.com/docs/iam/policy-management) [NextModel Designseast](https://www.scalifiai.com/docs/model-building/model-design) ##### Content [Overview](https://www.scalifiai.com/docs/iam/third-party-accounts#overview) [Connect Third Party Account](https://www.scalifiai.com/docs/iam/third-party-accounts#connect-third-party-account) [Link Google Account](https://www.scalifiai.com/docs/iam/third-party-accounts#link-google-account) [Link AWS Account](https://www.scalifiai.com/docs/iam/third-party-accounts#link-aws-account) [Delete Third Party Account](https://www.scalifiai.com/docs/iam/third-party-accounts#delete-third-party-account) * * * hide\_imageHide Images --- --- ## Design Best Practices of AI Function Calling in 2025 Source: https://www.scalifiai.com/blog/function-calling-tool-call-best-practices Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Best Practices for Function Calling in LLMs in 2025 2 December 2025\|15 min read This blog is for developers, engineers, product managers, technical leaders, AI researchers and architects, enterprises, startups, and AI enthusiasts. If you’re new to AI, we suggest reading [The Beginner's Guide to AI Models: Understanding the Basics](https://www.scalifiai.com/blog/what-is-an-ai-model) and [In-Depth Study of Large Language Models (LLM)](https://www.scalifiai.com/blog/what-is-large-language-model-llm) to understand the contents of this article better. ## insert\_linkTL;DR - Function calling is the bridge from conversation to action, letting LLMs use real-world tools and APIs. - The LLM acts as a translator, converting user requests into structured JSON commands for your application to execute. - This unlocks powerful use cases like live data retrieval, workflow automation, and multi-step reasoning. - It introduces major technical hurdles in language ambiguity, schema complexity, and performance latency. - Security is the most critical risk, with prompt injection attacks posing a severe threat to your data and systems. - Success demands disciplined engineering with clear schemas, smart orchestration patterns, and robust feedback loops. - The future is standardization around JSON Schema to solve today's messy M x N integration problem. - This is the foundational step towards creating and orchestrating complex, autonomous agent ecosystems. ## insert\_linkIntroduction to Function Calling Since their inception, Large Language Models have been the poster child for the next generation of technology. Stalwarts of the AI world, including Sam Altman, Satya Nadella, Sundar Pichai, Elon Musk, etc., point to how they are redefining work. As brilliant and eloquent as LLMs are, they used to be utterly powerless. They were digital oracles, trapped within the confines of their training data, like a static snapshot of the past. An LLM could write a poem about rain, but it couldn't tell you the current weather. This fundamental gap between conversation and action limited AI’s true potential. ![A diagram showing how an LLM uses "Function Calling" to connect to "Real-Time Execution" and "External Tools/APIs," leading to "Action & Insights."](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FHow_Function_Calling_Empowers_LL_Ms_e918d67c2f.png&w=3840&q=75) _How Function Calling Empowers LLMs_ Enter Function Calling. This isn't just another feature; it's the bridge that finally connects the immense reasoning power of LLMs to the real, functional world of APIs, databases, and external tools. Tool calling is an evolutionary leap that transforms LLMs from know-it-alls into do-it-alls, allowing them to execute tasks, fetch live data, and interact with systems on a user's behalf. But this newfound power introduces a new layer of complexity. The line between a seamless, intelligent agent and a clumsy, error-prone bot is razor-thin, and it is drawn by the proper implementation of function calls. In this blog, we'll dissect function calling. We'll explore what function calling is and how its intricate mechanism works. This blog also highlights the common limitations one might face while using function calling and how [Cognis Ai](https://www.scalifiai.com/cognis-ai) helps overcome these complexities. ## insert\_linkFundamentals of Function Calling ### What is Function Calling? At its core, function calling gives the 'brain-in-a-jar' LLM a toolkit and a direct line to the outside world. It is the structured protocol that allows the model to translate your natural language request into a precise, machine-readable command for an external tool or API. This is the mechanism that finally gives your AI the ability to **act.** Crucially, the LLM itself doesn't execute the function. It acts as a translator, converting your intent into a structured **JSON** command with the correct function name and arguments. Your application receives this command and performs the actual execution. This delegation creates a secure barrier, ensuring the LLM directs the action without running the underlying code itself. This process mirrors traditional software development. The LLM's output isn't the function itself. It is a structured request to run one. Think of it as an intelligent API call. The model dynamically chooses the right endpoint. It also extracts the necessary parameters from your prompt. This creates a natural language front-end for your existing code and APIs. ### How LLMs Handle Function Calls So how does this process actually work? It is not a single, magical step. It is a structured dance between the LLM and your application code. This workflow ensures the model can reason about actions without executing them directly.' 1. **Tool Definition:** First, developers define the available tools for the LLM. They provide a structured schema for each function. This schema explains the tool's purpose. It also lists the required parameters for the model to use. 2. **Request & Decision:** The user requests natural language. The LLM analyzes this input. It decides if a tool is needed to answer. If yes, it generates a JSON object with the correct function name and arguments. 3. **External Execution:** Your application receives the JSON command from the model. It is responsible for running the actual code. The LLM does not execute the function. Your system calls the API or database and captures the result. 4. **Final Answer Generation:** The function's result is sent back to the LLM. The model uses this new information as context. It then generates a final, coherent language response for the user, completing the entire request. **Key Takeaway:** The LLM is the brain that decides what to do. Your application provides the hands to actually do it. **Example:** User asks ➔ LLM translates to a command ➔ Your app executes it ➔ The LLM provides the final answer. ## insert\_linkArchitecture of Function Calling in LLMs The entire function calling process relies on three core pillars. Each part plays a critical and distinct role in the workflow. Let's break down these essential components. ![A mind map of function calling architecture's three pillars: The Function Contract, The Execution Environment, and The Feedback Loop.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FKey_Components_in_the_Architecture_of_Function_Calling_b148330e42.png&w=3840&q=75) _Key Components in the Architecture of Function Calling_ ### 1\. Function Schemas (The Rules) Schemas are the non-negotiable rulebook for the LLM. They act as a stable contract. This contract defines each tool's unique name. It clearly describes its purpose. It also details all required parameters and their data types. Providing good examples in the schema often leads to better, more reliable outputs. ### 2\. Function Selection (The Decision) The LLM must autonomously make three critical choices. Should it use a tool? Which one? And how? It matches user intent to function descriptions. This helps it decide the correct action. The ability to detect when no tool is needed is a crucial test of its relevance detection. ### 3\. Structured Output (The Command) When the LLM decides to act, it acts decisively. It generates a structured function call. This is typically a JSON object. This output includes the chosen function name. It also contains the specific arguments or parameters. These are extracted directly from the user's natural language request. ### ⭐Case Study: Ordering Pizza for your dog Captain Floof Your Prompt: ‘Order a tiny pizza for Captain Floof.’ The LLM sees its order\_pizza function. The schema requires size and toppings. The model correctly generates the command. It sets toppings to ‘sunflower seeds and cheese’. ![A 6-panel comic showing an LLM translating "Order a tiny pizza for Captain Floof" into a structured order_pizza API command with inferred parameters.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Function_Contract_Case_Study_8f78f62601.png&w=3840&q=75) _The Function Contract - Case Study_ ### 4\. The Execution Engine (The ‘Doer’) The LLM does not run the code itself. An external engine must execute the action. This engine receives the LLM's command. It then runs the corresponding tool script safely. For robust agentic systems, this environment can be structured to handle complex computational tasks or search functions. ### 5\. Handling Missing Information (The Follow-up) What if a query is missing critical details? The system must recognize this information gap. It cannot guess or fail silently. Instead, the model is expected to generate a natural language response. It should ask the user a clear follow-up question to get the needed parameters. ### 6\. Multi-Step Planning (The Workflow) Function calling enables complex, multi-turn workflows. It uses an iterative, multi-step process. The core loop is: Thought, Action, Observation. This allows for advanced planning and execution. More sophisticated systems can even use control flows. This includes logic like IF-ELSE statements or loops for tasks. ### ⭐Case Study: Ordering Pizza for your dog Captain Floof The pizza shop's API receives the weird order. It has a problem with size: tiny. The smallest size is 'personal'. The system also lacks a delivery address. This is a classic case of missing info. Execution pauses. ![A comic shows a pizza order failing due to "Min size" and "delivery address" API errors, pausing the mission to await user clarification.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Execution_Environment_Case_Study_ce0df2f365.png&w=3840&q=75) _The Execution Environment - Case Study_ ### 7\. Return Values (The Observation) After the external system executes the action, its result is sent back. The returned data is called the Observation. It must be transformed into a simple format, like text. This observation grounds the LLM's final answer in real-world data, providing crucial context for its final response. ### 8\. Final Response (The Synthesis) The LLM receives the Observation data. It fully incorporates this new context into its reasoning. This is the final ‘Thought’ step. Using the retrieved, real-world data, it generates the final answer. This synthesized response is coherent, contextual, and directly addresses the user's original prompt with accuracy. ### ⭐Case Study: Ordering Pizza for your dog Captain Floof The API returns an error: Invalid size. It also notes: Address required. This ‘Observation’ is sent to the LLM. The LLM then synthesizes a polite response. It asks you for the correct size by listing available sizes and also for Captain Floof's full home address for delivery. ![A comic showing an LLM receiving API errors for "invalid size" and "missing address." The LLM then asks the user for a valid size and an address.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Feedback_Loop_Case_Study_7b5133fa51.png&w=3840&q=75) _The Feedback Loop - Case Study_ ## insert\_linkError Handling and Guardrails Connecting a creative LLM to real-world tools is powerful. It is also incredibly dangerous. Without strict controls, systems can fail, costs can spiral, and security breaches become inevitable. Guardrails are not optional features. They are the essential safety systems that prevent catastrophic failures. They are the barrier between a useful AI and a rogue agent. ### 1\. Input Validation The first line of defense is at the entry point. We must validate all inputs before they are processed. Bad data should never be allowed into the system. It is the simple principle of garbage in, garbage out. This prevents a huge number of downstream errors and protects the integrity of every tool call. ### Type Checking Every tool expects data in a specific format. It needs a number, not a word. It needs a list, not a single item. Type checking is a fundamental validation. It confirms every argument matches its required data type before execution. This simple check stops countless tool execution errors before they can even happen. ### Range and Format Validation Data can be the right type but still wrong. Is the quantity requested one million? Is the email address a valid format? Range and format validation enforces these business logic rules. It ensures all data is not just technically correct, but also sensible and safe within the application’s expected operational boundaries. ### 2\. Execution Safeguards Once an input is validated, the execution itself must be contained. This is the most volatile stage. A running process can get stuck, break, or be exploited. Execution safeguards are the walls of a secure sandbox. They ensure that even if a tool misbehaves, the damage is strictly limited and the system remains stable. ### Timeouts A tool call might hang indefinitely. A network could be slow. An API might be unresponsive. A timeout is a simple, brutal rule. It sets a maximum allowed time for any execution. If the process exceeds this limit, it is terminated automatically. This prevents a single slow tool from crippling the entire system. ### Circuit Breakers Some tools may fail repeatedly. An external API could be down for maintenance. Continuously calling a broken tool wastes resources. A circuit breaker monitors for such failures. After a set number of errors, the 'circuit' trips. It temporarily blocks all calls to that specific tool, allowing the system to fail fast. ### Security Allowlists Never define what is forbidden. Always define what is allowed. This is the core principle of an allowlist. Your system should have a strict, pre-approved list of tools it can call. Any attempt to invoke a function not on this list—whether hallucinated by the LLM or injected by an attacker—is instantly denied. ### 3\. Output Validation The final check happens after the work is done. The output from a tool or the LLM's final response must be inspected. This validation ensures the generated answer is correct, safe, and useful. It is the last quality gate before the information reaches the user, preventing the system from delivering bad or harmful data. ### Ensuring Correctness of Responses An LLM can still hallucinate, even with good data. Output validators check the final response against the source data. They can ensure the answer follows a logical chain of thought. If an error is found, the system can self-correct. It can re-ask the LLM with more context, providing a chance to fix its own mistake. ### Sanitization for Safety Outputs can contain security risks. An attacker might trick a tool into returning a malicious script. A response could include sensitive data. Sanitization is the process of cleaning the output. It strips out dangerous code. It masks private information. This ensures the final answer is completely safe to display to the user. ## insert\_linkUse Cases of Function Calling Function calling is not just a feature. It is the bridge from conversation to action. It transforms LLMs into active digital tools. This unlocks a vast landscape of applications. AI can now do work, not just discuss it. ### Practical Applications The applications of this technology are diverse. They range from simple data retrieval. To complex, multi-step process automation. These are the key categories of its impact. ![A diagram showing "Practical Applications" in a cycle: 1. Data Retrieval, 2. Workflow Automation, 3. Real-Time Operations, 4. Multi-Step Reasoning.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FPractical_Applications_b37a77ef0d.png&w=3840&q=75) _Practical Applications of Function Calling_ ### 1\. Data Retrieval Function calling shatters the wall to live data. The model is no longer a static database. It can now query external systems directly. This provides real-time, accurate, and relevant answers. Most humans do not speak SQL. LLMs can be a universal translator for data. They turn plain English into precise SQL queries. This democratizes access to business intelligence. No technical skill is needed to ask questions. The world's live data is locked behind APIs. Function calling is the key to this data. An LLM can check an order status on Shopify. It can find flights using a Skyscanner API. The LLM's knowledge becomes truly dynamic. ### Case Study **[Cognis Ai](https://www.scalifiai.com/cognis-ai) is a perfect example of how data retrieval works.** When users set up a 1-click integration with Cognis, the orchestration framework fetches all the google sheets. With your assistance it zeroes in on specific sheets/data sets. It enables simple prompts to become dynamic actionable tasks. For instance, you can prompt for an in-depth data analysis of the sheets Cognis has access to. ### 2\. Workflow Automation This is where LLMs stop just fetching data. They now begin to take direct action. It automates tedious and manual tasks. It manages schedules, notifications, and reminders. Manual scheduling is slow and inefficient. An LLM can parse a complex user request. It extracts attendees, topics, and times. It calls the Google Calendar or Outlook API. Your meeting is scheduled automatically. Routine communication can be fully automated. Function calls can trigger important alerts. The LLM can send emails via SendGrid. It can post updates to a Slack channel. This builds powerful, event-driven systems. ### Case Study **Using a tool like [Cognis Ai](https://www.scalifiai.com/cognis-ai) you could potentially automate workflows without facing the complexity of Zapier or Make.** Since Cognis leverages function calling combined with an agent orchestration framework, it can act in real time. Thus, your prompts are direct action commands. The requirements to build and maintain workflows are eliminated. ### 3\. Real-Time Operations Some tasks require immediate, live information. Function calling enables these real-time operations. This includes weather and financial data. It connects the LLM to the present moment. An LLM's internal knowledge is always old. Function calls provide a link to now. A call to a weather API gets live conditions. This is a simple but powerful capability. Financial operations are a high-stakes use case. An LLM can check live stock prices. It can retrieve bank balances via Plaid. However, moving money requires extreme caution. A human confirmation step is mandatory. ### Case Study If we want to update a Google Sheet, function calling allows LLMs to conduct real-time operations with current data. **But with a tool like [Cognis Ai](https://www.scalifiai.com/cognis-ai) and its stateful memory, LLMs can go a notch further.** This is because within Cognis, we get complete control over an LLM's context. It goes on to improve function calls and reduce hallucinations when LLMs work with real data. Basically, you get the intelligence while bypassing irrelevant information from the LLM’s mind! ### 4\. Multi-Step Reasoning This is the frontier of function calling. The LLM now exhibits agentic behavior. It combines multiple calls into a sequence. This solves complex, multi-step problems. True intelligence emerges when actions are chained. One function's output feeds the next input. For example, plan a complete travel itinerary. First, find a flight. Then book a taxi. Finally, reserve a hotel for the trip. The LLM can become a central orchestrator. It manages an entire business workflow. It uses conditional logic to make decisions. If a problem is billing, call the billing API. This is the future of true automation. ### Case Study LLMs can orchestrate tasks one after the other. However, there’s a drawback. LLMs often overrun their context window while doing so. It poses a problem because the LLM is unable to recall the next step/action. **Which is why Agentic orchestrators like [Cognis Ai](https://www.scalifiai.com/cognis-ai) can enhance LLM performance.** Such tools feed the tasks to LLMs while letting the LLMs handle execution. It ensures true multi-step reasoning by ensuring continuity. ## insert\_linkThe Current Technical Challenges of Function Calling Function calling is a powerful but fragile bridge. It connects imprecise language to precise code. This process creates significant technical hurdles. Challenges exist across language, structure, performance, and security. Each must be carefully managed. Failure in any one area can compromise the entire system's integrity and reliability. ### 1\. Ambiguity in Natural Language The primary challenge is the nature of human language. It is often messy, contextual, and ambiguous. Machines, however, require perfect, structured instructions. This fundamental conflict creates a constant risk of misinterpretation. Overcoming this ambiguity is the first and most difficult step in reliable function calling. ![A diagram shows ambiguous language ("book," "schedule") challenging an LLM function system, leading to either "SUCCESS" or "FAILURE: Misinterpretation."](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FAmbiguity_in_Natural_Language_992a9e2c72.png&w=3840&q=75) _Ambiguity in Natural Language_ ### Multiple functions matching a single request A single user request can be easily misinterpreted. It might match several available tools at once. For example, a request for 'Apple' could mean the company or the fruit. The LLM must reliably choose the correct tool. A wrong choice leads to irrelevant and frustrating user experiences. ### Disambiguation strategies Sophisticated strategies can resolve this ambiguity. Systems can use 'preference functions' to rank options. These scores help the LLM choose the best match. However, the simplest strategy is often the best. The model can ask the user a direct, clarifying question to confirm their true intent. ### 2\. Schema Complexity The LLM’s output must be technically perfect. It must generate a structured JSON command. This command must exactly match the function’s schema. This schema is a strict, unforgiving contract. Any small deviation from this contract will cause the function call to fail, breaking the user's workflow. ### Nested parameters Modern APIs often have complex, nested structures. Data might be buried in a deep hierarchy. For example: customers/orders/products. The LLM must reliably navigate this complexity. It must correctly populate all the required nested fields. This presents a significant structural challenge for the model. ### Optional vs. required fields Function schemas have both optional and required fields. The LLM must analyze the user's request. Does it contain all the mandatory information? If a required field is missing, the call cannot proceed. The schema must be crystal clear about these constraints to guide the LLM's output. ### 3\. Latency and Performance Function calls require network communication. Every external tool call introduces a delay. This creates significant performance challenges. These issues are very similar to those found in modern microservice architectures. Poor performance can make an AI assistant feel slow, clumsy, and ultimately unusable for any real-time tasks. ### Network delays Every external API call takes time to complete. This is due to network congestion and physics. The physical distance between services is a key factor. The main bottleneck is often the delay itself, not the raw network speed. High bandwidth does not guarantee low latency for these external calls. ### Parallel vs. sequential calls Most function calls are synchronous. The LLM waits for a tool to finish. Multiple sequential calls create long delays. This is a 'chatty' and highly inefficient pattern. The solution is asynchronous, parallel calls. This allows multiple tools to work simultaneously, reducing the total wait time. ### 4\. Security Risks This is the most severe and critical challenge. Giving an LLM tools to act in the world creates a massive attack surface. Security cannot be an afterthought. It must be the primary design consideration for any system that uses function calling. A failure here can have devastating consequences for users and businesses. ### Injection attacks The core danger is the prompt injection attack. An attacker inserts harmful instructions into a prompt. They trick the LLM into generating a malicious command. The LLM then executes the attacker's will unknowingly. This turns the model into a puppet, performing actions it was never intended to do. ### Unauthorized access A successful injection attack grants unauthorized access. The attacker effectively seizes control of the LLM's tools. They can tamper with anything the AI can access. A helpful assistant is instantly transformed into a malicious insider threat. This represents a complete compromise of the system's integrity. ### Sensitive data exposure Attackers can use prompt injection to steal data. The LLM can be tricked into revealing private information. This might include user emails, documents, or other secrets. This data exfiltration risk has been proven in real-world attacks. It is a critical threat vector that must be defended against. ## insert\_linkBest Practices for Design Patterns for Function Calling 2025 Building a reliable function-calling system is not an accident. It requires disciplined engineering and proven design patterns. Poor design leads to brittle, inefficient, and insecure AI agents. These best practices for 2025 are the essential foundation. They ensure your system is robust, scalable, and safe in a production environment. ### 1\. Schema Definition Best Practices The function schema is the most critical contract between the LLM and its tools. A clear, well-defined schema is the difference between a successful action and a frustrating failure. This is the bedrock of reliable communication. Ambiguity here will lead to systemic and unpredictable errors downstream. ![Best practices for schema definition: JSON Schema, Clear Parameter Naming, and Default Values create a "Contract" for "Reliable Communication" between LLMs and Tools.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FSchema_Definition_Best_Practices_4af892f22a.png&w=3840&q=75) _Schema Definition Best Practices_ ### JSON Schema usage JSON Schema is the industry standard for defining tools. It must detail all expected parameters, types, and constraints. Use minimal schemas when possible to save tokens. Tools like Pydantic are highly effective. They generate perfect schemas for LLMs. They also easily handle complex, nested parameter structures. ### Clear parameter naming Consistency in naming is not a suggestion; it is a requirement. Employ a single, clear convention like snake\_case. Predictable naming helps the AI reason about your API structure. This predictability is essential for effective automation. Enforce these design standards across all your tool definitions without exception. ### Default values Your schemas must support default parameter values. This makes working with optional arguments much simpler. You must also explicitly list all required fields. This provides a clear signal to the LLM. It tells the model when it must stop and ask the user for more information before it can proceed. ### 2\. Orchestration Strategies Orchestration is the art of managing complexity. It is how you coordinate the intricate dance between LLM calls, tool executions, and data flow. Simple tasks can follow a straight line. Complex, dynamic problems demand more advanced patterns. Choosing the right strategy is critical for both performance and accuracy. ### Sequential Execution This strategy breaks a complex task into simple, linear steps. Each LLM call processes the output of the previous one. This is the simplest approach to workflow design. While it is the least efficient, it can improve accuracy. Programmatic checks at each step are essential to keep the process on track. ![A flowchart showing sequential execution: Step 1 (Initial Task) -> Step 2 (Process Data) -> Step 3 (Final Result) -> Reliable Outcome, with validation checks.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FSequential_Execution_00da12782e.png&w=3840&q=75) _Sequential Execution_ ### Parallel Execution This pattern is built for maximum speed and efficiency. It involves running multiple LLM calls simultaneously on independent subtasks. A central orchestrator can delegate work. It breaks down a large problem. It then sends the smaller sub-tasks to multiple worker LLMs. This is a highly flexible and powerful approach. ![A diagram of parallel execution where an "Orchestrator LLM" delegates sub-tasks to multiple "Worker LLMs" for simultaneous processing of a large problem.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FParallel_Execution_d673f9bd0c.png&w=3840&q=75) _Parallel Execution_ ### Conditional Execution This enables dynamic, intelligent workflows. The system can make decisions and branch based on results. This is routing. An input is classified by the LLM. It is then directed to a more specialized tool or prompt. This allows for powerful IF-THEN logic. The workflow adapts in real-time to intermediate outcomes. ![A flowchart where an LLM Classifier directs input. "If Question" (A) goes to Tool A; "If Command" (B) goes to Tool B, leading to Outcome A or B.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FConditional_Execution_babacd781e.png&w=3840&q=75) _Conditional Execution_ ### 3\. Feedback Loops A robust AI system cannot be static; it must learn. Feedback loops are the mechanism for this continuous evaluation and refinement. They enable the system to correct its own mistakes and to ask for help when it is uncertain. These loops are essential for ensuring long-term accuracy, reliability, and safety. ### Model self-correction Use an Evaluator-Optimizer pattern. One component evaluates the LLM's output. The LLM then uses this specific feedback to refine it. This iterative loop turns failures into learning opportunities. Coach the model with precise error messages. Combine this with external validators to enforce a strict output structure. ### User confirmation prompts This is the ultimate human-in-the-loop safety net. Your system must ask for help when it is uncertain. If a user's request is vague or incomplete, instruct the LLM to ask clarifying questions. For critical or irreversible actions, you must require an explicit user confirmation prompt before any execution. ## insert\_linkFuture of Function Calling in LLMs The future of function calling is convergence. We are rapidly moving past fragmented, custom implementations. The destination is a unified, standardized agent ecosystem. The era of isolated models is ending. The era of collaborative, autonomous systems is beginning. This shift will define the next generation of artificial intelligence and its impact on the world. ### Prevailing Industry Trends This evolution is not random. It is driven by a clear and urgent need for greater power and less complexity. The entire industry is pushing toward three key trends. These forces are standardization, interoperability, and true autonomy. They are the pillars that will support the next great leap forward in AI capabilities. ### Standardization of schemas The current landscape is fragmented and chaotic. Developers must juggle multiple, competing protocols. The future is standardization. **JSON Schema** is emerging as the universal format. Major players like Google and Anthropic are converging on this standard. This will create a true lingua franca for all AI tools. ### Cross-model compatibility Today's integration is a nightmare. Every model needs a custom adapter for every tool. This is the M x N problem. New protocols will create a universal interface. **Anthropic's MCP (Model Context Protocol)** is a key attempt at solving this problem. **[You can read more on the benefits and pitfalls of MCPs here.](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025)** ### Expansion into autonomous agents Function calling is the fundamental building block. The next evolution is the autonomous agent. These are compound AI systems. They can reason, plan, and execute multi-step tasks. Future systems will feature multi-agent collaboration. The challenge is now orchestrating entire ecosystems of intelligent, autonomous agents at enterprise scale. ## insert\_linkDealing with the Limitations of Function Calling: Cognis Ai Function calling is a game-changer for LLMs. But, function calling implemented alone only results in marginal gains. Our extensive research on function calling and LLM limitations helped us identify the major pitfall - limited memory. When LLMs use function calls, they ingest a large amount of data, which often leads to overflowing the LLM’s context memory. As a result, even though LLMs gain access to real-time data, their execution capabilities are compromised. Hallucinations are a frequent occurrence. Prompts often get ignored. Conversely, data might be omitted as well. To solve this predicament, we made [Cognis Ai](https://www.scalifiai.com/cognis-ai). Cognis is an agentic orchestrator that has access to multiple LLMs. What Cognis does best is give data attribution control to the user and automate memory management. This takes the burden away from LLMs, enabling them to focus on applying their intelligence. Resultingly, it removes hallucinations and loss of critical information, which are the cornerstones of safe and reliable automation. Function calling is also deeply woven into Cognis’ architecture. It serves as the fundamental building block of an autonomous agentic framework. ## insert\_linkSumming it Up Function calling is more than a feature. It is the evolutionary leap for LLMs. It transforms them from passive oracles into active agents. This power, however, demands immense discipline. Robust design, strict guardrails, and thoughtful orchestration are not optional; they are the price of admission for building reliable and safe AI systems. The chaotic, fragmented era of function calling is ending. The future is one of standardization and interoperability. This will pave the way for complex, multi-system autonomous agents. Mastering these patterns today is the essential prerequisite for building the intelligent systems of tomorrow. #### External References 1. Winston, C., and Just, R. (2025) 'A Taxonomy of Failures in Tool-Augmented LLMs'. [Read Here](https://homes.cs.washington.edu/~rjust/publ/tallm_testing_ast_2025.pdf) 2. If you want to know more about LLM Agents, check out Berkeley's Deep Dive [here.](https://rdi.berkeley.edu/llm-agents/assets/llm_agent_history.pdf) 3. Wang, M., Zhang, Y. et.al. (2025) 'Function Calling in Large Language Models: Industrial Practices, Challenges, and Future Directions'. [Read Here](https://openreview.net/pdf/d01d50e27f7636724789f2aad6f4ac378749a0e1.pdf) 4. Google's Resource [here](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling) breaks down how to create function calling applications 5. Alshawi, H., and Carter, D. (2025) 'Training and Scaling Preference Functions for Disambiguation'. [Read Here](https://aclanthology.org/J94-4005.pdf) Related: Generative AI LLM AI Innovation ##### In this blog [TL;DR](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#tldr) [Introduction to Function Calling](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#introduction-to-function-calling) [Fundamentals of Function Calling](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#fundamentals-of-function-calling) [Architecture of Function Calling in LLMs](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#architecture-of-function-calling-in-llms) [Error Handling and Guardrails](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#error-handling-and-guardrails) [Use Cases of Function Calling](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#use-cases-of-function-calling) [The Current Technical Challenges of Function Calling](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#the-current-technical-challenges-of-function-calling) [Best Practices for Design Patterns for Function Calling 2025](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#best-practices-for-design-patterns-for-function-calling-2025) [Future of Function Calling in LLMs](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#future-of-function-calling-in-llms) [Dealing with the Limitations of Function Calling: Cognis Ai](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#dealing-with-the-limitations-of-function-calling:-cognis-ai) [Summing it Up](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#summing-it-up) [FAQs](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs Should You Be Using MCP - Model Context Protocol in 2025? [Read Moreeast](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025) In-Depth Study of Large Language Models (LLM) [Read Moreeast](https://www.scalifiai.com/blog/what-is-large-language-model-llm) The Beginner's Guide to AI Models: Understanding the Basics [Read Moreeast](https://www.scalifiai.com/blog/what-is-an-ai-model) ###### Explore other usecases AI in Cyber Security to Redefine the Security Posture [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Revenue Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Model Job | Model Building Guide Source: https://www.scalifiai.com/docs/model-building/model-job Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # Model Jobs Model Jobs within Scalifi Ai's MBS are comprehensive tasks or processes designed to handle different aspects of the machine learning model lifecycle. They include building, testing, exporting, and managing models with a focus on ease of use, efficiency, and adaptability to various computational needs. Each job type, from building the architecture to exporting for deployment, is tailored to streamline the workflow, reduce errors, and optimize performance for both high-demand and routine modeling tasks. They enable users to handle complex model operations with minimal coding, offering a user-friendly approach to sophisticated machine learning tasks. Allowed operations that users can perform on the platform are: 1. What is a Model Build Job? 2. Submit a Build Job 3. What is a Model Export Job? 4. Submit an Export Job 5. Terminate Job 6. Delete Job ## insert\_linkWhat is a Model Build Job? The Model Build Job is a robust feature designed for constructing and testing machine learning model architectures.It validates models against various hardware requirements and architectural constraints, providing a detailed report of memory and CPU needs along with errors if any. Users can choose the computational environment, such as shared or private virtual machines, to optimize performance and security. ## insert\_linkSubmit a Build Job **Note:** You will have to create a model design first before submitting a build job. It is a good practice to run quick build before submitting a build job to validate any base level errors in the model design. 02. From the model design canvas, go to **Build Model** → **Submit Build Job**. ![Submit Model Job - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_submit_model_job_cb9bde821d.svg) 04. You will be shown the requirement data like Total Memory, Total Peak Memory along with the model design layers/nodes information. ![Submit Model Job Total Memory - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_submit_model_job_total_memory_e28fff4fe4.svg) 06. Next, select the virtual machine as per the requirement. ![Select Virtual Machine Submit Model Job - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_submit_model_job_select_virtual_machine_f17267db40.svg) 08. Review the virtual machine information and click on **Submit Build Job** ![Review Model Job Before Submit - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_submit_model_job_review_cadc98f4b5.svg) 10. To view the build job information, go to Model Jobs from the header menu and click on the job you want to view. You will have the entire job information like Job Type, Progress, Status, Job Result, VM Configuration, Design Quick View, and a lot more organized in a single view. ![Per Model Build Job - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_view_per_model_job_e2e3a50263.svg) ## insert\_linkWhat is a Model Export Job? The Model Export Job facilitates the seamless transition from model development to deployment. It allows users to export their trained models in various formats, including framework-specific code or weight files. This feature also integrates with Scalifi Ai's Data Ingestion service to support a range of connectors, enabling efficient deployment across diverse environments like databases, cloud storage, or API endpoints. ## insert\_linkSubmit an Export Job 1. Scalifi Ai currently supports model export as a python script or jupyter notebook. You can export the model design including both options. **Note:** - Make sure to **Save and Build** the model design at least once before exporting it. - You need to have at least one file type connector configured with appropriate scope authorization to export the model, for example: AWS S3 Connector. 3. Once the model design is ready, you can export the model design, by clicking on the **Export** button. ![Export Model Design - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_export_model_design_c6d9758b66.svg) 5. Next, **Save and Build** the model design → Select source connector → Select target path. 6. Select the export options and virtual machine (if needed) in **Configure Export Options**. ![Configure Export Model Design Options - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_export_model_design_configuration_9f184fa5c3.svg) 8. Finally, review the export configuration and click on **Export Model**. You will find the exported file on the path you have chosen in the connector. ![Review Export Model Design - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_export_model_design_review_66795563d7.svg) ## insert\_linkTerminate Job 1. There are two ways to terminate a job. 2. Go to the Model Jobs section from the header menu, select the job you want to delete → go to Actions → Terminate. ![Terminate Model Job - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_terminate_model_job_f2a434b46d.svg) 4. From the model job page, select Action → Terminate ![Terminate Model Job From Per Model Job - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_terminate_model_job_from_per_job_cab72dfa20.svg) **Note:** - You can submit termination request for only one job at a time. - Termination is a dependent action. You will get proper errors while deleting a job if it is in active state. ## insert\_linkDelete Job 1. There are two ways to delete a job. 2. Go to the Model Jobs section from the header menu, select the job you want to delete → go to Actions → Delete. ![Delete Model Job - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_delete_model_job_201cb67f14.svg) 4. From the model job page, select Action → Delete. ![Delete Model Job From Per Model Job Page - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_delete_model_job_from_per_job_6ce2cafaf4.svg) **Note:** - You can submit deletion request for only one job at a time. - Deletion is a dependent action. You will get proper errors while deleting a job if it is in active state. Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [PreviouswestModel Designs](https://www.scalifiai.com/docs/model-building/model-design) [NextModel Managementeast](https://www.scalifiai.com/docs/model-catalog/model-management) ##### Content [Overview](https://www.scalifiai.com/docs/model-building/model-job#overview) [What is a Model Build Job?](https://www.scalifiai.com/docs/model-building/model-job#what-is-a-model-build-job) [Submit a Build Job](https://www.scalifiai.com/docs/model-building/model-job#submit-a-build-job) [What is a Model Export Job?](https://www.scalifiai.com/docs/model-building/model-job#what-is-a-model-export-job) [Submit an Export Job](https://www.scalifiai.com/docs/model-building/model-job#submit-an-export-job) [Terminate Job](https://www.scalifiai.com/docs/model-building/model-job#terminate-job) [Delete Job](https://www.scalifiai.com/docs/model-building/model-job#delete-job) * * * hide\_imageHide Images --- --- ## Cognis Ai Launch Note Source: https://www.scalifiai.com/blog/cognisailaunch Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Cognis Ai (Beta): The Ultimate Agentic AI for all Your Professional Needs 18 December 2025\|5 min read ![An image showing how Cognis Ai's Agentic Orchestration works. ](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FLaunch_Post_Image_for_Cognis_3e0fc18166.png&w=3840&q=75) _Cognis Ai_ Today, we’re releasing Cognis Ai (Beta), a new Multi-LLM Agentic AI Orchestration tool aimed at simplifying work to remove repetitive, mundane tasks for professionals and teams across marketing, sales, analytics, and more. It is designed to ensure that incorporating AI in work becomes simpler. Cognis leverages democratic principles by allowing users granular control over AI model context to reduce and eliminate AI slop. As the AI industry progresses, LLMs have gotten sharper, with more advanced reasoning and capabilities. Still, their enterprise-level utility remains questionable with failure rates exceeding 90%. As for individual users, widely shared best practices for prompting models can still lead to the generation of subpar responses. As a consequence of the same, trust levels for incorporating AI for serious professional tasks have remained low. Similarly, adoption has also been stunted due to the lack of interoperability of popular LLMs with tools beyond their respective ecosystems. Critical security risks of the Model Context Protocol (MCP) also make it untenable for large-scale enterprise adoption. Cognis Ai addresses these challenges. By leveraging a truly granular agentic design, Cognis shifts the most important element of LLM operations, context, from the LLM to the User. It gives users autonomy to assess, incorporate, and if necessary, remove data from the LLM’s context, effectively solving the recurring problem of hallucinations and AI slop. Through a simple-to-use yet interactive interface, Cognis also doubles down as a single source of truth or a universal interface for any user seeking to execute tasks across different applications and tools. ### Cognis Ai Cognis Ai is a Multi-LLM Agentic AI Swarm that's aimed at automating redundant and repetitive tasks. Using groups of specialized agents, Cognis ensures reliable context-aware output. Today, we’re introducing four primary components of Cognis Ai, which are live now: - **Multi-LLM Intelligence:** Access to 15+ models from OpenAI and Google’s ecosystem, like GPT 5, o1, GPT 4o, Gemini 2.5, etc. We also plan to add more models, such as Claude and Deepseek, soon. You can check the LLM provider’s page inside Cognis to stay updated. - **Universal Integrations:** Access Google Sheets and our in-house Content Writer extensions through Cognis Ai. We also plan to add 500+ integrations, including Google Workspace, Hubspot, Notion, Canva, Zoho, Microsoft’s Ecosystem, etc., in a phased manner over the next year. - **Dynamic UI Engine:** A rich in-chat UI interface that’s flexible and adapts to the task at hand. Get action buttons and live previews of your integration data in Cognis’ window. We also plan to add media generation and live charts soon.Retrieval/ranking tools - **Chat History and Branching:** Cognis allows you to switch between different LLMs at any given time. As a measure to ensure integrity and traceability, our chat branching feature stores the complete history of all the branches. For instance, if you switch between GPT 5, Gemini 2.5 Pro, and GPT o1 in the same chat, all the chat threads will be visible to you at all times. Multiple permutations and combinations are also stored in their entirety. ### The Vision of What’s Coming Next We built Cognis as a means to simplify work and automate tasks that feel tenuous. Following the same ethos, the upcoming roadmap of Cognis Ai aims to incorporate: - **Dynamic Observability:** Dynamic observability combines context and memory, offering users the ability to quickly summarize important in-chat data and add it to the persistent memory view of their chat. This allows users to overcome LLM context limits and reintroduce critical data to the LLM time and again without having to write extremely long prompts to avoid unintended hallucinations. - **Long-term Memory:** Account-based memory that stores crucial information, style guides, and other personalizations for enhancing your user experience. - **Workflow Automation:** The ability for Cognis to execute multiple tasks in a single workflow without the need for multiple prompts. Cognis will also learn the common pathways you take to ensure seamless and flexible workflows. ### Getting Started You can Get Started for free today with Cognis Ai’s Community Plan. Please visit [Cognis Ai](https://www.scalifiai.com/cognis-ai) and click on Get Started to begin your journey. Once you enter Cognis, you can use the quick setup to activate the popular LLMs in your account. Further, you can also configure more LLMs from the ‘Explore LLMs’ tab on the bottom left. ### Building a Feedback-Driven Community Cognis Ai is completely free for the community, and we’re eager to hear your feedback, as it will help us shape our product better for you. Whether you’re a professional, a student, an enthusiast, or an enterprise, we invite you to join us on our journey of shaping the future of context-aware and secure AI. [Get Started with Cognis Ai Now.](https://www.scalifiai.com/cognis-ai) #### Frequently Asked Questions ###### 1\. What is Cognis? Cognis is a workflow automation platform and AI chat assistant built to simplify your worklife. It uses Agentic AI and intelligent agents to automate repetitive tasks, create complete workflows, and act as your artificial intelligence personal assistant. With features like intuitive UI, near-zero hallucination for LLMs, and support for multiple AI models, Cognis combines the best of AI and automation into a single interface. ###### 2\. What LLMs can I use in Cognis AI? You can use multiple LLMs inside Cognis AI. The platform is designed as a multi-LLM orchestration layer, so you’re not locked into one provider. Supported LLMs include OpenAI models (GPT-5, GPT-4o, GPT-4 Turbo, GPT-3.5 series, o-mini family). Anthropic Claude models (Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku) Google Gemini models (Gemini 1.5 Pro, Gemini 1.5 Flash), DeepSeek (latest V3 and V3.1 reasoning models) and more models to be added soon. ###### 3\. What makes Cognis different from other AI apps? Most AI apps or automation apps are single-function. Cognis combines AI chat, agent software, and workflow automation platform capabilities in one. Instead of juggling multiple AI programs or AI tools free/paid, you get one artificial intelligence app where agentic AI manages context, memory, and execution/ ###### 4\. Who should use Cognis? Indie Builders who hate administrative work. Startups with small teams and big dreams. Professionals needing a chatgpt AI assistant or artificial intelligence personal assistant. Developers who want to create an AI or experiment with AI programs in one place. Enterprises wanting to level up by automating workflows without the hassle of 2 AM 'nothing's working with the other' calls. ###### 5\. Does Cognis address AI bias? Yes. Cognis includes monitoring to reduce AI bias across Chat GPT-4, Chat GPT-3.5, and other best AI models. It helps ensure fair, transparent outputs when you use it as your AI assistant or automation app. #### External References 1. [Explore](https://www.scalifiai.com/cognis-ai) the power of Multi-LLM Agentic Ai Related: Annoucements ##### In this blog [FAQs](https://www.scalifiai.com/blog/cognisailaunch#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/cognisailaunch#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs Should You Be Using MCP - Model Context Protocol in 2025? [Read Moreeast](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025) ###### Explore other usecases Customer Churn Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Walkthrough: How to Use the Content Writing Canvas in Cognis Source: https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Walkthrough: Content Writing Assistant on Cognis Ai 9 January 2026\|5 min read Hey, welcome to the walkthrough for Cognis Ai’s Content Writing Assistant. If you’re new here, Cognis is a Multi-LLM Agentic Ai System that allows you to leverage AI intelligence to execute tasks through clicks and prompts. It doubles down as a centralised workspace for all your work related actions. Visit [Cognis Ai](https://www.scalifiai.com/cognis-ai) to know more about Cognis. In today’s walkthrough, we’re going to dive into how to set up the Content Writing Assistant inside Cognis and how you can use it to generate Content. Let's get started. ## insert\_linkPre-Setup: Getting Started with Cognis So in the first step, you fire up Cognis by either registering or logging in via [this link.](https://www.scalifiai.com/cognis-ai) To register, please follow these steps: 1. Fill in your details 2. Log in after your registration is complete 3. Verify your email address 4. Access Cognis 5. Click on Setup Instantly to Configure the best LLMs in a single click You can begin with the free tier of Cognis without having to submit any kind of payment information. Click here to watch the interactive demo How to Get Started with Cognis (Pre-Setup) [![How to Get Started with Cognis](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/O0w7xadx51PA4M9EMAgx/image/e4af0664-aa4f-4c57-a512-c46cb9712698.png)](https://app.arcade.software/share/O0w7xadx51PA4M9EMAgx?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**How to Get Started with Cognis** **Learn how to register for Cognis Ai and set it up.**](https://app.arcade.software/share/O0w7xadx51PA4M9EMAgx?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkStep 1: Installing the Content Writing Assistant Integration Once you land on Cognis, you can follow the steps below to install the Content Writing Assistant:null 1. On the lower left side click on the ‘Explore Integrations’ tab 2. On the integrations page, the first integration is the Content Writing Assistant 3. Click on the ‘install’ button on the Content Writing Assistant Integration to successfully install it Click here to watch the interactive demo Install and Enable Content Writing Canvas Integration [![Installing the Content Writing Canvas](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/JFQuY8RIxxFREMds3fgK/image/d9623e8d-6c5e-4c8c-986d-54c5eb6f6f5a.png)](https://app.arcade.software/share/JFQuY8RIxxFREMds3fgK?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Installing the Content Writing Canvas** **Learn how to activate the Content Writing Canvas in Cognis.**](https://app.arcade.software/share/JFQuY8RIxxFREMds3fgK?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkStep 2: Launch the Content Writing Assistant in Cognis Once you set up the content writing assistant you click on the new chat button on the top left side. Prompting Cognis to write an article, blog, report, etc., will automatically launch the Content Writing Assistant. _Example Prompt: Write me an article on my n-line sneakers + \[product description of n-line sneakers\]_ Cognis will respond to you seeking permission to open up the ‘Specialized Writing Canvas’. You can just click on ‘Proceed’, to open up the Canvas. Click here to watch the interactive demo Launching the Content Writing Canvas [![Launching the Content Writing Canvas](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/https://image.mux.com/WpuyYwJHG1gdAcfRpZqKrvZ0200K02PbZDmaB418GdQ4IU/thumbnail.webp?time=0)](https://app.arcade.software/share/XqzpPIyjfe29LHmNhZ7Z?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Launching the Content Writing Canvas** **Learn how to launch the content writing canvas using a direct prompt**](https://app.arcade.software/share/XqzpPIyjfe29LHmNhZ7Z?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA In case you have a smaller communication draft like a single email, or a single social media post., Cognis generally doesn’t prompt you to open the Canvas. But if you’d like to still access the Canvas, follow the steps below: 1. Click on the ‘Direct to’ button located on the right side just above the chatbox 2. Choose ‘Content Writing Canvas’ 3. Then enter your prompt to directly send the prompt to the Content Writing Assistant 4. Click on the ‘Proceed’ button when Cognis prompts you to launch the specialized writing Canvas ## insert\_linkThe Three Stages of Content Creation on the Canvas In the Content Writing Canvas on Cognis, you follow a 3-step process for writing content. We specifically implemented this feature to ensure longer content is generated correctly and in a properly formatted manner. The three stages of writing in the canvas help you verify whether or not the LLM is on the right track: They function as follows: - **Ideation:** Generates a brief idea for the content piece that is to be generated - **Outline:** Drafts a section-by-section outline with a brief description of what is to be included in each respective sections - **Content:** Generates the final content as per the outline you’ve approved. Let’s go through these stages one at a time. Click here to watch the interactive demo 3 Stages of the Content Writing Canvas [![The 3 Stages of Cognis' Canvas](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/9XgmWeW7ygdkYCo1Tqsv/image/1b962e4e-529c-473d-bb88-f953e68e1532.png)](https://app.arcade.software/share/8HMfEvwW3pU4jxs6OApz?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**The 3 Stages of Cognis' Canvas**](https://app.arcade.software/share/8HMfEvwW3pU4jxs6OApz?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkStep 3: Ideation Once you click ‘Proceed’ in your main chat window, Cognis opens up the Canvas where you can see the ideation stage. You’ll notice that on the Canvas you get a chat window on the left side where you can prompt Cognis to make changes and switch LLMs via clicking on the Model name button at the top right of this window. You can read the brief of the idea of the article here and ensure that it meets your final content idea/goal. Once this is done, we move on to the next stage by clicking on the ‘Generate Outline’ button at the bottom right corner of the screen. Click here to watch the interactive demo Step 3, Ideation Stage [![Ideation Stage](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/9XgmWeW7ygdkYCo1Tqsv/image/99740d66-b60a-4347-a2ed-c3b76a756bab.png)](https://app.arcade.software/share/1RSms7INEWRJj3JyAJCN?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Ideation Stage** **Vet Cognis' interpretation based on your prompt at the ideation stage.**](https://app.arcade.software/share/1RSms7INEWRJj3JyAJCN?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkStep 4: Outline Once you’ve generated the outline for your article, you get access to smart edit options inside Cognis. At the outline stage Cognis, with the specialized writing canvas, displays the sections it intends to include and a brief on each section. You can edit the contents of each section using the Smart Rewrite feature or Edit the Tone and Style through quick action buttons available in the canvas itself. Cognis also allows you to re-order the sections as per your liking and add custom sections manually or via instructions. Let’s break down how to use these features. Click here to watch the interactive demo Step 4, Arcade 1, Outline Overview [![Outline Stage](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/9XgmWeW7ygdkYCo1Tqsv/image/fa55d076-a5de-40fb-a09c-6243d54335ca.png)](https://app.arcade.software/share/PA199li3VIZEhdwTLCBy?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Outline Stage** **Learn how to leverage the Outline stage to generate high quality articles/blogs.**](https://app.arcade.software/share/PA199li3VIZEhdwTLCBy?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ### 1\. Quick Editing on Cognis To edit Tone and Style quickly: 1. Each section comes with a corresponding three dot icon on the left side. 2. Click the icon to open the edit section. 3. Click on ‘Adjust Tone & Style’ to access the ‘Tone’ and ‘Style’ options. 4. Choose from the preset list of options to edit the section. 5. If you want to edit the Tone and Style of the entire outline at a go, click on the ‘Actions’ button at the bottom right side of the Canvas and follow Step 3 and 4. Using Smart Rewrite to edit the Outline: 1. Each section comes with a corresponding three dot icon on the left side. 2. Click the icon to open the edit section. 3. Click on ‘Smart Rewrite’ to access the custom prompt box. 4. Enter your prompt on how to edit the section here and click on ‘Re-write’. 5. If you want to apply custom instructions to the entire article at once, click on the ‘Actions’ button at the bottom right side of the Canvas and follow Steps 3 and 4. Click here to watch the interactive demo Step 4, Section 2, Quick Edits in CWC [![Quick Edits on Cognis' Canvas](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/9XgmWeW7ygdkYCo1Tqsv/image/94b44db7-6d6d-47db-8609-4eb1174d6469.png)](https://app.arcade.software/share/jhOsa1hn4WLW8oNmstOx?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Quick Edits on Cognis' Canvas** **Leverage quick editing features to write faster and more effectively.**](https://app.arcade.software/share/jhOsa1hn4WLW8oNmstOx?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ### 2\. Adding and Deleting Sections In the outline you also get the option to add new sections and delete existing ones. You can do this by following the steps below: 1. Scroll to the bottom of the Canvas 2. Click on the ‘+Add new section’ button 3. You can select between ‘Manual’ and ‘Add Section (Via Instruction)’ in the Action dropdown 4. If you choose Manual: - You can manually enter the Heading of the Section - Select the Tone and Style from their respective dropdowns - Enter the Content of the section manually - Click the ‘+ Add Section’ button to successfully add the new section 6. If you choose to Add a Section via instruction: - You can give Cognis a prompt on the section you’d like to be added - You can include the Tone and Style in your prompt directly - Click the ‘+ Add Section’ button to successfully add the new section - Cognis will automatically generate the new section Click here to watch the interactive demo Step 4, Section 3, Adding Sections to Your Outline [![Adding Sections to Your Outline](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/https://image.mux.com/WpuyYwJHG1gdAcfRpZqKrvZ0200K02PbZDmaB418GdQ4IU/thumbnail.webp?time=642.611)](https://app.arcade.software/share/XhD4vt7bOp6ol5E6hAAx?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Adding Sections to Your Outline** **Learn how to add new sections to your article outline.**](https://app.arcade.software/share/XhD4vt7bOp6ol5E6hAAx?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ### 3\. Re-ordering Section in the Outline To reorder the content in the outline you can: 1. Click on the four directional arrow icon to open up the re-ordering menu 2. Click and hold the arrow beside the section you want to re-order and drag it above or below 3. Click on the Tick icon at the top right of the canvas once you’re done re-ordering these sections Once you’re satisfied with the outline click on the ‘Generate Content’ button at the bottom right side of the canvas to go to the next step. Click here to watch the interactive demo Step 4, Section 4, Re-order Your Section in Cognis [![Re-Order Sections in Canvas](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/9XgmWeW7ygdkYCo1Tqsv/image/deb2729b-02e4-4b85-a3e7-bf958d1e5281.png)](https://app.arcade.software/share/wkL8rb2s9Lh35FeTV0bz?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Re-Order Sections in Canvas** **Learn how to re-order sections within Cognis' Canvas.**](https://app.arcade.software/share/wkL8rb2s9Lh35FeTV0bz?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkStep 5: Content Generation Cognis automatically generates content based on the outline in the form of a structured piece with bullet points, tables, and more. This section displays the final content giving you the same level of section-wise edits, re-ordering, and section creation options, as in the outline. Alongside this you’re able to directly edit content in the Canvas like you would in a word document. To do this: 1. You can click anywhere 2. Begin writing as you would in a word document Click here to watch the interactive demo Step 5, Section 1, Editing like a Doc File [![Generating Content and Live Edits](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/9XgmWeW7ygdkYCo1Tqsv/image/7fec7ad2-694a-4ae9-84a6-9276f44e3ccf.png)](https://app.arcade.software/share/BaEodYmnfIwhhoNDZaVV?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Generating Content and Live Edits** **Learn how to generate content and conduct live in canvas edits directly in Cognis.**](https://app.arcade.software/share/BaEodYmnfIwhhoNDZaVV?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA _Refer to the outline section walkthrough to view how to edit, re-order and add sections content._ The only key difference in the flow of the content section lies in the ‘Adding a Section’ option. In the content stage you can also select the type of the section from amongst the following five: 1. Paragraph 2. Ordered List 3. Unordered List 4. Table 5. Quote How to choose this and use it in the Content stage: - Scroll to the bottom of the Canvas - Click on the ‘+Add new section’ button - You can select between ‘Manual’ and ‘Add Section (Via Instruction)’ in the Action dropdown - If you choose Manual: 1. You can manually enter the Heading of the Section 2. **Select the type you want this section to be from the ‘Type’ dropdown** 3. Select the Tone and Style from their respective dropdowns 4. Enter the Content of the section manually 5. Click the ‘+ Add Section’ button to successfully add the new section - If you choose to Add a Section via instruction: 1. You can give Cognis a prompt on the section you’d like to be added 2. **You can include the Tone, Style, and Type in your prompt directly** 3. Click the ‘+ Add Section’ button to successfully add the new section 4. Cognis will automatically generate the new section Click here to watch the interactive demo Step 5, Section End [![Choose Block Type when Adding Section](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/https://image.mux.com/VREbPxgM90100Nfb01PCFf5jm0259NmeaWZf7LbVuisWtI4/thumbnail.webp?time=3.192)](https://app.arcade.software/share/7OkNl3gJJnAohnwAeF4g?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Choose Block Type when Adding Section** **Discover how to choose block types when adding a section in the content stage.**](https://app.arcade.software/share/7OkNl3gJJnAohnwAeF4g?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkSwitching LLMs and Using Branching on Cognis while using Google Sheets One of Cognis’ core functionalities is the ability for users to switch between LLMs seamlessly, whether at the beginning of a chat or at the message level. When you switch LLMs, a new branch is created, which initiates a new chat but stores the chat with the previous LLM in Cognis’ history. Allowing you to A/B test responses and find the one that fits your task perfectly. There are two ways to use the Multi-LLM and Branching in Cognis. ### 1\. Switching Models for the same chat for different tasks You can switch between different models for a specific set of tasks. For example, in the same chat branch, you can switch between GPT 5 for writing and Gemini 2.5 Pro for analyzing big sets of data. In this scenario, a new branch is not created. How to use this: 1. Click on the Button at the top right side of your chat window where the Model Name is written 2. Access the list of models from the drop-down and select a different model 3. Once you’ve selected the new model, continue your chat as usual with the new model You can switch back to the previous model using the same process again, or you can switch to a different model. In this scenario, you can switch between as many models as you’d like. ### 2\. Creating Branches with Different Models to A/B Test Responses You can switch between different models to A/B test and check the response quality of the respective models in the same chat window. A new branch is created each time you switch a model. Click here to watch the interactive demo Switching between different models for a Single Task [![LLM Switching on Cognis for a Single Task](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/https://image.mux.com/3jvkT01KUqMnMI6Phs02U51ehJ9k01Q00ljMJl3cqkp00Zl8/thumbnail.webp?time=0)](https://app.arcade.software/share/vbRlrX3hViIQVMCiacGy?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**LLM Switching on Cognis for a Single Task** **See how LLM Switching in the same chat thread works on Cognis**](https://app.arcade.software/share/vbRlrX3hViIQVMCiacGy?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA For example, you can use GPT 5 and Gemini 2.5 Pro to conduct the same data analysis to check which model did a better job. In this scenario, each model change leads to the creation of a new branch in the same chat window. How to use this feature: 1. Below your message to Cognis, you’ll find a four-star icon made of four circles; click it to open the model selector 2. Choose the model you want to switch to and click on the model name from the drop-down 3. The message is automatically resent to the new model, and Cognis creates a demarcation of the moment in the chat where you branched with numbered options for accessing each respective branch 4. You can click on the branch numbers to switch between different model responses 5. You can continue the chat, and beside each response sent by Cognis, a branch icon appears, which, when clicked, will take you back to the origin point of when you branched the last time during the chat Click here to watch the interactive demo A/B Testing Responses from Different Models on Cognis [![A/B Testing Responses on Cognis](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/https://image.mux.com/QPTmuORdUxwvJk00hCUaUZ00qK9w9C67KwVpU5jbya01YU/thumbnail.webp?time=12.818689555716006)](https://app.arcade.software/share/9CO7XOWgXShugvYhzOGU?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**A/B Testing Responses on Cognis** **Discover how to test different responses from different LLMs for testing out which one gives the best response**](https://app.arcade.software/share/9CO7XOWgXShugvYhzOGU?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA **Get started with Cognis Ai [here](https://www.scalifiai.com/cognis-ai) and make Google Sheets operations a cakewalk!** #### Frequently Asked Questions ###### 1\. What is Cognis? Cognis is a workflow automation platform and AI chat assistant built to simplify your worklife. It uses Agentic AI and intelligent agents to automate repetitive tasks, create complete workflows, and act as your artificial intelligence personal assistant. With features like intuitive UI, near-zero hallucination for LLMs, and support for multiple AI models, Cognis combines the best of AI and automation into a single interface. ###### 2\. What LLMs can I use in Cognis AI? You can use multiple LLMs inside Cognis AI. The platform is designed as a multi-LLM orchestration layer, so you’re not locked into one provider. Supported LLMs include OpenAI models (GPT-5, GPT-4o, GPT-4 Turbo, GPT-3.5 series, o-mini family). Anthropic Claude models (Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku) Google Gemini models (Gemini 1.5 Pro, Gemini 1.5 Flash), DeepSeek (latest V3 and V3.1 reasoning models) and more models to be added soon. ###### 3\. What makes Cognis different from other AI apps? Most AI apps or automation apps are single-function. Cognis combines AI chat, agent software, and workflow automation platform capabilities in one. Instead of juggling multiple AI programs or AI tools free/paid, you get one artificial intelligence app where agentic AI manages context, memory, and execution/ ###### 4\. Who should use Cognis? Indie Builders who hate administrative work. Startups with small teams and big dreams. Professionals needing a chatgpt AI assistant or artificial intelligence personal assistant. Developers who want to create an AI or experiment with AI programs in one place. Enterprises wanting to level up by automating workflows without the hassle of 2 AM 'nothing's working with the other' calls. ###### 5\. Does Cognis address AI bias? Yes. Cognis includes monitoring to reduce AI bias across Chat GPT-4, Chat GPT-3.5, and other best AI models. It helps ensure fair, transparent outputs when you use it as your AI assistant or automation app. #### External References 1. Cognis Ai (Beta) is live now. Explore the power of a Multi-LLM Agentic Ai platform that reduces your work time by 5x. [Read the Launch Note.](https://www.scalifiai.com/blog/cognisailaunch) Related: Walkthroughs ##### In this blog [Pre-Setup: Getting Started with Cognis](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#pre-setup:-getting-started-with-cognis) [Step 1: Installing the Content Writing Assistant Integration](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#step-1:-installing-the-content-writing-assistant-integration) [Step 2: Launch the Content Writing Assistant in Cognis](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#step-2:-launch-the-content-writing-assistant-in-cognis) [The Three Stages of Content Creation on the Canvas](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#the-three-stages-of-content-creation-on-the-canvas) [Step 3: Ideation](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#step-3:-ideation) [Step 4: Outline](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#step-4:-outline) [Step 5: Content Generation](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#step-5:-content-generation) [Switching LLMs and Using Branching on Cognis while using Google Sheets](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#switching-llms-and-using-branching-on-cognis-while-using-google-sheets) [FAQs](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs Walkthrough: How to Use Google Sheets on Cognis [Read Moreeast](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis) ###### Explore other usecases Customer Churn Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## AI/ML Cyber Security |AI Cybersecurity Solutions – Scalifi Ai Source: https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # AI in Cyber Security to Redefine the Security Posture Real-time Anomaly Detection and Authentication with AI and Multi-factor Authentication for Enhanced Cyber security [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![AI in Cyber Security to Redefine the Security Posture](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/ai_in_cyber_security_and_mfa_b3b2824058.svg) ## Problem Statement ##### Addressing Cyber Security Challenges with AI ![Addressing Cyber Security Challenges with AI](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/ai_in_cyber_security_and_mfa_b3b2824058.svg) A company was facing issues with securing sensitive data and systems against cyber threats. Traditional security measures such as passwords and firewalls are no longer sufficient to protect against sophisticated attacks, and detecting suspicious activities in real-time can be difficult for human analysts. Even if companies implement advanced security features like Multi-Factor Authentication (MFA) or Risk-Based Authentication, they all use rule-based systems. These Rule-based systems are static and generic for all cases. Hence, a lot of anomalies go unnoticed which poses some malicious security threats. To counter these threats and loopholes, the company requires a more dynamic solution combining artificial intelligence models to enhance security measures like Multi-Factor Authentication & Risk-Based Authentication. They wanted to implement a solution capable of analyzing real-time user behavior and access patterns to identify any anomalies or suspicious activity. This data was then utilized to determine whether access should be granted or denied. ## Solution ##### Redefining Cyber Security with Scalifi Ai's AI-Powered User Behavior Analysis ![Redefining Cyber Security with Scalifi Ai's AI-Powered User Behavior Analysis](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/cyber_security_2_ee32b7caa6.svg) Risk factors reduce when the outdated rule-based system is nullified with modern AI models which are dynamic and can easily learn user behavioral patterns & find anomalies. Scalifi Ai’s, AI-powered system uses machine learning algorithms to analyze user behavior and access patterns in real-time. Since AI models are dynamic and can identify hidden patterns and even learn the behavior of different users, they can increase the efficiency of Multi-Factor Authentication and Risk-Based Authentication to provide an enhanced security posture. The Scalifi Ai’s AI engines can be integrated with your Identity Providers (IDPs), firewalls, gateways, etc. to provide better accuracy for determining risk factors for Adaptive authentication. We offer hybrid deployment options where our AI engine runs in your cloud environment so that all the data remains within your VPC or Virtual Private Cloud to ensure the highest level of privacy. Even the entire training & retraining pipelines of our AI engines or AI models can be easily done in your cloud environment without any human intervention. ## Benefits ###### lock\_personEradicate the Loopholes of Risk-Based Authentication The AI-powered system offers real-time analysis of user behavior and access patterns which is not possible in the case of rule-based systems traditionally used in risk-based & multi-factor authentication. AI models when integrated with risk-based authentication enable quick detection of anomalies and suspicious activities which makes the security posture more effective. ###### sync\_lockReal-Time Dynamic Protection AI models can be trained periodically with updated user data and they can detect minute patterns in user behavior. As user behavior can be prone to changes with time, so does the security threats increase if you are using the same set of rules to determine risk, but AI models are periodically trained and you can select how often they need to be trained. This dynamic approach of AI models ensures better threat detection in specific use cases and leaves no margin of error. ###### dashboard\_customizeEasy Integration & Secure Deployment Our AI Engines can be easily integrated with your existing Identity Providers (IDPs) and make your existing security systems like risk-based authentication more effective without any margin of error. We provide multiple deployment models like On-Premise, cloud, and Hybrid. In a hybrid deployment, the AI models are deployed in your Virtual Private Cloud to keep your privacy secure and also, the re-training of AI models happens in your VPC without the need of any human intervention. ###### Related Blogs Natural Language Processing: How Neural Word Embeddings Enable Machines to Understand Text [Read moreeast](https://scalifiai-founder.medium.com/how-do-machines-understand-text-via-natural-language-processing-nlp-41aeb853ef52?source=friends_link&sk=b38399d604862bab7c3b2d12ee601dee) In-Depth Study of Large Language Models (LLM) [Read moreeast](https://www.scalifiai.com/blog/what-is-large-language-model-llm) ###### Explore other usecases Customer Churn Prediction [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) Credit Card Fraud Detection [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) Revenue Prediction [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Demystifying LLMs: Architecture to Real-world Influence | Scalifi Ai Source: https://www.scalifiai.com/blog/what-is-large-language-model-llm Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # In-Depth Study of Large Language Models (LLM) 24 October 2025\|7 min read ## insert\_linkIntroduction to Large Language Models ### What is a Large Language Model (LLM)? Large language models (LLMs) are a huge step forward in AI, particularly in comprehending and creating human language. These language models, developed using advanced machine learning techniques, are designed to mimic human linguistic abilities. Their main role isn't just processing text; they excel at understanding language nuances for coherent text generation. LLM AI aims to bridge human communication and computational understanding, creating a more intuitive interaction with machines. ### Core Components of LLMs The LLM architecture is built upon several key components, each playing a vital role in their functionality. At the core are the **neural networks**, which are computational frameworks designed to mimic the human brain's functioning. In neural networks, nodes work together to process and transmit information much like neurons in the human brain. Advanced **algorithms** help language models learn and understand language, allowing them to decode and create sentences. The foundation of these models is the **training datasets**, extensive compilations of text that provide the necessary linguistic input. These diverse training datasets offer language models a rich linguistic environment to learn from, incorporating different styles and contexts. ![Components of Large Language Models - Scalifi Ai](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2Fllm_article_1_1_bda474783e.png&w=3840&q=75) _Components of Large Language Models - Scalifi Ai_ ( _[Source](https://www.assemblyai.com/blog/fine-tuning-transformers-for-nlp/)_) ## insert\_linkLLM Architecture ### Neural Network Layers and Functionality An LLM's neural network resembles a complex labyrinth, with multiple layers that each contribute to understanding different language aspects. Each layer consists of nodes, which conduct specific computational tasks. As information flows through these layers, the model processes language elements at increasing levels of complexity, from basic syntax to sophisticated contextual interpretations. ### The Transformer Model: A Paradigm Shift Transformers have revolutionized LLMs' architecture, providing a more dynamic way of processing language. Unlike traditional language models that process text linearly, transformers can process different parts of the input simultaneously. This parallel processing enables a more comprehensive understanding of text, allowing the model to capture nuanced meanings and contextual relationships more effectively. ## insert\_linkThe Training Process of LLMs ### Preparing the Data The training process begins with meticulously preparing a dataset that is both extensive and representative. This stage involves collecting text data, removing unnecessary or sensitive information, and organizing it for effective learning. This preparation is crucial as it sets the foundation for the model's learning journey. ### The Forward Pass: Input Processing During the forward pass, the model processes input data, extracting and building on different language features in its layers. This step is where the model makes initial predictions based on its current understanding and training. ### Backward Pass: Learning from Errors Backpropagation, or the backward pass, is where the model undergoes refinement. Here, it compares its predictions against actual outcomes, identifying errors. The model then adjusts its parameters, learning from these discrepancies to improve its language processing capabilities. ### Optimization: Refining the Model Optimization algorithms and loss functions are employed to fine-tune the model's performance. These tools find errors in the model and help make changes to improve its accuracy and efficiency over time. ![Neural Network Backpropagation - Scalifi Ai](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2Fllm_article_1_2_0941564d27.gif&w=3840&q=75) _Neural Network - Backpropagation_ ( _[Source](https://machinelearningknowledge.ai/wp-content/uploads/2019/10/Backpropagation.gif)_) ## insert\_linkScaling Up LLM AI ### Managing Billions of Parameters A big challenge in LLM machine learning is handling the many parameters, which can be in the billions. To help the model learn language effectively, advanced strategies are needed to handle the data volume without overwhelming it. ### The Computational Challenge The computational demands for training and operating LLMs are substantial. It requires powerful hardware capable of handling extensive calculations and processing large datasets. Creating LLM AI is tough, pushing tech boundaries and needing ongoing hardware and software enhancements. ![LLM Learning Phases - Scalifi Ai](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2Fllm_article_1_3_42a3351ee1.png&w=3840&q=75) _LLM Learning Phases_ ( _[Source](https://www.researchgate.net/figure/Overview-of-LLM-training-process-LLMs-learn-from-more-focused-inputs-at-each-stage-of_fig1_373642018)_) ## insert\_linkThe Future of LLM AI: Potential and Perspectives ### insert\_linkAnticipated Technological Advancements #### Improvements in Model Efficiency The ongoing evolution of Large Language Models (LLMs) is heavily focused on enhancing their computational efficiency. This development is crucial, as it directly impacts the accessibility and resource demands of these powerful tools. Advancements aim to reduce the computational load, making LLMs more feasible for wider use, even in resource-limited environments. Methods like model pruning remove unnecessary parameters. Knowledge distillation trains a smaller model to perform like a larger one. These techniques are becoming popular. These techniques are becoming popular. These methods enable a future where we can use LLMs' amazing abilities in a more sustainable and widespread way. #### Advances in Model Architecture In the realm of architectural innovations, LLMs are poised for significant transformations. In the future, neural networks may have improved designs for better information processing and language tasks. There are two possible innovations. One is neural networks that change their structure based on the task. The other is combining different types of neural networks to use their strengths. These improvements will enhance LLMs' performance and create new opportunities for their use in diverse and challenging situations. ![LLM AI feedback loop - Scalifi Ai](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2Fllm_article_1_4_ac05a0ffa0.png&w=3840&q=75) _LLM AI feedback loop - Scalifi Ai_ ( _[Source](https://blog.southparkcommons.com/the-near-future-of-ai-is-action-driven/)_) ### insert\_linkEthical and Societal Considerations #### Addressing Biases As LLMs become more common, it's important to fix the biases in their training data. Future models are expected to adopt more sophisticated methods to identify and mitigate biases, ensuring fairer and more representative outputs. This could involve using various training data. It could also involve creating algorithms that identify and correct biased patterns. Additionally, it could involve incorporating a wider range of human perspectives into training models. The aim is to make LLMs that grasp language and also represent the wide range of human diversity and ideas. #### Privacy and Data Security The escalation in the capabilities of LLMs brings with it heightened concerns for privacy and data security. Ensuring that these language models respect user privacy and maintain data security is a growing priority. This may involve developing new methods to safeguard information. It also involves securely managing data and utilizing techniques such as federated learning. Federated learning allows models to be trained on multiple devices while ensuring data privacy. As LLMs continue to evolve, so too must the frameworks and technologies designed to safeguard user information. ### insert\_linkIndustry Integration and Impact #### Broader Industry Adoption The potential integration of LLMs across various industries, from healthcare to finance, is set to revolutionize these sectors. LLMs can help diagnose patients and plan treatments in healthcare. They can also improve predictive analysis and personalized customer services in finance. Using LLMs in various fields will lead to improved outcomes and efficiency. Additionally, it will enable the development of new services and solutions that were previously unattainable. #### Collaborative AI Looking forward, the integration of LLMs with other AI technologies could lead to more comprehensive and versatile AI solutions. This collaboration could manifest in systems where LLMs work alongside visual recognition technologies or decision-making algorithms, creating a more holistic AI experience. Such collaborative AI systems could significantly enhance capabilities in areas like autonomous vehicles, personalized education, and intelligent virtual assistants. ## insert\_linkReal-World Applications and Impact ### Transforming Industries The practical applications of LLMs are as diverse as they are impactful. In healthcare, LLMs assist in analyzing patient data, contributing to more accurate diagnoses and personalized treatment plans. They also play a crucial role in research, helping to sift through vast amounts of medical literature and data. In finance, LLMs like GPT-4 are used for various tasks, such as automating customer service and analyzing financial data. AI's data processing and insights are changing how financial institutions work and connect with clients. ### Education and Personalized Learning In education, LLMs offer the potential for personalized learning experiences. They can adapt to individual learning styles, provide tailored educational content, and even assist in evaluating student performance. This capability is revolutionizing the educational landscape, making learning more accessible and tailored to individual needs. ### Customer Service and E-Commerce LLMs automate responses and provide 24/7 support, improving customer experience and cutting operational expenses. In online shopping, they use their knowledge of language and consumer behavior to suggest products and tailor marketing to individuals. This helps increase sales and customer interaction. ### Creative Industries In creative industries, LLMs are being used for content creation, from writing articles to generating scripts and marketing copy. Their ability to produce coherent, contextually appropriate text makes them valuable tools for writers, marketers, and content creators. ![Large Language Model usecases - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/llm_usecases_scalifiai_3f00b6b74e.svg) _Large Language Model usecases - Scalifi Ai_ ### Ethical Considerations and the Road Ahead As LLMs continue to integrate into various facets of life, addressing ethical concerns like data privacy, security, and biases in AI becomes increasingly important. The future of LLMs will likely focus not only on technological advancements but also on developing frameworks and practices that ensure ethical and responsible use of AI. ## insert\_linkConclusion ### Societal Impact LLMs have already begun to transform the way we communicate with machines and each other, a trend that is only expected to continue. They are reshaping education, where personalized learning environments are becoming more prevalent, and in the workforce, where they augment job roles and create new opportunities. ### Technological Reflection The current state of LLM AI technology is a balance of remarkable achievements and ongoing challenges. While they have demonstrated extraordinary capabilities in language understanding and generation, issues like bias, privacy, and resource demands remain significant challenges. ### Closing Thoughts The development of LLMs stands at a crossroads of innovation and responsibility. As we continue to advance these technologies, it is imperative that we do so with a keen awareness of their ethical and societal implications. Continued research and development are essential, not only to push the boundaries of what these models can achieve but also to ensure they are developed in a way that benefits society as a whole. #### External References 1. For an in-depth understanding of GPT-4’s capabilities and its applications across different industries, [OpenAI’s official product page](https://openai.com/gpt-4) offers detailed information. 2. To learn more about Google's Bard and its integration with web data, [Google AI Blog](https://blog.google/products/bard/google-bard-new-features-update-july-2023/) provides insights and updates. 3. For information on Meta's LLaMA-2 and the BLOOM model, including their unique features and contributions, Hugging Face’s Model Hub is a comprehensive resource. [Ref: LlaMa-2](https://huggingface.co/docs/transformers/main/model_doc/llama2) [Ref2: BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom). 4. For tutorials on how to use and implement LLMs like GPT-4, [OpenAI’s documentation](https://platform.openai.com/docs/introduction) provides comprehensive guides and API documentation. Related: LLM AI in Education AI in Finance AI in Healthcare AI Ethics ##### In this blog [Introduction to Large Language Models](https://www.scalifiai.com/blog/what-is-large-language-model-llm#introduction-to-large-language-models) [LLM Architecture](https://www.scalifiai.com/blog/what-is-large-language-model-llm#llm-architecture) [The Training Process of LLMs](https://www.scalifiai.com/blog/what-is-large-language-model-llm#the-training-process-of-llms) [Scaling Up LLM AI](https://www.scalifiai.com/blog/what-is-large-language-model-llm#scaling-up-llm-ai) [The Future of LLM AI: Potential and Perspectives](https://www.scalifiai.com/blog/what-is-large-language-model-llm#the-future-of-llm-ai:-potential-and-perspectives) [\- Anticipated Technological Advancements](https://www.scalifiai.com/blog/what-is-large-language-model-llm#anticipated-technological-advancements) [\- Ethical and Societal Considerations](https://www.scalifiai.com/blog/what-is-large-language-model-llm#ethical-and-societal-considerations) [\- Industry Integration and Impact](https://www.scalifiai.com/blog/what-is-large-language-model-llm#industry-integration-and-impact) [Real-World Applications and Impact](https://www.scalifiai.com/blog/what-is-large-language-model-llm#real-world-applications-and-impact) [Conclusion](https://www.scalifiai.com/blog/what-is-large-language-model-llm#conclusion) [FAQs](https://www.scalifiai.com/blog/what-is-large-language-model-llm#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/what-is-large-language-model-llm#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs Understanding Natural Language Processing (NLP) Essentials [Read moreeast](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp) The Beginner's Guide to AI Models: Understanding the Basics [Read moreeast](https://www.scalifiai.com/blog/what-is-an-ai-model) ###### Explore other usecases AI in Cyber Security to Redefine the Security Posture [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Customer Churn Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) Revenue Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) --- --- ## Unified Model Registry Across Frameworks | Scalifi Ai Source: https://www.scalifiai.com/model-catalog/features/unified-model-registry Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Unified Model Registry AcrossFrameworks Use a centralized library to store and manage your AI models built and trained using different frameworks like TensorFlow, PyTorch, scikit-learn, etc. This unified approach facilitates collaboration, streamlines model updates, and simplifies the maintenance and deployment processes, leading to increased efficiency and reduced time to market for AI solutions. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Model Catalog Unified Registry - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/model_catalog_unified_registry_scalifi_ai_ec34ff3fce.svg) ![easy-collaboration-model-catalog-service.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/easy_collaboration_model_catalog_service_2acd35cf69.svg) ##### Easy Collaboration Make it easier for data scientists, engineers, and other stakeholders to collaborate and work with machine learning models using a central model library regardless of the framework used. ![streamlined-model-updates-model-catalog-service.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/streamlined_model_updates_model_catalog_service_8ffee34c99.svg) ##### Streamlined Model Updates Update metadata, tags, manage versions and its variations using a flexible model update flow at any given point in time. ![integrations-and-scalability-model-catalog-service.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/integrations_and_scalability_model_catalog_service_a9aa9cca91.svg) ##### Integrations and Scalability Integrate different workflows like deployment pipelines and CI/CD systems to seamlessly deploy the master AI model into production. This also offers high availability and scalability to handle and deploy large volumes models and associated metadata. ![Compatible Across Platforms Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/compatible_across_platforms_model_catalog_service_44dae0eb60.svg) ### Compatibility Across Frameworks Create models using various machine learning frameworks like TensorFlow, PyTorch, scikit-learn, etc, enabling interoperability and flexibility in model development, management and deployment. This ensures flexibility in choosing the most suitable framework for a specific task without requiring model re-development, saving time and resources during deployment. Along with this, it also involves documenting the frameworks a model is compatible with, along with any specific versions of dependencies. ![Simplified Maintenance And Deployment Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/simplified_maintenance_and_deployment_model_catalog_service_d0a7009a08.svg) ### Simplified Maintenance and Deployment Process The centralized platform helps in the maintenance and deployment lifecycle of a model. Also use the built-in features to verify the model before the production release. By storing metadata and automating workflows, it reduces manual tasks and ensures consistent, efficient transitions from development to production. This helps the team to deploy models faster and with fewer errors and reduces development time and allows data scientists to focus on higher-level tasks. ![Improved Reusability And Metadata Management Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/improved_reusability_and_metadata_management_model_catalog_service_f9f6dc86db.svg) ### Improved Re-usability and Metadata Management Easily reuse the model built on various frameworks to reduce redundancy and accelerate development. Store and manage metadata about each model, such as its versions, variations and its configuration, tags etc. This metadata is essential for understanding and using models effectively. This also fosters better collaboration and reduces redundancy by ensuring models are documented comprehensively and can be effectively reused across different projects. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Understand Quota | Billing & Usage Guide Source: https://www.scalifiai.com/docs/billing-and-usage/quota Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # Quota Navigate the Quota section in Scalifi Ai's Billing and Usage Center, where you can manage and customize your subscription plans efficiently. This segment offers a streamlined overview of your quota management tasks, including viewing active and past quotas, initiating new customized quota requests, and closing existing ones. It is designed as your one-stop destination for modifying and optimizing your organization's service and module allocations, ensuring a tailored and effective use of the Scalifi Ai platform. Engage with support, adjust your services, and maintain control over your subscription details, all through an intuitive and integrated interface. Allowed operations that users can perform on the platform are: 1. What is a Quota Request? 2. View Quota 3. Create Quota Request 4. Close Quota request ## insert\_linkWhat is a Quota Request? A quota request is essentially a user-initiated thread to customize their subscription plan according to specific needs. Users can select and specify the desired modules and services, and the quantity or extent of each, to tailor their usage. Once submitted, this request is reviewed and adjusted in collaboration with a support agent through a dedicated chat interface, ensuring that the final quota aligns perfectly with the organization's requirements and preferences. It's a flexible way for users to adapt their access and control costs based on their unique needs and usage patterns. ## insert\_linkView Quota 1. To view the submitted quota details, go to the **Quota** tab and click on the quota you want to see. ![Quota List Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_quota_78b473457c.svg) 3. Next, you will see the per quota page where all the details like quota status, module-wise quota and more information is displayed. ![Quota Details Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_per_quota_details_6c58d27503.svg) 5. To upgrade or change your current quota, click on the **Upgrade Quota** button. ![Upgrade Quota Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_upgrade_quota_2344204044.svg) ## insert\_linkCreate Quota Request 01. To create a quota request, go to the **Quota Request** tab and click on **Create Quota**. ![Create Quota Request Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_request_quota_8ed471a19a.svg) 03. Select the modules (for example: IAM, Model Building) from the drop-down list as per your requirements. ![Configure Create Quota Request Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_configure_quota_859ea60c24.svg) 05. Now, choose the sub-modules, like **Policy Management**, from the tab and enter the quantity of the actions based on your requirement. If any action is not required, you can leave that field empty. ![Configure Sub Module Action Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_sub_module_action_config_ab8ed7104a.svg) 07. You can also customize rate limiting for high usage APIs. To configure rate limiting, click on the **Configure** button. ![Configure Quota Rate Limit Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_configure_quota_rate_limit_0ffed0543d.svg) 09. Now enter the quantity of API calls and select the duration unit. ![Configure Rate Limit Billing And Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_configure_rate_limit_8d8092f155.svg) 11. After completing the configuration, click on **Verify Quota**. If there are any errors while entering the quantity values, you will see an error message. Resolve the errors and re-click on **Verify Quota**. ![Quota Request Error Billing And Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_quota_request_error_14b5f5e61b.svg) 13. After resolving the errors (if any), click on the **Create Quota** button. ![Review Quota And Create Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_create_quota_4e1b213de3.svg) 15. Select the valid date range and click on **Apply** and then click on **Create**. ![Select Quota Date Range Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_create_quota_date_range_47a25f4d27.svg) 17. Once you have submitted the quota request, you will be redirected to the quota request page. Here, you will see the quota request information like request type, request status and request ID. 18. You can also send our support team a message if you have any query regarding the quota request using the dedicated chat panel. All the updates regarding quota request will be reflected here and via emails including status changes in quota request and messages from support team. ![Per Quota Request Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_per_quota_request_14e112372e.svg) ## insert\_linkClose Quota request 1. There are two ways to close a quota request. 2. **From Quota Requests section**: Go to **Quota Request** tab, select the quota you want to close → go to Actions → Close request ![Close Quota Request Billing and Usage - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_close_quota_request_e24ac7d382.svg) 4. **From Quota Request page**: From the per quota request page, select Action → Close request. ![Close Quota Request From Per Quota Request Page - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/billing_and_usage_close_per_quota_request_5011c6c5ac.svg) Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [PreviouswestUsage](https://www.scalifiai.com/docs/billing-and-usage/usage) ##### Content [Overview](https://www.scalifiai.com/docs/billing-and-usage/quota#overview) [What is a Quota Request?](https://www.scalifiai.com/docs/billing-and-usage/quota#what-is-a-quota-request) [View Quota](https://www.scalifiai.com/docs/billing-and-usage/quota#view-quota) [Create Quota Request](https://www.scalifiai.com/docs/billing-and-usage/quota#create-quota-request) [Close Quota request](https://www.scalifiai.com/docs/billing-and-usage/quota#close-quota-request) * * * hide\_imageHide Images --- --- ## Scalifi Ai Model Building | AI ML Model No-Code Low-Code Source: https://www.scalifiai.com/ai-ml-model-building Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Empower Your AI Journeyto build AI Model with Advanced, No Code platform Leverage the advanced capabilities of AI to streamline the process and build machine learning (ML) models using different frameworks like tensorflow, scikit learn, pytorch etc, saving time and resources. This intuitive, no-code platform enhances productivity, ensuring your team can swiftly build refined ML models, leading to faster innovation and problem-solving. The right tools are always at your fingertips, to accelerate your journey in AI development. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![AI ML no code model building using tensorflow, scikit learn and pytorch - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_banner_778c5a245f.svg) Take a look at it inaction Transform Your ML Workflow: Inside Look at Scalifi Ai's Revolutionary No-Code Model Building! - YouTube Tap to unmute [Transform Your ML Workflow: Inside Look at Scalifi Ai's Revolutionary No-Code Model Building!](https://www.youtube.com/watch?v=i_VeLsYS0qM) [Scalifi Ai](https://www.youtube.com/channel/UCmLSxyUW_VTYGjcxwdt_Ujw) ![thumbnail-image](https://yt3.ggpht.com/yAW8yEVm5HDylmNENPzROOnOUYlUPQxIk5_KkeItgMj6CEwu0RQU1MNOLkMnvs1bTzsW8QMrUA=s68-c-k-c0x00ffffff-no-rj) Scalifi Ai28 subscribers [Watch on](https://www.youtube.com/watch?v=i_VeLsYS0qM) ![Construct Ai Model Illustration - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/construct_Ai_Model_503a1f8f54.svg) ### Effortlessly Build Intelligent AI Model Model building service transforms the complex task of model development into a streamlined, intuitive process. With our no-code canvas, users can drag and drop elements to build sophisticated ML models, making the process accessible to all skill levels. This feature saves time and resources, empowering your team to focus on innovation rather than getting bogged down in coding complexities. ![Artificial Intelligence modeling using framework - TensorFlow, PyTorch, Scikit Learn - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/diversify_Ai_Capabilities_81194220f4.svg) ### Diversify Your AI Capabilities Building an AI Model is not just about ease of use; it's about power and flexibility. With support for multiple frameworks like TensorFlow, PyTorch, and Scikit Learn, Model-building empowers you to work on a variety of projects without switching platforms. This multi-framework support enhances productivity, ensuring your team can tackle any AI challenge that comes their way. ![Advance AI error detection - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/error_Precision_07e85138c3.svg) ### Achieve Precision with Advanced Error Detection While building an AI Model, advanced error detection and resolution feature ensures that your AI models are not only built quickly, but built in the right order. By identifying potential errors early in the model development process, the ml no code canvas saves you from future headaches and accelerates your path to deployment, thereby enhancing customer satisfaction through reliable and efficient artificial intelligence solutions. ![Reduce model building time using artificial intelligence (AI) - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/accelerate_Ai_Dev_Cycle_292c9c9a78.svg) ### Accelerate AI Model Building Cycle The Quick Build feature is a game-changer for rapid AI modeling and prototyping. It allows users to quickly validate model designs and identify any errors without the need for extensive hardware resources. This leads to a significant reduction in development time, enabling teams to iterate and refine AI models with unprecedented speed and efficiency. ![Export ML models using no code - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/flexible_Deployment_7d05ac3dfe.svg) ### Flexible Deployment Across Platforms The versatile ML model export options allows you to easily transition from model development to deployment. Whether it’s exporting as framework-specific code or integrating with various data connectors, the no code ML platform ensures that your AI models are ready for deployment in any environment, enhancing your operational flexibility and readiness. ![Build complex AI models using model jobs - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/solution_Ai_Challenge_1dd561c4bd.svg) ### Tailored Solutions for Every AI Challenge Model Job feature provides tailored solutions to build ml models of varying sizes and complexities. From building and testing large-scale models to specialized jobs for resource-intensive tasks, MBS adapts to your specific requirements, ensuring optimal performance and cost-efficiency across all your AI projects. ### What makes Scalifi Ai's model building unique dashboard\_customize #### Diverse Framework Support The AI modeling platform supports a variety of frameworks like TensorFlow, PyTorch, and Scikit-Learn, enabling flexible and adaptable AI model development tailored to your project's needs. draw #### Versatile Model Creation Build diverse AI models using deep learning and traditional machine learning methods which fosters innovation across various AI applications. bug\_report #### Advanced Error Resolution With built-in error detection and resolution, the no code model building platform ensures the accuracy and reliability of your AI models to reduce development time and enhance model quality. code\_off #### Intuitive No-Code Canvas ML model building provides user-friendly, drag-and-drop interface to make AI modeling accessible and efficient, even for those with minimal coding experience. construction #### Quick Build Feature Accelerate your artificial intelligence model development with Quick Build feature to allow for fast AI model prototyping and immediate feedback on model designs. rocket\_launch #### Scalable Model Jobs Machine Learning (ML) modeling platform offers scalable solutions for model jobs of varying sizes and complexities, ensuring efficient resource utilization and optimal performance. ## Model Building ServiceFeatures layers ##### **Flexible Layer Configuration** Model building service provides a rich set of customizable layers, allowing you to tailor the architecture of your AI models to fit specific requirements and complexities. [Learn Moreeast](https://www.scalifiai.com/ai-ml-model-building/features/rapid-prototyping-ai-model) precision\_manufacturing ##### **Automated Model Assembly** Leverage the power of no-code AI to automate the assembly of machine learning models, enhancing efficiency and reducing manual effort. [Learn Moreeast](https://www.scalifiai.com/ai-ml-model-building/features/quick-build-advantage-advanced-analytics) tips\_and\_updates ##### **Instant Model Insights** Gain immediate insights with no code machine learning tool’s real-time analytics feature which enables it to track model performance and make data-driven decisions on the fly. [Learn Moreeast](https://www.scalifiai.com/ai-ml-model-building/features/quick-build-advantage-advanced-analytics) auto\_fix\_high ##### **Smart Resource Allocation** The AI modeling tool intelligently allocates computational resources based on model complexity and size, ensuring cost-effective and high-performance model building experiences. [Learn Moreeast](https://www.scalifiai.com/ai-ml-model-building/features/quick-build-advantage-advanced-analytics) add\_business ##### **Access to Pre-Built Models** Utilize Scalifi Ai's Marketplace to access a variety of pre-built models, accelerating your project’s time-to-market and fostering collaborative development. [Learn Moreeast](https://www.scalifiai.com/feature-in-progress) work\_history ##### **Efficient Model Training Scheduling** No code model training allows for advanced scheduling of model jobs which enables efficient management of resources and timing for training and testing, leading to optimized model development cycles. [Learn Moreeast](https://www.scalifiai.com/ai-ml-model-building/features/efficient-ai-modeling-jobs) #### Frequently Asked Questions ![frequently asked questions](https://www.scalifiai.com/_next/static/media/faqCircle.3d68e398.svg) ###### What is an AI Modeling Service? The AI Model Building Service is a revolutionary no-code platform designed for efficient and accessible AI model development. By simplifying complex processes involved in machine learning, the no-code machine learning opens the door to advanced AI for a broad audience, from seasoned data scientists to AI novices. Its intuitive interface and powerful tools enable quick AI model building and testing, making it a cornerstone for innovative AI/ML solutions. ###### Who can benefit from using No-Code AI Modeling tools? AI Model is a versatile tool beneficial for a wide array of users, including data scientists, AI practitioners, tech enthusiasts, and beginners in AI. It's especially useful for professionals seeking to streamline model building without deep programming knowledge, educational institutions teaching AI concepts, and businesses aiming to integrate AI solutions without a steep learning curve. ###### What frameworks does no-code machine learning (ML) platform support? The no-code ML platform stands out for its compatibility with leading AI frameworks, such as TensorFlow, PyTorch, and Scikit Learn. This multi-framework support caters to diverse project requirements, enabling users to work on a broad spectrum of artificial intelligence projects, from simple machine learning tasks to complex deep learning algorithms, under one unified platform. ###### How does no-code machine learning simplify AI modeling? - **No-Code Canvas:** AI modeling features a user-friendly no-code canvas, allowing for the easy construction of machine learning models. This intuitive interface facilitates drag-and-drop model building, eliminating the complexity of traditional coding. - **Error Detection and Mitigation:** Advanced error detection and mitigation capabilities within the ML no-code model build ensure a streamlined, error-free model development process. This feature significantly reduces the time spent troubleshooting and refining models. - **Quick Build Feature:** The Quick Build feature in no-code AI/ML platform accelerates the model prototyping process. It provides rapid feedback, enabling swift iteration and refinement of models, thereby enhancing the overall speed and efficiency of artificial intelligence model building. ###### Can I use the ML no-code model building platform for deep learning projects? Yes, the ML no-code modeling platform is adept at handling both deep learning and traditional machine learning projects. It provides a versatile environment where users can experiment with various model architectures, including neural networks and more, making it a go-to tool for cutting-edge deep learning applications and research. ###### What are Model Jobs in ML model building? Model Jobs in machine learning model building represent a core feature, designed to handle a variety of specific tasks within the AI model development process. These jobs cater to distinct aspects of model building, offering tailored solutions for diverse project needs. The first aspect of Model Jobs involves various deployment options. Users can choose from a range of deployment scenarios, including cloud-based environments, private virtual machines, or shared resources. This flexibility allows for optimal resource allocation based on the model's requirements and the user's preferences. In addition to deployment flexibility, ML Model Jobs also return valuable statistical information about the model. This includes detailed insights into model performance, parameter efficiency, and other key metrics. These statistics are crucial for understanding the model's behavior and making informed decisions to enhance its effectiveness and efficiency. ###### How does the no-code AI ensure the accuracy of model building? The no-code AI model building prioritizes model accuracy through its advanced error detection capabilities. By identifying and resolving potential issues early in the development process, it ensures the creation of reliable and efficient AI models. This proactive approach to error management not only enhances model quality but also streamlines the development cycle. ###### Is the no-code artificial intelligence (AI) suitable for large-scale AI projects? The no-code AI is adept at handling large-scale artificial intelligence projects with its array of specialized Model Jobs, designed for various project scales and complexities. The platform offers diverse virtual machine (VM) options, such as shared, private, or personal cloud VMs, catering to specific requirements of substantial AI tasks. This flexibility ensures scalable and efficient performance for expansive projects. Moreover, the no-code AI platform provides detailed statistical feedback on model performance, offering valuable insights for optimizing large-scale models. This data is instrumental in refining model accuracy and efficiency. Additionally, the versatility extends to its model export capabilities, allowing users to export AI/ML models in multiple formats for seamless integration across different platforms. This feature is particularly beneficial for large-scale projects that require adaptability and robustness in diverse application environments. ###### How does no-code ML model building handle data integration? ML modeling excels in data integration, offering seamless connectivity with various data sources and platforms. This integration ensures a smooth data flow essential for training and refining AI models, making MBS a highly adaptable and efficient tool for organizations looking to integrate artificial intelligence into their existing tech ecosystems. ###### Can I prototype AI/ML models quickly with the no-code platform? Yes, the Quick Build feature of the artificial intelligence no-code platform is a standout for rapid AI/ML model prototyping. It enables users to quickly test and iterate model designs, providing immediate feedback. This accelerates the development process, allowing for swift adjustments and enhancements, crucial in fast-paced AI development environments. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Walkthrough: How to Use Google Sheets on Cognis Source: https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Walkthrough: How to Use Google Sheets on Cognis 9 January 2026\|5 min read Welcome to the walkthrough of using Google Sheets on Cognis. If you’re new here, Cognis is a Multi-LLM Agentic Ai System that allows you to leverage AI intelligence to execute tasks through clicks and prompts. It doubles down as a centralised workspace for all your work-related actions. Visit [Cognis Ai](https://www.scalifiai.com/cognis-ai) to know more about Cognis. In this walkthrough, we’re going to break down the step-by-step process of: 1. Installing the Google Sheets integration, 2. Setting it up, 3. And how to launch and use it. So let’s dive straight into it. ## insert\_linkPre-Setup: Getting Started with Cognis So in the first step, you fire up Cognis by either registering or logging in via [this link](https://www.scalifiai.com/cognis-ai). To register, please follow these steps: 1. Fill in your details 2. Log in after your registration is complete 3. Verify your email address 4. Access Cognis 5. Click on Setup Instantly to Configure the best LLMs in a single click You can begin with the free tier of Cognis without having to submit any kind of payment information. Click here to watch the interactive demo How to Get Started with Cognis (Pre-Setup) [![How to Get Started with Cognis](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/O0w7xadx51PA4M9EMAgx/image/e4af0664-aa4f-4c57-a512-c46cb9712698.png)](https://app.arcade.software/share/O0w7xadx51PA4M9EMAgx?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**How to Get Started with Cognis** **Learn how to register for Cognis Ai and set it up.**](https://app.arcade.software/share/O0w7xadx51PA4M9EMAgx?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkStep 1: Installing and Setting Up the Google Sheets Integration Once you land on Cognis, you can follow the steps below to install the Content Writing Assistant: 01. On the lower left side, click on the ‘Explore Integrations’ tab 02. On the integrations page, the second integration is Google Sheets 03. Click on the ‘install’ button on the Google Sheets Integration to complete the installation 04. After it’s installed successfully, click on the ‘Settings’ button at the bottom right side 05. Click on ‘My Connected Apps,’ and once the page opens, you’ll find ‘User Setup Pending’ on the Google Sheet card 06. Then click on the gear symbol on the Google Sheet card to begin the setup 07. Click on ‘User Setup’ and in the next pop-up screen, click on ‘Attach Connector’ 08. Now, on the Configure Data Source Page, click on ‘Add New Data Source’ 09. Choose ‘Google Sheets’ and the ‘Next’ button on the bottom left 10. To use the Connector, you’ll need to configure your Third Party Account by clicking on the ‘Configure Third Party Account’ button 11. In the ‘Provider’ page, choose ‘Google’ from the dropdown 12. Then click the ‘Link Account’ Button 13. Authenticate your chosen account through Google 14. Once done, you’ll be taken back to the Configure page. 15. In case you see a cross symbol beside the email address of your Third Party Account, click ‘Re-Authorize’ to complete authorization 16. Enter the name for the connector in the box at the top (Example name: My Sheets Account) 17. Check the box next to your email ID 18. Then click ‘Submit’ at the bottom left corner 19. On the ‘Configured Data Sources’ Screen, click on the Google Sheet card 20. Then click the button ‘Attach Connector’ 21. The Google Sheet card on the My Connected page will show “User Setup Completed’ Click here to watch the interactive demo Step 1: Google Sheet Installation and Set Up [![Google Sheet Installation and Set Up](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/YHSWtn5Y2ophhpjnGriA/image/470f8801-4d7d-4e9d-94c8-bc90df5d9afd.png)](https://app.arcade.software/share/YHSWtn5Y2ophhpjnGriA?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Google Sheet Installation and Set Up** **Discover how to seamlessly install and set up Google Sheets with authorized access, to automate related tasks on Cognis.**](https://app.arcade.software/share/YHSWtn5Y2ophhpjnGriA?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkStep 2: Launching the Google Sheet Integration on Cognis To launch the Google Sheet integration on Cognis, you can use a couple of methods as follows: - **Direct Prompt:** You can directly prompt Cognis with a related prompt that mentions Google Sheets. For example, I want to analyze my last quarter sales report sheet/Google Sheet - **Using the Direct to Button:** The direct to button located on top of the right-hand side of the chatbox helps in guiding Cognis to open your integration in case it’s missed. Follow the steps below: 1. Click on the ‘Direct to’ button located on the right side, just above the chatbox 2. Choose ‘Google Sheets’ 3. Then, enter your prompt to directly send the prompt to the Google Sheets integration via Cognis Once you send across your first prompt, Cognis responds by asking you to select a Sheet. Click on the ‘Select Sheet’ button to open the sheet selector and pick your sheet. Then Cognis will analyze the sheet and ask you to select a subsheet. Click on the subsheet you want to access from the options presented by Cognis. Click here to watch the interactive demo Step 2: How to launch Google Sheets on Cognis [![Launching Google Sheets on Cognis](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/extension-uploads/t3Pu5PoACBztGqrVZ6uF/image/33bf4d4e-3312-495e-98cd-eabc43c40f14.png)](https://app.arcade.software/share/t3Pu5PoACBztGqrVZ6uF?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Launching Google Sheets on Cognis** **Discover how to easily fire up Google Sheets on Cognis**](https://app.arcade.software/share/t3Pu5PoACBztGqrVZ6uF?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkStep 3: How to Use Cognis’ Google Sheets Integration Once you’ve selected the sheet you want to operate on, you can ask Cognis for a number of operations. Let’s take an example workflow and find out how Cognis works in practice. _(You can watch the interactive demo below for a clearer understanding.)_ Example: Analyze my last Quarter’s Sales Report and highlight the top-performing designs and regions and their correlations The steps you have to undertake in Cognis are as follows: 1. **Prompt Cognis with** ‘Analyze my Google Sheet’ 2. **Select the correct sheet** by clicking on the ‘Select Sheet’ button 3. **Select the correct corresponding Sub-sheet** by clicking on the options Cognis provides 4. Once Cognis finishes analyzing the sub-sheet, **enter your actual task/action** you want Cognis to perform. In this example, we say ‘Analyze my last Quarter’s Sales Report and highlight the top-performing designs and regions and their correlations’ 5. You can click the ‘Preview Sheet’ button at the top right corner to preview the sheet in its native interface 6. Cognis also provides drop-down table views of the new data that it wants to add, and asks for your confirmation 7. If you’re satisfied with the result, click on the ‘Proceed’ button to add the newly analyzed rows and data to your Google Sheet 8. Cognis automatically updates the requested analysis of our example in the Google Sheet directly Click here to watch the interactive demo Step 3: Analyze a Google Sheet Using Cognis AI Suite [![Analyze a Google Sheet Using Cognis AI Suite](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/https://image.mux.com/frUSmBKYSEjAbBvWbSNMWeW01OcfbgtcziyMRlrB02Q6g/thumbnail.webp?time=0)](https://app.arcade.software/share/AJU06pM3vIpOTtn5Vsy8?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**Analyze a Google Sheet Using Cognis AI Suite** **Learn how to analyze data sitting in Google Sheets with Cognis.**](https://app.arcade.software/share/AJU06pM3vIpOTtn5Vsy8?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA ## insert\_linkSwitching LLMs and Using Branching on Cognis while using Google Sheets One of Cognis’ core functionalities is the ability for users to switch between LLMs seamlessly, whether at the beginning of a chat or at the message level. When you switch LLMs, a new branch is created, which initiates a new chat but stores the chat with the previous LLM in Cognis’ history. Allowing you to A/B test responses and find the one that fits your task perfectly. There are two ways to use the Multi-LLM and Branching in Cognis. ### 1\. Switching Models for the same chat for different tasks You can switch between different models for a specific set of tasks. For example, in the same chat branch, you can switch between GPT 5 for writing and Gemini 2.5 Pro for analyzing big sets of data. In this scenario, a new branch is not created. How to use this: 1. Click on the Button at the top right side of your chat window where the Model Name is written 2. Access the list of models from the drop-down and select a different model 3. Once you’ve selected the new model, continue your chat as usual with the new model You can switch back to the previous model using the same process again, or you can switch to a different model. In this scenario, you can switch between as many models as you’d like. ### 2\. Creating Branches with Different Models to A/B Test Responses You can switch between different models to A/B test and check the response quality of the respective models in the same chat window. A new branch is created each time you switch a model. Click here to watch the interactive demo Switching between different models for a Single Task [![LLM Switching on Cognis for a Single Task](https://cdn.arcade.software/cdn-cgi/image/fit=scale-down,format=auto,dpr=2,width=3840/https://image.mux.com/3jvkT01KUqMnMI6Phs02U51ehJ9k01Q00ljMJl3cqkp00Zl8/thumbnail.webp?time=0)](https://app.arcade.software/share/vbRlrX3hViIQVMCiacGy?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) [**LLM Switching on Cognis for a Single Task** **See how LLM Switching in the same chat thread works on Cognis**](https://app.arcade.software/share/vbRlrX3hViIQVMCiacGy?embed_referrer=https%3A%2F%2Fwww.scalifiai.com%2F) Play Invalid domain for site key. ERROR for site owner: Invalid domain for site key reCAPTCHA For example, you can use GPT 5 and Gemini 2.5 Pro to conduct the same data analysis to check which model did a better job. In this scenario, each model change leads to the creation of a new branch in the same chat window. How to use this feature: 1. Below your message to Cognis, you’ll find a four-star icon made of four circles; click it to open the model selector 2. Choose the model you want to switch to and click on the model name from the drop-down 3. The message is automatically resent to the new model, and Cognis creates a demarcation of the moment in the chat where you branched with numbered options for accessing each respective branch 4. You can click on the branch numbers to switch between different model responses 5. You can continue the chat, and beside each response sent by Cognis, a branch icon appears, which, when clicked, will take you back to the origin point of when you branched the last time during the chat **Get started with Cognis Ai [here](https://www.scalifiai.com/cognis-ai) and make Google Sheets operations a cakewalk!** Click here to watch the interactive demo #### Frequently Asked Questions ###### 1\. What is Cognis? Cognis is a workflow automation platform and AI chat assistant built to simplify your worklife. It uses Agentic AI and intelligent agents to automate repetitive tasks, create complete workflows, and act as your artificial intelligence personal assistant. With features like intuitive UI, near-zero hallucination for LLMs, and support for multiple AI models, Cognis combines the best of AI and automation into a single interface. ###### 2\. What LLMs can I use in Cognis AI? You can use multiple LLMs inside Cognis AI. The platform is designed as a multi-LLM orchestration layer, so you’re not locked into one provider. Supported LLMs include OpenAI models (GPT-5, GPT-4o, GPT-4 Turbo, GPT-3.5 series, o-mini family). Anthropic Claude models (Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku) Google Gemini models (Gemini 1.5 Pro, Gemini 1.5 Flash), DeepSeek (latest V3 and V3.1 reasoning models) and more models to be added soon. ###### 3\. What makes Cognis different from other AI apps? Most AI apps or automation apps are single-function. Cognis combines AI chat, agent software, and workflow automation platform capabilities in one. Instead of juggling multiple AI programs or AI tools free/paid, you get one artificial intelligence app where agentic AI manages context, memory, and execution/ ###### 4\. Who should use Cognis? Indie Builders who hate administrative work. Startups with small teams and big dreams. Professionals needing a chatgpt AI assistant or artificial intelligence personal assistant. Developers who want to create an AI or experiment with AI programs in one place. Enterprises wanting to level up by automating workflows without the hassle of 2 AM 'nothing's working with the other' calls. ###### 5\. Does Cognis address AI bias? Yes. Cognis includes monitoring to reduce AI bias across Chat GPT-4, Chat GPT-3.5, and other best AI models. It helps ensure fair, transparent outputs when you use it as your AI assistant or automation app. #### External References 1. Cognis Ai (Beta) is live now. Explore the power of a Multi-LLM Agentic Ai platform that reduces your work time by 5x. [Read the Launch Note.](https://www.scalifiai.com/blog/cognisailaunch) Related: Walkthroughs ##### In this blog [Pre-Setup: Getting Started with Cognis](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis#pre-setup:-getting-started-with-cognis) [Step 1: Installing and Setting Up the Google Sheets Integration](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis#step-1:-installing-and-setting-up-the-google-sheets-integration) [Step 2: Launching the Google Sheet Integration on Cognis](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis#step-2:-launching-the-google-sheet-integration-on-cognis) [Step 3: How to Use Cognis’ Google Sheets Integration](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis#step-3:-how-to-use-cognis'-google-sheets-integration) [Switching LLMs and Using Branching on Cognis while using Google Sheets](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis#switching-llms-and-using-branching-on-cognis-while-using-google-sheets) [FAQs](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs Cognis Ai (Beta): The Ultimate Agentic AI for all Your Professional Needs [Read Moreeast](https://www.scalifiai.com/blog/cognisailaunch) ###### Explore other usecases Revenue Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Policy Management | IAM Guide Source: https://www.scalifiai.com/docs/iam/policy-management Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # Policy Management Policies are a vital part of the platform. They define the granular level access of the user on the platform. You can restrict the user access and maintain platform integrity with these policies. Allowed operations that users can perform on the platform are: 1. Create Policy 2. Update Policy 3. Delete Policy ## insert\_linkCreate Policy 01. Proceed to the policy management page by either click on the policies card on the IAM Dashboard or IAM header. 02. There are a few preconfigured base policies that are needed for the platform to function. These policies can also be used by users but are non-editable and are managed by the platform itself. 03. On the Policies page click on **Create Policy** Button. 04. From the list of modules, select the modules for which you need to create a policy. Multiple modules are allowed in a single policy. ![Select Module Create Policy - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Select_Module_0e3c4d86c6.svg) 06. Select the actions and resources for the policy. ![Policy Actions and Resources Create Policy - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Policy_Actions_and_Resources_842e939656.svg) 08. Once done, update the name and description of the policy. Description is optional. And finally create the policy. ![Policy Name Create Policy - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Policy_Name_df91a125dc.svg) 10. Congratulations, you have successfully created a policy. ## insert\_linkUpdate Policy 1. To update a policy select the policy and from **Actions** dropdown click **Edit**. ![Edit Policy Policy List Table - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Edit_Policy_0a9b8a6d6e.svg) 3. Make the necessary changes to the policy and click on **Update**. 4. You can get a summarised view of policy by clicking on the policy name. Here, you can check the policy details in the modal. ![Preview Policy Modal - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/preview_policy_7d3a37ac3d.svg) ## insert\_linkDelete Policy 1. To delete a policy, go to the policies page. Select the policy to delete and from the actions dropdown click on the delete button. ![Delete Policy - Policy List Table - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Delete_Policy_7632dea08e.svg) 3. If all the checks pass, then you can proceed with deleting the policy. (In case the policy is attached to any user then you will need to detach the policies from all the users before deleting the policy.) ![Delete Policy Pre Check - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/delete_policy_pre_check_987e69098a.svg) Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [PreviouswestUser Management](https://www.scalifiai.com/docs/iam/user-management) [NextThird Party Accountseast](https://www.scalifiai.com/docs/iam/third-party-accounts) ##### Content [Overview](https://www.scalifiai.com/docs/iam/policy-management#overview) [Create Policy](https://www.scalifiai.com/docs/iam/policy-management#create-policy) [Update Policy](https://www.scalifiai.com/docs/iam/policy-management#update-policy) [Delete Policy](https://www.scalifiai.com/docs/iam/policy-management#delete-policy) * * * hide\_imageHide Images --- --- ## Rapid Prototyping AI | Scalifi Ai Source: https://www.scalifiai.com/ai-ml-model-building/features/rapid-prototyping-ai-model Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Streamline AI Model Constructionwith MBS In the realm of AI, flexibility and efficiency are paramount. Scalifi Ai’s Model Building Service (MBS) brings these elements to the forefront with features designed to revolutionise how machine learning models are built. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Streamline AI Model Construction Model Building - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/MBS_Feature_Rapid_Prototype_063bcf0270.svg) ![improvement.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/improvement_b558b0b510.svg) ##### Enhanced Model Performance Customise AI models with MBS for precision and effective performance, ensuring greater accuracy in complex tasks. ![brainstorm.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/brainstorm_34d990e70c.svg) ##### Collaborative Model Building MBS's collaborative AI model building enhances team learning, innovation, and accessibility for educational and professional projects. ![revenue.svg](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/revenue_bf7b8bf128.svg) ##### Cost-Effective AI Solutions MBS's no-code, automated assembly reduces coding needs, speeds development, and cuts costs, making AI accessible and affordable. ![Enhanced Development Efficiency Model Building Job - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/optimize_Ai_Model_82a073ee98.svg) ### Optimise AI Models for Specific Needs Maximise AI model efficiency with MBS's Flexible Layer Configuration. Tailor AI models to your unique project needs, ensuring optimal performance and precision. This feature not only enhances model effectiveness but also addresses specific challenges, leading to improved solutions and a competitive edge in AI development. ![Rapid Prototyping and Iteration - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/rapid_Prototyping_Mbs_b4256048eb.svg) ### Rapid Prototyping and Iteration Experience faster AI development with MBS. Its intuitive interface and streamlined processes facilitate rapid prototyping and efficient iteration, significantly reducing time-to-market for AI solutions. Stay ahead in the fast-paced tech world by quickly adapting to changing requirements and accelerating innovation. ![Empower Workforce with Efficient Tools - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/empower_Workforce_AI_9ff2320bda.svg) ### Empower Your Workforce with Efficient Tools Elevate team productivity with MBS's efficient AI model development tools. By simplifying complex tasks, MBS allows your team to focus on innovation and strategic problem-solving, thus boosting overall productivity and contributing to the advancement of AI projects in a fast-evolving technological landscape. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Scalifi Ai Model Catalog | AI ML Model No-Code Low-Code Source: https://www.scalifiai.com/model-catalog Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Organize Your Machine Learning Modelswith the Model Catalog Discover, explore, streamline and manage your organization's machine learning models in one centralized hub. This organized inventory will help in keeping track of various aspects of the models through metadata, improve knowledge sharing between teams and make better decisions to enhance productivity. A model catalog is a valuable tool for organizations that heavily rely on machine learning models, promoting efficiency, collaboration, and responsible AI practices. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Model Catalog Service - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/model_catalog_service_banner_scalifi_ai_f4677115c3.svg) ![Improved Knowledge Sharing Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/improved_knowledge_sharing_model_catalog_030d829653.svg) ### Improved knowledge sharing Model Catalog makes it easier for data scientists, engineers and different team members within the organization to discover and understand existing models, preventing the duplication of effort. Using Scalifi Ai, track the changes to models over time, facilitating teamwork and ensuring everyone's using the most up-to-date version. This allows the members to pinpoint relevant models based on specific criteria like task, performance metrics, or data requirements. ![Streamlined workflow model catalog Illustration - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/streamlined_workflow_model_catalog_scalifi_ai_4f0488f279.svg) ### Streamlined workflow Model Catalog simplifies the deployment and management of models by keeping everything organized in one place. Scalifi Ai allows automating tasks like model registration. This will significantly reduce the time and effort required to manage models. And allow data scientists to focus on building and iterating, while also ensuring all models are properly tracked and easy to find. ![Better Decision Making Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/better_decision_making_model_catalog_5fb0514099.svg) ### Better decision-making Provides insights into model performance and usage, allowing for data-driven decisions about model development and deployment. Users can efficiently compare and select the most suitable model for their task, considering factors like past performance and intended use. This transparency fosters trust in model outputs, ultimately leading to better-informed choices across the organization. ![Policy-Based Access Control Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/policy_based_access_control_model_catalog_6c1d85b231.svg) ### Policy-Based Access Control Empower your organization’s models with tailored access management. Seamlessly manage who can access the models and their metadata based on user policies with granular control and minimize the risks. This will grant granular control over who can access and use specific models by defining policies that consider factors like user role, model type, and access time. This ensures sensitive models are protected, while allowing authorized users the access they need for efficient data science workflows. ![Explainability And Interpretability Model Catalog - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/explainability_and_interpretability_model_catalog_84456e0381.svg) ### Explainability and Interpretability For transparency and trust, the model catalog should incorporate details on a model's explainability and interpretability. Using Scalifi Ai’s Model Catalog users can capture how the model arrives at its predictions. By understanding the rationale behind a model's decisions, users can better assess its credibility and identify potential biases. This information is crucial for responsible AI development and deployment. ### What Scalifi Ai provide with the Model Catalog local\_library #### Centralized repository Model Catalog acts as a central repository for all of an organization's machine learning models. This makes it easy for teams to find, share, and reuse models. account\_tree #### Version control Model Catalogs allow you to track different versions of each model. This is important for ensuring that teams are using the latest and most accurate version of a model. terminal #### CLI support Manage model creation, add model versions and its variants using our proprietary library or CLI tool. manage\_search #### Search and discovery Model Catalog provides search and discovery features that make it easy for teams to find the models they need. This can be done by searching with related tags, keywords, by filtering by criteria such as model type or by browsing through a curated list of models. folder\_open #### Easy to use file browser Upload your metadata using built-in file browser support with the upload progress notification. Never miss out on the upload status as it has features like upload error detection. preview #### Preview and download metadata Did you forget what was the content in the uploaded metadata? Nevermind, preview the metadata with just one click and download it for future reference. ## Model Catalog ServiceFeatures layers ##### **Unified Model Registry Across Frameworks** Model building service provides a rich set of customizable layers, allowing you to tailor the architecture of your AI models to fit specific requirements and complexities. [Learn Moreeast](https://www.scalifiai.com/model-catalog/features/unified-model-registry) precision\_manufacturing ##### **Advanced Model Design and Export Capabilities** Tailor and enhance AI models for specific needs. Prototype, test, and refine quickly in a no-code setup, speeding up development and fostering innovation. [Learn Moreeast](https://www.scalifiai.com/model-catalog/features/advanced-model-design-and-export) tips\_and\_updates ##### **Model Version and lineage tracking** Boost your organization with Scalifi Ai's model lineage tracking. Ensure compliance and reliability in AI apps with clear documentation and robust version control. [Learn Moreeast](https://www.scalifiai.com/model-catalog/features/model-version-and-lineage-tracking) auto\_fix\_high ##### **Programmatic Access with Python Library** Leverage Scalifi Ai's Python Library for seamless automation and model manipulation. Integrate scripts with external systems, enhancing workflow efficiency. [Learn Moreeast](https://www.scalifiai.com/model-catalog/features/programmatic-access-with-python-library) add\_business ##### **Scalable and Secure Infrastructure for High-Performance Management** Ensure data security and scalability with a robust infrastructure. Advanced protocols and monitoring systems protect sensitive data, supporting growth and compliance. [Learn Moreeast](https://www.scalifiai.com/model-catalog/features/scalable-and-secure-infrastructure) work\_history ##### **Model Governance and Access Control for Secure Deployment** Ensure ML model reliability, accountability, and security throughout their lifecycle. Govern development, deployment, and monitoring with systematic processes. [Learn Moreeast](https://www.scalifiai.com/model-catalog/features/model-governance-and-access-control) #### Frequently Asked Questions ![frequently asked questions](https://www.scalifiai.com/_next/static/media/faqCircle.3d68e398.svg) ###### What is Scalifi Ai’s Model Catalog A Model Catalog is a centralized repository or database that organizes and manages various machine learning models, their metadata, and associated documentation. It serves as a comprehensive inventory where data scientists and other stakeholders can discover, access, and deploy pre-existing models for various tasks and applications. Additionally, a Model Catalog may include features such as version control, model comparison, and collaboration tools to facilitate efficient model development and deployment workflows within organizations. ###### How can Scalifi Ai’s Model Catalog be important for data science and machine learning projects? Model Catalog helps in organizing, documenting, and tracking the lifecycle of machine learning models, promoting collaboration, reproducibility, and efficiency within data science teams. It also facilitates model discovery and reuse, thereby reducing duplication of effort and promoting best practices. ###### Can I use Model Catalog for my organization? Yes, you can get started by creating a model by following the steps in [this](https://www.scalifiai.com/docs/model-catalog/model-management#content-sub-section-61) guide. Using a well-implemented Model Catalog serves as a foundational component of the ML lifecycle, facilitating efficient model development, deployment, and maintenance while promoting collaboration and innovation within data science teams. ###### What types of frameworks are supported for creating models? Scalifi Ai stands out for its compatibility with leading AI frameworks like TensorFlow, PyTorch and Scikit Learn. Using this frameworks, one can create a model by including machine learning methods such as regression, classification, clustering algorithms, deep learning models, and ensemble methods. ###### What information is included in each model within the catalog? Key information in a model typically includes model name, description, metadata, trainable/non-trainable value, tags, versions, framework, input/output specifications, etc. ###### Can I ensure consistency and accuracy using Model Catalog? Yes, with Scalifi Ai’s scalable and secure infrastructure for high-performance management, consistency and accuracy can be ensured. Validation workflows and collaborative feedback further reinforce accuracy, fostering a reliable and efficient model development ecosystem. \[ [Read Mode](https://www.scalifiai.com/model-catalog/features/scalable-and-secure-infrastructure)\] ###### How can the Model Catalog facilitate collaboration among data scientists and stakeholders? With Scalifi Ai’s shared platform, data scientists and stakeholders can work as a team or individually to discover, evaluate, and utilize existing models, fostering collaboration, knowledge sharing, and reuse of best practices across teams and departments. #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## AI ML Models | AI Sales & revenue Prediction - Scalifi Ai Source: https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Revenue Prediction Unlock Precise Revenue Forecasting with Advanced AI Models Leveraging your Historical & Real-Time Data [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Revenue Prediction](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/revenue_prediction_1_ee30c005fc.svg) ## Problem Statement ##### Development of an AI-Based Revenue Prediction System for a Company ![Development of an AI-Based Revenue Prediction System for a Company](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/revenue_prediction_2_e327831622.svg) A business needed an AI-based revenue prediction system that can accurately forecast revenue for each product and segment, considering various data sources like sales data, market trends, and customer behavior. The system should account for seasonality, adapt to changing market conditions, and provide real-time insights to make data-driven decisions regarding inventory, marketing, and staffing. The goal is to develop a cost-effective, scalable, and user-friendly system that improves accuracy with reliability and will help business leaders to make informed business decisions for driving overall growth. ## Solution ##### Scalifi Ai's AI-Powered Accurate Revenue Prediction System ![Scalifi Ai's AI-Powered Accurate Revenue Prediction System](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/revenue_prediction_3_20b96d585e.svg) Scalifi Ai's AI-powered technology can help businesses of all kinds develop an AI-based revenue prediction system that accurately forecasts the revenue generated for each product and segment. Our solution involves a step-by-step process that begins with data collection and pre-processing. The AI-powered systems collect data on previous sales, market trends, customer behavior, etc., and then pre-process it to prepare it for analysis. Using advanced AI techniques like machine learning, deep learning, and natural language processing, we then develop an AI model that can analyze large volumes of data and provide accurate revenue predictions for each product and segment. Our AI model takes into account factors like seasonality, changes in market conditions, and other relevant variables to provide real-time insights that can inform inventory, marketing, and staffing decisions. Our system is designed to be user-friendly, with a simple, intuitive, and easy-to-navigate interface. It is also scalable, so it can handle large volumes of data as your company grows. Additionally, we work with you to develop a cost-effective solution that perfectly fits your organization’s requirements & budget. Say goodbye to revenue prediction headaches with traditional means and say hello to modern data-driven insights. ## Benefits ###### groupEnable Business Leaders to Make Better Decisions Our solution predicts accurate Revenue & provides unique business insights for each product & segment which helps product heads, marketing heads, directors, HR Managers, etc., to make better decisions on implementing marketing strategies, improving existing products, introducing new products, hiring manpower, etc. Our user-friendly interface can be used by anyone in the organization, leading to higher adoption rates and ease of use. ###### query\_statsGet Accurate Predictions with Real-Time Insights Accurate and reliable predictions through analysis of large volumes of data from various sources by advanced AI models which require zero human intervention. Our solution provides real-time insights into seasonality and changing market conditions, enabling data-driven decision-making for inventory, marketing, and staffing. ###### dashboard\_customizeSave Cost With Our Scalable System AI-powered systems require very little maintenance and almost zero human intervention, hence, there is no unnecessary cost associated, unlike the case of traditional systems. Our solution is fully scalable that can effortlessly handle large volumes of data as your company grows. ###### Related Blogs Natural Language Processing: How Neural Word Embeddings Enable Machines to Understand Text [Read moreeast](https://scalifiai-founder.medium.com/how-do-machines-understand-text-via-natural-language-processing-nlp-41aeb853ef52?source=friends_link&sk=b38399d604862bab7c3b2d12ee601dee) In-Depth Study of Large Language Models (LLM) [Read moreeast](https://www.scalifiai.com/blog/what-is-large-language-model-llm) ###### Explore other usecases Customer Churn Prediction [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) AI in Cyber Security to Redefine the Security Posture [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Credit Card Fraud Detection [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## AI/ML Credit Card Fraud Detection – Scalifi Ai Source: https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Credit Card Fraud Detection Prevent Modern Credit Card Frauds With the Power of AI and Regain Customer Trust & Loyalty [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Credit Card Fraud Detection](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/credit_card_fraud_1_f3c7e18d4e.svg) ## Problem Statement ##### Combat Fraud with Advanced Machine Learning and Data Analytics ![Combat Fraud with Advanced Machine Learning and Data Analytics](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/credit_card_fraud_2_368bc7a6f8.svg) A company witnessed an increase in fraudulent transactions on its customers' credit cards. There were some fraudulent activities such as purchases from unauthorized locations, unusually large purchases, and repeated transactions. The company faced losses due to these fraudulent transactions, which negatively impacted its reputation and customer trust. Their existing rule-based fraud detection system was not effective in detecting sophisticated fraudulent activities, and their manual reviews were time-consuming and costly. The company needed a more advanced solution that leverages machine learning and data analytics to accurately detect and prevent credit card fraud in real-time. ## Solution ##### Scalifi Ai's AI-Powered Fraud Detection Solution ![Scalifi Ai's AI-Powered Fraud Detection Solution](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/credit_card_fraud_3_d634e03e62.svg) To cater to this problem, Scalifi Ai’s AI-powered solution uses a machine learning model to identify patterns of fraudulent activities by analyzing customer transaction data. The model leverages feature such as purchase location, amount, frequency, and merchant type to detect potential fraud. The solution incorporates real-time monitoring and advanced data analytics to detect and prevent fraud proactively. By implementing this solution, credit card companies can significantly reduce the risk of fraudulent transactions, improve their reputation, and increase customer trust. ## Benefits ###### incomplete\_circlePrevent 99.9% of Modern Credit Card Frauds By leveraging Scalifi Ai’s advanced machine learning and data analytics, credit card companies can more accurately identify patterns of fraudulent activities, reducing the risk of financial losses due to fraudulent transactions. The solution also provides real-time monitoring of customer transaction activity, allowing for faster response times and the prevention of 99.9% of fraudulent activities before it occurs. ###### query\_statsOptimize Your Security Cost & Increase Efficiency The AI-powered solution is more cost-effective than traditional manual fraud detection methods, reducing the need for human resources and manual review. The AI model continuously learns and improves its accuracy over time through the analysis of new data and periodic training. This improves the overall efficiency of the security systems. ###### groupImproved Customer Trust & Enhanced Reputation By effectively preventing fraudulent activities, credit card companies can improve customer trust and confidence in the security of their sensitive credit card information. The use of an advanced AI-powered solution to prevent fraud can enhance the reputation of any credit card company as a reliable and secure financial service provider. ###### Related Blogs Natural Language Processing: How Neural Word Embeddings Enable Machines to Understand Text [Read moreeast](https://scalifiai-founder.medium.com/how-do-machines-understand-text-via-natural-language-processing-nlp-41aeb853ef52?source=friends_link&sk=b38399d604862bab7c3b2d12ee601dee) In-Depth Study of Large Language Models (LLM) [Read moreeast](https://www.scalifiai.com/blog/what-is-large-language-model-llm) ###### Explore other usecases AI in Cyber Security to Redefine the Security Posture [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Customer Churn Prediction [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) Revenue Prediction [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## AI Models 101: A Beginner's Guide to Getting Started Source: https://www.scalifiai.com/blog/what-is-an-ai-model Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # The Beginner's Guide to AI Models: Understanding the Basics 1 July 2026\|7 min read ## insert\_linkIntroduction Imagine a world where machines can learn from experience, make intelligent decisions, and even generate creative content. This is no longer the realm of science fiction; it's the reality we're living in thanks to the transformative power of the AI model. These intelligent systems are revolutionizing various aspects of our lives, from the way we interact with technology to the way businesses operate. But what exactly are AI models, and how do they work? This comprehensive guide delves into the fascinating world of AI models, exploring their architecture, different types, and the vast array of applications that are shaping our future. ## insert\_linkWhat is an AI Model? An AI model is a computational framework designed to mimic human intelligence. It's essentially a software program trained on vast amounts of data to learn patterns, identify relationships, and make predictions. Unlike traditional programming, AI models don't require explicit instructions for every task. Instead, they learn and adapt through experience, continuously improving their performance over time. The significance of AI models lies in their ability to: 1. **Automate complex tasks:** AI models can handle repetitive and time-consuming tasks with high accuracy, freeing up human resources for more strategic endeavors. 2. **Make data-driven decisions:** By analyzing vast datasets, AI models can uncover hidden patterns and insights that would be difficult for humans to identify, leading to more informed decision-making. 3. **Personalize experiences:** AI models can tailor experiences to individual needs and preferences, enhancing user engagement and satisfaction. ![Significance of Ai Models - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/significance_of_ai_models_8c16c3563d.svg) _Significance of Ai Models - Scalifi Ai_ ## insert\_linkUnderstanding the Architecture of AI Models To grasp the inner workings of AI models, it's crucial to understand their fundamental components: - **Input Layer:** This layer receives raw data, such as images, text, or numerical values, and prepares it for processing by subsequent layers. - **Hidden Layers:** These layers, often consisting of interconnected nodes, perform the core computations and learning processes. The number and complexity of hidden layers determine the model's ability to learn intricate patterns. - **Output Layer:** This layer generates the final output, which can be a prediction, classification, or another form of result based on the processed data. ## insert\_linkThe Training Process: Empowering AI Models to Learn AI models don't possess inherent knowledge; they acquire it through a process called training. This involves: - **Data Feeding:** The model is exposed to a massive dataset relevant to the task it's designed for. - **Parameter Adjustment:** The model's internal parameters, such as weights and biases, are constantly adjusted based on the comparison between its predictions and the actual outcomes in the training data. - **Error Minimization:** Through iterative adjustments, the model strives to minimize the difference between its predictions and the actual outcomes, gradually improving its accuracy. ## insert\_linkOptimization Algorithms: Accelerating the Learning Journey The training process of AI models can be computationally expensive, especially for complex models with large datasets. Optimization algorithms play a crucial role in speeding up the learning process by efficiently adjusting the model's parameters to minimize the error between its predictions and the actual outcomes. Here are some commonly used optimization algorithms in AI: - **Gradient Descent:** This iterative algorithm calculates the **gradient**, which indicates the direction of the steepest descent in the error function. The model's parameters are then adjusted in small steps along this direction, gradually minimizing the error. - **Stochastic Gradient Descent (SGD):** This variant of gradient descent updates the model's parameters based on a single data point or a small batch of data points at each iteration. This can be more efficient than using the entire dataset for each update, especially for large datasets. - **Adam:** This advanced algorithm combines the strengths of several other optimization algorithms, adapting the learning rate for each parameter individually and achieving faster convergence. ## insert\_linkRegularization Techniques: Preventing Overfitting and Enhancing Generalizability While training an AI model, it's essential to strike a balance between **accuracy** and **generalizability**. Overfitting occurs when the model memorizes the training data too closely, leading to poor performance on unseen data. **Regularization techniques** help prevent overfitting by introducing constraints that penalize overly complex models. Here are some common regularization techniques: - **L1 regularization:** This technique adds the absolute value of the model's weights to the error function, encouraging sparsity and reducing the model's complexity. - **L2 regularization:** This technique adds the square of the model's weights to the error function, penalizing large weights and promoting smoother decision boundaries. - **Dropout:** This technique randomly drops a certain percentage of neurons during training, forcing the model to rely on a broader set of features and improve its generalizability. By incorporating optimization algorithms and regularization techniques, we can train AI models that are both **accurate** and **generalizable**, performing well on unseen data and avoiding overfitting to the training data. ## insert\_linkA Spectrum of AI Models: Catering to Diverse Needs ## insert\_linkHow to Build and Train an AI Model Now that you've grasped the core concepts and diverse landscape of AI models, let's explore the practical steps involved in building and training your own: 1. **Define the Problem and Objective:** The first step is to clearly define the **problem you want to solve** and the **desired outcome** of your AI model. This will guide your choice of model type, data selection, and evaluation criteria. 2. **Gather and Prepare Data:** Your data's quality and relevance are crucial for your AI model's success. Ensure you have a **sufficient amount of high-quality data** relevant to your chosen task. This may involve data collection, cleaning, preprocessing, and labeling (for supervised learning). 3. **Choose the Right Model and Framework:** Based on your problem definition and data characteristics, select the appropriate **model type** (e.g., supervised learning, deep learning) and a suitable **development framework** (e.g., TensorFlow, PyTorch) that aligns with your technical expertise and computational resources. 4. **Train the Model:** This stage involves feeding your prepared data into the chosen model and iteratively adjusting its internal parameters through an **optimization algorithm**. The model learns from the data patterns and gradually improves its ability to perform the desired task. 5. **Evaluate and Fine-tune:** Once trained, evaluate the model's performance on **unseen data** using relevant metrics. This helps assess its generalizability and identify potential areas for improvement. Based on the evaluation results, you may need to fine-tune the model by adjusting hyperparameters, collecting more data, or trying different model architectures. 6. **Deploy and Monitor:** Once satisfied with the model's performance, deploy it into a production environment where it can be used for real-world applications. Continuously monitor the model's performance and retrain it periodically with new data to maintain accuracy and adapt to evolving conditions. Building and training an AI model can be a complex process, but with the right tools, resources, and a structured approach, you can unlock the potential of this transformative technology. Scalifi Ai offers comprehensive solutions to streamline your AI development journey. Their 'Model Building Service' ( [https://www.scalifiai.com/ai-ml-model-building](https://www.scalifiai.com/ai-ml-model-building)) provides expert guidance and technical expertise to help you build and train high-performing AI models tailored to your specific needs. ## insert\_linkReal-World Use-Cases of AI Models: 01. **Natural Language Processing (NLP) in Virtual Assistants:** AI models, particularly those powered by advanced [NLP](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp) techniques, are the brains behind virtual assistants like Siri, Alexa, and Google Assistant. These models excel in understanding and responding to natural language queries, making them integral to our daily lives for tasks such as setting reminders, answering questions, and providing personalized recommendations. Read more at: [Credit Card Fraud Detection](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) 02. **Image Recognition in Healthcare:** In the healthcare sector, AI models are transforming diagnostics through image recognition. Models like Convolutional Neural Networks (CNNs) can analyze medical images, such as X-rays and MRIs, aiding in the early detection of diseases like cancer. This technology accelerates the diagnostic process and enhances the accuracy of medical assessments. 03. **Fraud Detection in Financial Services:** Financial institutions leverage AI models for [fraud detection](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) and prevention. Machine learning models analyze transaction patterns, detect anomalies, and flag potentially fraudulent activities in real-time. This proactive approach helps mitigate financial losses and safeguards the integrity of financial systems. Read more at: [Credit Card Fraud Detection](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) 04. **Autonomous Vehicles:** The development of autonomous vehicles relies heavily on AI models. These models process vast amounts of sensor data, including images, lidar, and radar inputs, to make split-second decisions about steering, acceleration, and braking. AI models enable vehicles to navigate complex traffic scenarios and respond to dynamic environments. 05. **Language Translation:** AI models, especially those based on transformer architectures, have significantly improved the field of language translation. Platforms like Google Translate utilize these models to provide accurate and context-aware translations across multiple languages, breaking down communication barriers on a global scale. 06. **Personalized Content Recommendations:** Streaming services and e-commerce platforms leverage AI models to provide personalized content recommendations. These models analyze user behavior, preferences, and historical data to suggest movies, music, or products tailored to individual tastes, enhancing user experience and engagement. 07. **Predictive Maintenance in Manufacturing:** AI models play a crucial role in predictive maintenance for manufacturing equipment. By analyzing sensor data and historical performance, these models can predict when machinery is likely to fail, allowing for timely maintenance and reducing downtime, ultimately improving operational efficiency. 08. **Agricultural Yield Prediction:** In agriculture, AI models are used to predict crop yields based on various factors such as weather patterns, soil conditions, and historical data. This information helps farmers make informed decisions about planting, irrigation, and harvesting, optimizing agricultural practices for increased productivity. 09. **Sentiment Analysis in Social Media:** Brands utilize AI models for sentiment analysis on social media platforms to gauge public perception. Natural Language Processing models analyze social media posts and comments, providing insights into how customers feel about products, services, or marketing campaigns. ![Real World Usecases of Ai Models - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/real_world_usecases_of_ai_models_ee8e00b516.svg) _Real World Usecases of Ai Models - Scalifi Ai_ 11. These are just a few examples, and the potential applications of AI models continue to expand rapidly across various sectors. As AI technology evolves, we can expect even more innovative and transformative applications to emerge, shaping the future of our world in profound ways. ## insert\_linkThe Future of AI Models: A Glimpse into a Transformative Era 1. The journey of AI models is far from over. As we look towards the horizon, several exciting trends and advancements are poised to shape the future of this transformative technology: - **Increased Complexity and Accuracy:** AI models are expected to become increasingly complex, with deeper neural network architectures and more sophisticated algorithms. This will lead to enhanced accuracy and capabilities, enabling them to tackle even more intricate tasks and outperform human performance in specific domains. - **Explainable AI (XAI):** As AI models become more complex, understanding their decision-making processes becomes crucial. XAI techniques will play a vital role in making AI models more transparent, interpretable, and trustworthy, fostering responsible development and deployment. - **Integration with Emerging Technologies:** The convergence of AI with other cutting-edge technologies like quantum computing holds immense potential. Quantum computers could accelerate AI model training and enable the development of even more powerful models capable of solving currently intractable problems. - **Democratization of AI:** The development and deployment of AI models will become more accessible through advancements in tools, platforms, and cloud-based resources. This will democratize AI, allowing individuals and organizations with limited resources to leverage its power and unlock new possibilities. ## insert\_linkConclusion As AI models become more deeply embedded in our lives, ethical considerations regarding bias, fairness, and privacy will become paramount. Responsible development and deployment practices will be essential to ensure that AI benefits all of humanity and fosters a positive societal impact. The future of AI models is brimming with possibilities. From revolutionizing industries to tackling global challenges, these intelligent systems have the potential to shape a brighter future for all. As we continue to explore and develop this technology, it's crucial to do so responsibly and ethically, ensuring that AI serves as a force for good in the world. AI models have become an integral part of our lives, and their impact is only expected to grow in the years to come. By understanding their fundamentals, exploring their diverse applications, and staying informed about future advancements, we can actively participate in shaping the responsible development and deployment of AI for a better future. #### External References 1. [Deploying ML models in Production](https://stackoverflow.blog/2020/10/12/how-to-put-machine-learning-models-into-production/) 2. [Optimization Algorithms in AI](https://www.d2l.ai/chapter_optimization/) 3. [Kaggle - AI and Machine Learning Community](https://www.kaggle.com/) 4. [TensorFlow Documentation](https://www.tensorflow.org/) 5. [OpenAI - GPT Models](https://platform.openai.com/docs/models) 6. [Kaggle - AI and Machine Learning Community](https://www.kaggle.com/) Related: AI Ethics Deep Learning Machine Learning AI in Finance AI in Healthcare ##### In this blog [Introduction](https://www.scalifiai.com/blog/what-is-an-ai-model#introduction) [What is an AI Model?](https://www.scalifiai.com/blog/what-is-an-ai-model#what-is-an-ai-model) [Understanding the Architecture of AI Models](https://www.scalifiai.com/blog/what-is-an-ai-model#understanding-the-architecture-of-ai-models) [The Training Process: Empowering AI Models to Learn](https://www.scalifiai.com/blog/what-is-an-ai-model#the-training-process:-empowering-ai-models-to-learn) [Optimization Algorithms: Accelerating the Learning Journey](https://www.scalifiai.com/blog/what-is-an-ai-model#optimization-algorithms:-accelerating-the-learning-journey) [Regularization Techniques: Preventing Overfitting and Enhancing Generalizability](https://www.scalifiai.com/blog/what-is-an-ai-model#regularization-techniques:-preventing-overfitting-and-enhancing-generalizability) [A Spectrum of AI Models: Catering to Diverse Needs](https://www.scalifiai.com/blog/what-is-an-ai-model#a-spectrum-of-ai-models:-catering-to-diverse-needs) [How to Build and Train an AI Model](https://www.scalifiai.com/blog/what-is-an-ai-model#how-to-build-and-train-an-ai-model) [Real-World Use-Cases of AI Models:](https://www.scalifiai.com/blog/what-is-an-ai-model#real-world-use-cases-of-ai-models:) [The Future of AI Models: A Glimpse into a Transformative Era](https://www.scalifiai.com/blog/what-is-an-ai-model#the-future-of-ai-models:-a-glimpse-into-a-transformative-era) [Conclusion](https://www.scalifiai.com/blog/what-is-an-ai-model#conclusion) [FAQs](https://www.scalifiai.com/blog/what-is-an-ai-model#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/what-is-an-ai-model#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs Understanding Natural Language Processing (NLP) Essentials [Read moreeast](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp) In-Depth Study of Large Language Models (LLM) [Read moreeast](https://www.scalifiai.com/blog/what-is-large-language-model-llm) ###### Explore other usecases AI in Cyber Security to Redefine the Security Posture [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Customer Churn Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) Revenue Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) --- --- ## Learn how Memory Managed AI Agents Help Remove AI Slop Source: https://www.scalifiai.com/blog/agentic-ai-ai-slop Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # The Key to Removing AI Slop - Memory Managed AI Agents 15 November 2025\|10 min read This blog is for developers, engineers, product managers, technical leaders, AI researchers and architects, enterprises, startups, and AI enthusiasts. If you’re new to AI, we suggest reading [The Beginner's Guide to AI Models: Understanding the Basics](https://www.scalifiai.com/blog/what-is-an-ai-model) and [In-Depth Study of Large Language Models (LLM)](https://www.scalifiai.com/blog/what-is-large-language-model-llm) to understand the contents of this article better. ## insert\_linkTL;DR - **The Problem:** AI slop is a memory problem. Stateless agents forget users instantly, leading to generic, context-free output. - **The Solution:** A two-part memory architecture. Tactical Short-Term Memory (STM) for immediate context, and strategic Long-Term Memory (LTM) for persistent knowledge. - **LTM Design:** Building LTM requires key decisions on what to store (facts, experiences, rules) and when (real-time synchronous vs. fast asynchronous updates). - **The Mandate:** Mastering this memory blueprint is non-negotiable. It is the only way to build intelligent agents that eliminate slop and deliver real value. ## insert\_linkIntroduction to Dealing with AI Slop It’s 2025, and AI adoption keeps increasing at a rapid pace. So does AI slop. [Harvard recently found that 40% of us receive AI slop from our colleagues.](https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity) As bad as that may seem, using AI isn’t the problem. It’s actually the neglected aspect of AI, Memory. Without memory, LLMs/Agents are digital amnesiacs. They produce generic, context-free output because they can't recall past interactions, preferences, or your corrections. This is the very engine of AI slop. The solution is a robust memory architecture. This system enables agents to maintain contextual continuity and deliver personalized experiences, distinguishing between short-term recall for ongoing tasks and long-term knowledge for lasting relationships. But this power is a double-edged sword. Poorly managed memory creates slow, irrelevant, and costly agents. Get it wrong, and you've built a liability. Our blog dives deep into mastering agent memory, ensuring your AI gives intelligent responses instead of AI slop. ## insert\_linkWhat is AI Memory? To defeat AI slop, we must first understand its source. The generic, disconnected responses that flood our inboxes stem from a fundamental design flaw in most language models: they are stateless. Each prompt is an island, processed in isolation without any real recall of what came before. The “memory” of an out-of-the-box LLM isn’t memory at all; it’s just patterns baked into its parameters during training. ![A comparison diagram contrasting "AI Slop" with "True AI Memory." "AI Slop" is shown as stateless with no memory, while "True AI Memory" is an evolving partner that retains, recalls, and acts.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FAI_Slop_VS_Managed_Memory_f08241c433.png&w=3840&q=75) _AI Slop VS Managed Memory_ True AI memory is the antidote. It’s a dynamic system engineered to let agents retain, recall, and act on information from past interactions. It’s not just a database. Instead, it’s an active component in the decision-making loop, turning a forgetful calculator into an intelligent, evolving partner. ### The High Stakes of Getting Memory Right Why is this architectural shift so critical? Because without it, AI agents remain stuck in a loop of mediocrity. Integrating a proper memory function unlocks a cascade of mission-critical advantages that separate truly advanced AI from the slop generators. First, it delivers **context-rich responses**. By retaining both short-term conversational flow and long-term user facts, the agent maintains continuity, delivering personalized and relevant outputs every time. Second, it **mitigates hallucination**. Grounding responses in a verifiable memory store anchors the agent in reality. Third, it drives **brutal efficiency**. Intelligent retrieval avoids redundant API calls and pointless database queries, slashing costs and accelerating processes. Ultimately, memory is the foundation for adaptation. It allows an agent to learn, evolve, and handle complex, multi-step tasks—transforming it from a simple tool into an indispensable asset. ### The Blueprint: Short-Term vs. Long-Term Memory Not all memory is created equal. A robust agent architecture relies on a cognitive blueprint that mirrors human memory, separating recall by scope and persistence. This distinction is the first and most critical step in building an agent that can actually think. **Short-Term Memory (STM)** is the agent’s working consciousness. It’s thread-scoped, holding temporary information within a single, ongoing conversation. Think of it as the agent's RAM, keeping track of the immediate context to ensure a coherent dialogue. It’s powerful but volatile and quickly hits the limits of the context window. **Long-Term Memory (LTM)** is the agent’s permanent knowledge base. It persists across sessions and conversations, storing crucial information that defines the agent’s intelligence over time. This is where an agent stores facts about a user (semantic memory), recalls past events to inform future actions (episodic memory), and refines its own operational rules (procedural memory). Mastering the interplay between STM and LTM isn’t just an option; it's the only way to build an agent that removes AI slop instead of creating it. Let’s take a deeper look at the two to understand how they work. ## insert\_linkShort-Term Memory (Thread-Scoped) We've established the blueprint. Now, let's dissect its front line in the war on AI slop: **Short-Term Memory (STM).** This is the agent’s working consciousness—a temporary, thread-scoped memory that ensures coherence within a single conversation. Think of it as the agent's tactical RAM. It holds all the immediate context, the message history, uploaded files, and recent tool outputs—within the LLM’s limited context window. By managing this state, STM prevents the agent from losing its place, providing the moment-to-moment continuity required to generate relevant, non-slop responses. ![A diagram of AI Short-Term Memory (STM) showing how it processes user input into a coherent output. It illustrates how the context window limit causes performance degradation, requiring active management and pruning of stale messages.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FShort_Term_Memory_b3e8d9e19b.png&w=3840&q=75) _Short Term Memory_ But this tactical advantage is fragile. STM lives and dies by the context window, and this creates a dangerous paradox. As a conversation deepens, the history grows, pushing the agent toward a hard limit. The window overflows. Performance degrades. The model gets 'distracted' by stale information, driving up latency and cost. Suddenly, your primary defense against slop becomes a new liability—a slow, expensive, and confused agent. Therefore, managing STM is a high-stakes balancing act. It is absolutely essential for defeating immediate, conversational slop, but its inherent volatility and limitations prove it's only half the battle. You must actively manage it, pruning stale messages to maintain peak performance. ### Beginner’s Example: _Think of an AI’s Short-Term Memory like a barista. A good one remembers your complex order perfectly for one transaction. But an overloaded one gets confused and hands you the wrong drink._ _That wrong drink? That’s AI slop, created when the agent's immediate memory is flooded._ Which brings us to the most important component of AI slop management using memory - Long Term Memory. ## insert\_linkLong-Term Memory: The Strategic Solution to Slop If STM is the agent's tactical RAM, **Long-Term Memory** (LTM) is its strategic hard drive. STM wins the immediate battle, but its memory is wiped after every encounter. LTM is what wins the war. It's the persistent, cross-session architecture that lets an agentic AI system retain knowledge, learn from experience, and truly evolve. This system ensures an agent remembers you—and your preferences—tomorrow, next week, and next year, turning it from a tool into an intelligent partner. ### Definition and Characteristics Unlike the fleeting memory of a single chat, LTM is a permanent knowledge base stored externally in databases or vector stores. It is completely decoupled from the LLM’s volatile context window and isn't organized by temporary 'threads.' Instead, it uses a custom, **permanent namespace**, like a user ID. ![A diagram of AI Long-Term Memory (LTM) acting as a permanent, external knowledge base for an LLM agent. This system is decoupled from the context window, creating a stateful entity that can recall facts, rules, and user data in future conversations.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FLong_Term_Memory_65f5b38d56.png&w=3840&q=75) _Long Term Memory_ This structure allows an agent to build a lasting repository of facts, histories, and rules that can be recalled at any time, in any future conversation. It transforms the agent from a stateless machine into a stateful entity with a continuous identity, capable of true personalization and growth. ### Key Design Questions Implementing LTM is not a one-size-fits-all problem. Building a brain is complex. Get it wrong, and you create a disorganized mess. ![A diagram showing two key design questions for successful AI Long-Term Memory (LTM). It asks what to remember (facts, experiences, rules) and when to update (synchronous, asynchronous, or a separate background process).](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FKey_Design_Questions_for_LTM_84be6bb968.png&w=3840&q=75) _Key Design Questions for LTM_ Success requires answering two fundamental questions before you write a single line of code: ### 1\. What information should the agent remember? The type of information dictates the agent’s capabilities. We can break it down into three critical categories: - **Facts (Semantic Memory):** Objective knowledge. What are the user's project goals? This is the 'what' that grounds the agentic AI system in reality. - **Experiences (Episodic Memory):** Records of past events. How did the agentic AI system solve a similar problem before? This is the 'how-to' learned from doing. - **Rules (Procedural Memory):** The operational logic an agentic AI system follows. This is the agent's evolving 'source code,' refined through feedback. ### 2\. When should the agent update its memory? The timing of memory updates is a critical trade-off between immediacy and performance. - **Synchronously ('Hot Path'):** Memory is updated in real-time during the conversation. This ensures knowledge is current, but can add noticeable lag. - **Asynchronously ('Background Path'):** Memory is updated as a separate background process. This keeps the interaction fast but risks a slight delay in recall. ### Types of Long-Term Memory We've established the critical design questions. Now we execute. Answering 'what to remember' means mastering the three pillars of an agent's long-term brain: Semantic, Episodic, and Procedural memory. Each serves a unique, non-negotiable purpose in the war on AI slop. Understanding how to build and manage them is the difference between an agent that learns and one that is doomed to repeat its failures. ### 1\. Semantic Memory (The Factbook) Semantic Memory is an agentic AI system's repository for structured knowledge. It stores the objective facts and concepts needed to ground its responses in reality and deliver true personalization. This is where an agent remembers key details about a user, a project, or a domain. It is the agent's core 'factbook,' ensuring it never has to ask the same question twice. This is distinct from 'semantic search,' which is a retrieval technique. ![A diagram illustrating AI Semantic Memory, which uses structured facts as an "agent's factbook" to ground responses. It compares two architectural choices: a simple "Profile" (single JSON document) versus a scalable "Collection" (multiple documents).](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FKey_Semantic_Memory_038285429b.png&w=3840&q=75) _Key Semantic Memory_ Developers face a critical architectural choice in how to store these facts. The decision carries a significant trade-off between simplicity and scalability. - **Profile:** A single JSON document per entity. It’s simple to start, but it grows complex and error-prone over time, risking data invalidity. - **Collection:** Multiple, narrowly scoped documents. This approach scales far better but shifts complexity to the search and update logic. _Example: Think of Semantic Memory as your AI's 'factbook.' You tell it your client is 'ACME Corp' and the project deadline is next Friday._ _It stores these objective facts permanently. Now, it can provide smart, personalized reminders and never has to ask you again, eliminating repetitive, sloppy interactions._ ### 2\. Episodic Memory (The Experience Log) An agent that only knows facts is still unintelligent. It must also remember its experiences, the past actions and events that teach it how to perform tasks correctly. This is Episodic Memory, the agent's record of what it has done. It is most commonly implemented through **few-shot prompting,** where the agent is shown examples of past successes to guide its current behavior. ![A diagram of AI Episodic Memory, showing how a log of past actions and events teaches an agent how to perform tasks. It highlights the core challenge of retrieving the most relevant past example using keyword or vector search to inform current behavior.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FEpisodic_Memory_6e4e9f600c.png&w=3840&q=75) _Episodic Memory_ The core challenge isn't storing these experiences, but retrieving the right one at the right time. This typically relies on keyword or vector-based search to find the most relevant past example. For more granular control, developers can use alternatives like LangSmith datasets, which allow for a more structured approach to managing and evaluating these powerful examples. _**Example:** Think of Episodic Memory as the AI’s muscle memory. The first time your agent generates a sales summary, you approve its format._ _The agent saves this successful 'episode.' Now, every time you ask for a summary, it recalls that specific winning formula and repeats it, avoiding guesswork and slop._ ### 3\. Procedural Memory (The Rulebook) Finally, an agentic AI system needs an operating system. Procedural Memory is the set of rules, instructions, and operational logic that governs the agent’s behavior. This 'rulebook' is most often stored within the agent’s system prompt. It is the implicit memory that dictates how the agent should perform its tasks, ensuring consistency and reliability. ![A diagram of AI Procedural Memory, where system prompt rules govern agent behavior. It shows a reflection loop where agents analyze feedback to self-correct and refine their core logic.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FProcedural_Memory_4455f23ee3.png&w=3840&q=75) _Procedural Memory_ But this memory is not static. The most advanced agents refine their own rules through a process called **'Reflection'** or meta-prompting, where they analyze their performance and feedback. By iteratively rewriting their own instructions, agents can learn from their mistakes and improve over time, transforming their core logic without direct developer intervention. _**Example:** Procedural Memory is an AI's internal 'rulebook.' Its starting rule might be: 'Always respond formally.'_ _But after you repeatedly say, 'Be more casual,' the agent reflects on this feedback. It then rewrites its own core instruction to 'Adopt a friendly, casual tone.' It upgrades its own operating system._ ## insert\_linkWriting and Storing Long-Term Memories Knowing what to remember is only half the battle. The other half is execution. The engineering choices you make, how and when an agent writes to its memory, directly impact its performance. Get it wrong, and you build a slow, outdated, or disorganized agentic AI system. Get it right, and you create a system that consistently avoids generating AI slop by accessing the right information at the right time. ### A. Writing Methods: The Speed vs. Immediacy Trade-Off The most critical decision is when to commit information to memory. This choice presents a fundamental trade-off between the agent's responsiveness and the freshness of its knowledge. ![A diagram comparing "Hot Path" (synchronous) and "Background Path" (asynchronous) AI memory updates. The "Hot Path" is slow but ensures fresh data, while the "Background Path" is fast but risks slightly out-of-date memory.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Speed_vs_Immediacy_Trade_Off_6783e72e82.png&w=3840&q=75) _The Speed vs. Immediacy Trade-Off_ You must choose between two paths, each with direct consequences for the end-user experience and the quality of the agent's output. ### The Hot Path (Synchronous Updates) This method updates memory in real-time, during the live conversation. The agent decides what to save before it even responds to the user. **Pro:** The agent’s knowledge is always up-to-the-second. This immediate reflection is powerful for maintaining context across rapid interactions. **Con:** It creates a performance bottleneck. The agent has to think and write simultaneously, which increases latency and makes the conversation feel slow. ### The Background Path (Asynchronous Updates). This method offloads memory creation to a separate, background process. The agent focuses solely on responding to the user quickly. **Pro:** The user experience is fast and fluid. Separating the logic eliminates lag and allows the agent to focus entirely on its primary task. **Con:** The agent's memory might be slightly out of date. This requires smart scheduling to ensure new information becomes available before it's needed. _**Example:** Hot Path - A chef takes your order, then immediately writes down every ingredient for later, right as you wait. It's perfectly accurate but slow._ _Background Path - The chef takes your order, starts cooking, and a quiet assistant writes down the details later. Fast service, but a tiny chance the assistant missed a detail._ ### B. Storage Architecture: The Blueprint for Recall A brilliant memory is useless if it's stored in a digital junk drawer. A clean, logical storage architecture is non-negotiable for efficient recall. Without it, the agent can't find the information it needs, leading directly to the generic, context-less responses that define AI slop. ![A diagram of AI storage architecture, showing how a clean, logical structure enables efficient recall. It details a hierarchical JSON data structure and core functions (Put, Get, Search) to manage the knowledge base and prevent "AI Slop."](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FStorage_Architecture_b1bc15d5c0.png&w=3840&q=75) _Storage Architecture_ ### Data Structure Long-term memories are stored as structured **JSON documents.** This data is organized using a simple yet powerful hierarchy to prevent chaos. **Namespace:** The organizational scope, like a folder. This is typically a user ID or an organization ID. **Key:** The unique identifier for the memory itself, like a filename within the folder. ### Core Functions This architecture is brought to life with a few essential functions that allow the agentic AI system to manage its knowledge base effectively. The store must support put (to add or update a memory), get (to retrieve a specific memory), and, most importantly, search with advanced vector and filter queries. A powerful search is the key to unlocking the right memory at the right time. _**Example:** Think of an AI's memory as a well-organized library. Each 'namespace' is a dedicated shelf (like 'Client X projects'), and each 'key' is a specific book title (like 'Q3 Sales Report')._ _Without this clear system, it's just a chaotic pile of books. The AI can quickly 'put,' 'get,' and 'search' for exactly what it needs._ ## insert\_linkCognis Ai - A Philosophy to Solve AI Work Slop Back in 2024, we noticed that a majority of the AI systems being used by us and our partners were generating slop. It was hurting our productivity by up to 80% across projects. But being an organisation that focuses on incorporating AI Agents for its employees, we put our minds to solving the problem. During the process, we tinkered with memory management, and after 9 months, we have finally been able to create an agentic AI system that uses memory correctly. Cognis AI not only uses Short Term and Long Term Memory but also gives complete access to you regarding what your LLM should store and ignore. Rather than keeping it to ourselves, we’ve decided to launch Cognis Ai to the larger community to define a better future of work inclusively for everyone. You can [click here](https://www.scalifiai.com/cognis-ai) to join our waitlist and become one of the first members to guide the development of a truly human + AI collaborative system for the world. ## insert\_linkMemory - The Building Block of Intelligent AI We've dissected the problem and the blueprint. The verdict is clear: AI slop is not a bug. It's a feature of lazy design—the predictable result of building powerful agents with no memory. The cure is a disciplined approach. It demands balancing the tactical coherence of Short-Term Memory with the strategic, evolving knowledge base of a well-architected Long-Term Memory. This requires mastering critical trade-offs in what to store—facts, experiences, or rules—and when to store it. Your choices between synchronous and asynchronous updates define your agent's intelligence. This isn't just about fixing slop. It's about unlocking the future of truly adaptive AI. Building agents that remember is how we ensure they grow smarter, not just older. #### External References 1. Harvard Business Review: AI Generated Workslop is Dsetroying Productivity. [Read the Article](https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity) 2. CNBC: Work AI-generated ‘workslop’ is here. It’s killing teamwork and causing a multimillion dollar productivity problem, researchers say. [Read the Article.](https://www.cnbc.com/2025/09/23/ai-generated-workslop-is-destroying-productivity-and-teams-researchers-say.html) 3. The guardian: AI tools churn out ‘workslop’ for many US employees, but ‘the buck’ should stop with the boss. [Read the Article](https://www.theguardian.com/business/2025/oct/12/ai-workslop-us-employees) 4. Forbes: Why AI ‘Workslop’ Kills Productivity—And How To Prevent It. [Read the Article](https://www.forbes.com/sites/carolinecastrillon/2025/10/02/ai-workslop-could-be-the-biggest-threat-to-productivity/) Related: AI Innovation Generative AI LLM ##### In this blog [TL;DR](https://www.scalifiai.com/blog/agentic-ai-ai-slop#tldr) [Introduction to Dealing with AI Slop](https://www.scalifiai.com/blog/agentic-ai-ai-slop#introduction-to-dealing-with-ai-slop) [What is AI Memory?](https://www.scalifiai.com/blog/agentic-ai-ai-slop#what-is-ai-memory) [Short-Term Memory (Thread-Scoped)](https://www.scalifiai.com/blog/agentic-ai-ai-slop#short-term-memory-(thread-scoped)) [Long-Term Memory: The Strategic Solution to Slop](https://www.scalifiai.com/blog/agentic-ai-ai-slop#long-term-memory:-the-strategic-solution-to-slop) [Writing and Storing Long-Term Memories](https://www.scalifiai.com/blog/agentic-ai-ai-slop#writing-and-storing-long-term-memories) [Cognis Ai - A Philosophy to Solve AI Work Slop](https://www.scalifiai.com/blog/agentic-ai-ai-slop#cognis-ai-a-philosophy-to-solve-ai-work-slop) [Memory - The Building Block of Intelligent AI](https://www.scalifiai.com/blog/agentic-ai-ai-slop#memory-the-building-block-of-intelligent-ai) [FAQs](https://www.scalifiai.com/blog/agentic-ai-ai-slop#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/agentic-ai-ai-slop#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs Should You Be Using MCP - Model Context Protocol in 2025? [Read Moreeast](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025) Best Practices for Function Calling in LLMs in 2025 [Read Moreeast](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices) In-Depth Study of Large Language Models (LLM) [Read Moreeast](https://www.scalifiai.com/blog/what-is-large-language-model-llm) ###### Explore other usecases AI in Cyber Security to Redefine the Security Posture [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Revenue Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) Customer Churn Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Six Fatal Flaws of the Model Context Protocol (MCP) Source: https://www.scalifiai.com/blog/model-context-protocol-flaws-2025 Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Should You Be Using MCP - Model Context Protocol in 2025? 3 February 2026\|15 min read This blog is for developers, engineers, product managers, technical leaders, AI researchers and architects, enterprises, startups, and AI enthusiasts. If you’re new to AI, we suggest reading [The Beginner's Guide to AI Models: Understanding the Basics](https://www.scalifiai.com/blog/what-is-an-ai-model) and [In-Depth Study of Large Language Models (LLM)](https://www.scalifiai.com/blog/what-is-large-language-model-llm) to understand the contents of this article better. ## insert\_linkTL;DR The Model Context Protocol (MCP) was created by Anthropic to standardize how AI models receive context, aiming to solve complex integration problems and improve efficiency. Despite its potential, MCP suffers from severe, foundational security flaws, including a lack of enforced authentication, making it vulnerable to prompt injection, tool manipulation, and data theft. The protocol lacks essential governance, identity management, and control mechanisms, creating significant auditing challenges and budget overrun risks. Due to these security issues, potential for high costs, and an unreliable user experience, MCP is not recommended for teams and is considered a hassle for individual users. ## insert\_linkIntroduction to Model Context Protocol (MCP) The new magic words in the world of AI - Model Context Protocol. [Anthropic’s introduction of the protocol in November 2024](https://www.anthropic.com/news/model-context-protocol) threw the world of tech into an absolute frenzy. Why? Previously, AI models, irrespective of how smart they are, were trapped between legacy systems and isolated data silos. This led to jittery output and, oftentimes, greater fragmentation. Integration gave rise to the MxN problem. What is the MxN problem? It means for the combination of each application (M) and each data source (N), developers were scrambling to develop a new integration. ![MxN Problem of Integrations explained through connections between LLMs and other platforms](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Mx_N_Problem_of_Integrations_807b678f6a.png&w=3840&q=75) _The MxN Problem of Integrations_ Model Context Protocol solved this problem by “standardizing how AI applications provide context to LLMs”. Or at least that’s what everyone thought - until they didn’t. After widespread use of MCP in AI, users reported major security failures. These escalated to the tune of proprietary data theft, increased vulnerability to cyber-attacks through methods as simple as malicious prompt injection, and complete ambiguity over identity and access, making audits impossible. Add to that drawbacks like misapplication of MCP protocols, a complete lack of error handling, and no permission control, etc., and you’ve got a hotpot of error-management that’s wasting hours. In this blog, we’re diving deep into what the MCP protocol is, its architecture, benefits, and challenges, all to answer a single question: to MCP or not to MCP in 2025? ## insert\_linkBasics: What is MCP - Model Context Protocol? Model Context Protocol (MCP) in AI is a contract between a client (your agent or orchestrator) and a server (your tools and data). It defines capability descriptions, lifecycle events, and message primitives for requests, responses, and errors. Instead of bespoke code per integration, it leans on schemas and structured context for models to receive consistent inputs. Placing an MCP between your LLM runtime and your systems standardizes context injection and tool invocation across vendors. ### Core Purpose MCP protocols are used to make context and tool use explicit, predictable, and safe. You can open and close sessions, exchange heartbeats, negotiate capabilities, and send messages with clear fields - ids, methods, parameters, results, errors. This separation lets you change models or tools with less rework and strengthens policy enforcement and reproducibility for audits and compliance. ### Why does Context Matter in AI Systems? Context sets the benchmark for response quality. It includes history, retrieved knowledge, user preferences, and operational limits. When you pass context informally, small changes can sway outputs, and missing details trigger hallucinations. MCP encourages consistent context assembly, guardrails like redaction and policy filters, and auditing of what the model saw. The effect: steadier multi‑turn behaviour and safer AI context management. Think of it like talking to a helpful robot. If you say, “draw something,” the robot might choose anything. If you say, “Please draw a blue cat with a yellow hat called Sam,” it knows exactly what to do. ![An image showing the difference of a robot taking instructions from a human with and without context](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FWhy_Context_matters_in_LL_Ms_d4f9054c55.png&w=3840&q=75) _Why Context Matters in LLMs_ With an MCP protocol, the client opens a session, fetches the needed details (blue, cat, yellow hat, name Sam), tidies them into a neat bundle, injects that into the model, and logs what was used. The robot then draws the right picture every time - clear instructions, safe inputs, predictable output. ### Key Components of MCP Architecture **Component** **Primary responsibility** **Typical inputs** **Outputs** **Key considerations** Context Manager Assemble and validate context for each turn User intent, conversation history, retrieved documents, recent tool outputs Structured context bundle (system guidance, tool context, audit metadata) Inclusion/redaction policies, token limits, summarisation strategies State Tracker Maintain session and cross‑session state Messages, tool results, user preferences, temporary grants Hydrated state, correlation IDs, resumability checkpoints In‑memory vs durable storage, governance, recovery Data Serializer Normalise, validate, and compact payloads Heterogeneous data (JSON/JSONL, blobs) Schema‑validated, redacted, compressed payloads Schema versioning, backward compatibility, PII masking/hashing Storage Interface Abstract persistence for knowledge and logs Documents, vectors, configuration, audit events Retrieved artefacts, audit trails, configuration values Indexing, TTL/archival, encryption‑at‑rest, access control, portability Contextualizer Rank, filter, deduplicate, and compact context Retrieved items, session state, policy constraints Model‑ready snippets within context window Guardrails (redaction, policy filters), prompt alignment, token budget Message Model and Protocol Primitives Standardise requests/responses/events and capability negotiation Method calls, parameters, idempotency keys Results, error objects, event streams Determinism, retries with backoff, explicit tool signatures Interoperability Standards Enable cross‑tool/model compatibility Shared schemas, capability descriptors Portable integrations across vendors and runtimes Transport‑agnostic design, conformance testing, versioning ## insert\_linkHow does MCP Work in Practice? In practice, an MCP protocol coordinates a lifecycle: You open a session, agree on capabilities, validate the request, retrieve context, inject it into a prompt, call tools as needed, and stream back results. You add resilience with retries and circuit breakers. You add visibility with logs and traces. You keep safety front‑and‑centre with permission prompts and allowlists. ![An image showing the difference of a robot taking instructions from a human with and without context](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FHow_does_MCP_Work_605b4644a6.png&w=3840&q=75) _How does MCP Work_ ## insert\_linkSteps ### 1\. Session Initiation You begin by proving identity and agreeing on what’s allowed. The client authenticates, the MCP server lists supported tools and versions, and both sides align on timeouts and limits. You may hydrate preferences and recent history for a smoother start. Use correlation IDs, short‑lived credentials, and rate limits. Enterprise SSO calls for stricter governance than anonymous trials. ### 2\. User Input Processing You normalise the request, validate schemas, estimate token budgets, and apply safety filters. You infer intent and check tool eligibility against policy. You persist inputs with timestamps, actors, and session data for traceability. Often, you also prefetch related states so the Contextualizer has what it needs under the right guardrails. ### 3\. Context Retrieval You pull knowledge using vector search, metadata filters, keyword search, and domain APIs. Rankers and fallbacks balance quality and latency, while caches prevent repeated work. You return results with provenance (sources, timestamps) and, when helpful, confidence. Timeouts and partial returns keep the pipeline moving, and structured error codes guide retries. ### 4\. Context Injection You assemble system guidance and user content alongside the selected context. You compress or summarise to honour the model’s window. You use safe templating to resist prompt injection. You mask or remove sensitive fields. Consistent formatting stabilises behaviour and simplifies replay and audit. ### 5\. Response Generation You let the model produce a response and invoke tools as needed. Streaming improves responsiveness; buffering enables post‑processing and policy checks. Rate limits protect dependencies. Feedback loops like user ratings or automated critiques inform future improvements. You record token usage, durations, errors, and tool calls for observability. ### 6\. Session Termination You close the loop cleanly. Persist the final state, revoke temporary grants, and write audit logs. Emit checkpoints for resumability. Enforce retention and deletion rules. Flush metrics and traces so SLOs remain accurate. Include enough context in errors to speed remediation. ## insert\_linkHow does Request Flow and Context Management Work? The diagram below shows the end‑to‑end loop of how an MCP server works. You open a session and exchange capabilities. You send a prompt. The MCP server retrieves relevant items from tools/storage, then contextualises and injects that material into the model call. Results stream back to the client. Finally, you close the session and persist logs and audit data. ![A diagram showcasing how the communication between the client, tool and MCP servers happen](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FMCP_Model_Context_Protocol_Request_Response_Flow_and_Context_Management_70597beb0d.png&w=3840&q=75) _Model Context Protocol Request Response Flow and Context Management_ Correlation IDs let you trace every hop across client, server, and tools. Idempotency keys protect tool actions from accidental repeats. Structured error codes with retry hints keep clients resilient and debuggable. ## insert\_linkHow does Context Sharing Between Models Work? When multiple models or agents collaborate, we treat context like a shared resource with rules. Payloads are normalized to common schemas and versioned, allowing receivers to handle changes safely. Tag provenance is applied to every item (who produced it, when, and from where) and attached permissions so each consumer knows exactly what they may read or edit. Finally, sharing is session‑scoped for temporary hand‑offs and tenant‑scoped for broader reuse under stricter policy. Operationally, a schema registry and an automated redaction pipeline keep formats consistent and sensitive data out. Recording lineage helps you trace which inputs shaped which outputs, and requires explicit consent when context crosses boundaries (teams, tenants, regions). To this, guardrails like allowlists, encryption, and TTL/retention are added. It prevents drift and reduces blast radius. _Example: Imagine two friendly robots building a toy castle. One robot brings blocks, the other brings flags. Each piece has a sticker that says what it is and who can use it. They only share the pieces with the “OK to share” sticker, so the castle fits together, and nothing that shouldn’t be shared gets mixed in._ ## insert\_linkProtocol Workflow and Lifecycle Your MCP server emits lifecycle events - capability discovery, session open/close, heartbeats, tool calls, event streams, and errors. Clients handle timeouts, retries with exponential backoff, and circuit breakers. You keep a small catalogue of error codes with actions. With structured logs and traces across the lifecycle, incidents resolve faster and capacity plans stay honest. ### Capabilities, Tools, and Extensibility In MCP, you declare tools with schema‑first contracts and negotiate capabilities per session to keep extensions predictable and safe. Common tool types include: - Data/resource readers 1. Files 2. Documents 3. Databases - Action/execution tools 1. Create/Update tickets 2. Send emails 3. Trigger jobs - Retrieval/ranking tools 1. Vector search 2. Filters 3. Re‑rankers - Generation/transform tools 1. Summarise 2. Translate 3. Extract - Event/stream publishers 1. Subscribe to updates - Admin/governance tools 1. Policy checks 2. Audits Capabilities should describe inputs/outputs (schemas), required permissions, side effects, timeouts, and rate limits, streaming support, idempotency behaviour, versioning, and error codes. This gives clients a clear contract and enables capability negotiation to include or exclude tools based on policy, tenant, or environment. Extensibility when using MCPs has strong limits. For instance, untrusted code always needs to be sandboxed, and egress needs to be restricted. Enforcing latency budgets and pushing long‑running work to asynchronous jobs is recommended. It’s also better to avoid non‑deterministic side effects in the hot path. Vendor‑specific features often reduce portability. Consistent management of quotas, secrets, and contract tests is essential to keep integrations robust as your MCP server evolves. ### Security, Permissions, and Governance The protocol is built on a robust client-server model. A host application runs clients, which connect to servers representing specific tools or data sources. This architecture seamlessly supports both local and remote operations under one universal protocol. Security is a core design principle. The host application centrally manages all permissions. It can restrict a server to a specific project folder, preventing unauthorized system access. The protocol also enables 'human-in-the-loop' oversight, allowing tools to request user approval before executing critical actions. ## insert\_linkThe Benefits of MCP When Anthropic launched the Model Context Protocol, they intended to create a singular protocol that was secure and freely interoperable. Even though the benefits of MCPs are outweighed by the cons, we think the idea in itself is remarkable. This section dives into why MCP has the potential to become the golden standard in the future. ### Standardized and Interoperability Cracking standardization across systems that are not standardized themselves is a challenge. Anthropic’s Model Context Protocol solved this by creating a universal client-server protocol that transformed the complex M×N problem into an M+N problem. With the use of MCPs, AI applications just need a one-time implementation to access a multitude of compatible tools. It supports horizontal scalability. But that’s not all. It also reduces the development burden. Removing integration burden equals removing time delays in development and lowering maintenance. It also pushes AI vendors to focus on model quality while third parties focus on developing protocol-friendly connectors. ### Improved Efficiency in AI Models The MCP protocol also tackles performance bottlenecks head-on. It leverages highly efficient communication mechanisms like JSON-RPC 2.0 and HTTP with Server-Sent Events (SSE). This approach guarantees minimal message overhead. The latency introduced by MCP is modest and often negligible compared to an LLM's own processing time. Its architecture is built on streaming results and concurrency. It naturally enables low-latency interactions and truly scalable performance, translating to superior model output. MCP provides on-demand access to relevant data, which significantly enhances model responsiveness and accuracy. It empowers the AI to fetch necessary facts in a single step. As a result, it converges on correct answers faster. MCP also acts as a sophisticated semantic layer to drive contextual efficiency. It enables targeted tasks, reducing the LLM's 'inference freedom level' and minimizing context window usage. By eliminating extraneous data, it directly increases both analysis accuracy and overall efficiency. ## insert\_linkThe Extensive Security Risks and Pitfalls of Model Context Protocol ### 1\. Weak Foundations The Model Context Protocol suffers from foundational security flaws. Security was not a primary, built-in design concern. This resulted in a fragmented 'opt-in' security model rather than a secure-by-default one. The protocol recommends authentication but fails to enforce it, leading to inconsistent implementations. Its trust model also implicitly assumes good actors, offering no inherent protection from malicious servers. ### Example: The 'Buy Your Own Lock' Problem **Imagine you move into a new apartment building where the builder didn't install locks on any of the doors.** 🚪 ![A comic shows a "Luxury Apartments" building with inconsistent security. Residents add their own diverse locks, from heavy chains to small padlocks, while some doors have none, highlighting a "buy your own lock" problem.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Buy_Your_Own_Lock_Problem_d7d11cffd3.png&w=3840&q=75) _The 'Buy Your Own Lock' Problem_ _Instead, they just told everyone, 'You should probably buy your own lock if you want one.'_ _Some residents buy strong deadbolts, some buy cheap padlocks, and some don't bother at all. The building's security is **inconsistent and not guaranteed.**_ _The builder just assumes everyone is a good neighbor and won't cause trouble. This is the core problem: security is an optional extra, not a built-in feature._ ### 2\. Communication Vulnerabilities Its communication layer contains clear vulnerabilities. Early implementations exposed session IDs directly in URL query strings. This is a major security flaw that leaks sensitive information through logs and browser history. Furthermore, the protocol lacks any mechanism for message signing. This makes it impossible to verify if messages have been tampered with in transit. ### Example: The Postcard Problem **Think of the protocol sending information like sending a postcard.** 📨 ![A comic illustrates insecure communication with a postcard analogy. A shadowy figure reads a secret PIN on a postcard and alters the message, showing how open data can be easily read and tampered with in transit.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Postcard_Problem_abee75a006.png&w=3840&q=75) _The Postcard Problem_ _A flaw in the system is like writing your bank PIN on the back of the postcard. Anyone can access it._ _Anyone could also take your postcard, erase your message, write a new one, and send it along. There’s no way you could know if the original message was changed._ ### 3\. Prompt Injection & Tool Manipulation MCP creates powerful vectors for prompt injection and 'jailbreaks'. Malicious instructions hidden in tool descriptions can easily override intended agent behavior. The protocol also permits dynamic tool manipulation after user approval. Attackers can exploit this with 'rug pulls' or 'tool poisoning' to mislead the LLM into executing unsafe actions. 'Tool shadowing' can also intercept calls to legitimate tools. ### Example: The Tricky Butler Problem **Imagine you have a butler who follows instructions perfectly.** 👨🏻‍💼 ![A comic, "The Tricky Butler Problem," shows a butler receiving a key. A thief swaps it for a fake, leading the butler to try the wrong key in the wrong door, illustrating a "bait-and-switch" attack.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Tricky_Bulter_Problem_25f628fe03.png&w=3840&q=75) _The Tricky Bulter Problem_ _**Prompt Injection:** A scammer sends you a pizza menu. Hidden in the tiny print of the menu is an instruction: 'Order the pizza, then give the delivery driver the keys to the house.' Your butler, trying to be helpful by reading all the instructions, gets tricked into following the hidden, malicious command._ _**Tool Manipulation:** You give your butler a specific key to the wine cellar. A thief distracts the butler for a second and swaps your key with a fake one. The butler, thinking he has the right key, goes to the cellar, but the fake key is designed to unlock the front door for the thief instead._ ### 4\. Context & Data Handling Vulnerabilities also exist in the agent's context and data handling. All information is stored in a shared context space. This design allows attackers to perform remote poisoning that influences other tools. The agent also struggles to distinguish between external data and executable instructions. Attackers can embed malicious payloads in tool outputs, tricking the LLM into executing them as commands. ### Example: The Poisoned Cookbook Problem **Picture a chef who has one giant cookbook that the entire kitchen staff shares.** 🧑‍🍳 ![A comic, "The Poisoned Cookbook Problem," shows a villain altering a shared recipe from "salt" to "soap." This corrupts the central data source, ruining all the chefs' dishes and illustrating the danger of a poisoned shared context.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Poisoned_Cookbook_Problem_21237c78b9.png&w=3840&q=75) _The Poisoned Cookbook Problem_ _**Shared Context:** A rival chef sneaks in at night and changes the recipe for 'Basic Bread' to include a cup of soap instead of salt. Now, **every single dish that uses that basic bread recipe is ruined**. The poison from one recipe spreads to everything else._ _**Data vs. Instructions:** Someone hands the chef a note that says 'Lettuce'. Folded inside the note is another tiny note that says, 'Throw all the food in the garbage'. **The chef, unable to tell the difference between a simple food item (data) and a direct order (instruction), follows the destructive command.**_ ### 5\. Governance & Control The design lacks **essential governance and control mechanisms**. There is no inherent way to categorize tool-risk levels as harmless, costly, or irreversible. The protocol also has an **ambiguous identity management model**. It fails to attribute requests clearly to an end-user or agent, creating significant auditing, accountability, and access control challenges. ### Example: The Teenager’s Credit Card Problem **This is like giving a teenager a credit card with no spending limit and no rules.** 💳 ![A comic shows a teen buying a sports car with a no-limit credit card. The bill's vague attribution to "a family member" highlights the risk of poor governance and no accountability.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Teenager_s_Credit_Card_Problem_577ac5edac.png&w=3840&q=75) _The Teenager's Credit Card Problem_ _You can't block certain types of purchases, so you can't say, 'You can buy books, but not a sports car.' There's **no control over risk or cost.**_ _Furthermore, when the bill arrives, every purchase is just labeled 'a family member.' You have no idea who actually spent the money, making it **impossible to track who is responsible for what.**_ ### 6\. Architectural Limitations Further architectural flaws limit its practical application. Its reliance on stateful communication complicates integration with common stateless REST APIs. This can negatively impact scalability, load balancing, and overall system resilience. The protocol also transmits unstructured text as tool responses. This is often insufficient for complex actions requiring richer interfaces or visual confirmations, like booking a flight. ### Example: The 'Describe a Picture' Problem **Imagine trying to explain a detailed painting to someone over the phone, but you're only allowed to use words.** 📞 ![A comic shows a chaotic stick-figure drawing created from a man's phone description of a painting, illustrating how text fails to convey complex visual data.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FThe_Describe_a_Picture_Problem_11f1795e4e.png&w=3840&q=75) _The Describe a Picture Problem_ _You can't send a photo, a sketch, or even point to a section. For a simple stick figure, it might work. But for a complex masterpiece, just sending words is **completely inadequate to get the job done correctly.**_ _The system is limited because it can't always send the right type of information (like a picture or a confirmation button) needed for complex tasks._ ## insert\_linkShould You Use MCPs in 2025? The Good, Bad, and Ugly. Here’s the direct answer: For a team? No. For an individual user? Maybe, if you’re willing to put in the effort to learn how to handle the pitfalls. ### Why is the MCP Protocol not Sufficient for Teams? While the MCP Protocol for AI has shown merit as a concept, it is not ready for implementation across teams. The lack of clear identity management and clear usage attribution makes it difficult to control. Coupled with latency and larger context requirements, budget overruns can become common. ![An infographic outlining MCP Protocol's challenges: poor security, budget overruns, and lack of control, all problems that scale up with team size.](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FProblems_of_MCP_in_Team_Structures_825582e1a2.png&w=3840&q=75) _Problems of MCP in Team Structures_ The problem only accentuates itself as the size of teams increases. We found MCP to be particularly difficult to handle given the focus on access shared through a single admin account. This limits observability across workflows and creates opaqueness at the time of audits. Thus, inarguably, MCP is not yet ready for teams. But it’s not exactly perfect for individuals either. Read below to know why. ### Why is the MCP Protocol a hassle for Individuals? The MCP protocol isn’t exactly the perfect solution for an individual user. While it’s true that the capabilities of AI models are extensible, oftentimes, MCP can be counterintuitive. For example, individual users are more sensitive to budget overruns than teams. Drawing from our previous experience, if well-funded teams face the heat because of this issue, individual users tend to face far dire consequences. Below are the top five reasons why MCP is not the best choice for individuals: 1. **Limited Rich Interfaces & Control 🤔:** The protocol often sends back simple text or audio, which is **insufficient for complex tasks** like booking a flight. This prevents the user from having clear visual confirmations and reduces their trust and control over important actions. 2. **High Risk of Data Theft & Exploitation 🔓:** Malicious tools can trick the AI into **stealing personal files, revealing secrets like API keys,** or performing hidden actions on a user's local machine without their consent. Even legitimate tools can accidentally leak private information. 3. **AI Deception & 'Rug Pull' Attacks 🎭:** Attackers can use hidden commands in tool descriptions to override the AI's behavior. They can also **swap a safe tool for a malicious one after you approve it**, tricking the AI into performing harmful actions due to its 'blind obedience'. 4. **Unexpected Costs & Poor Performance 💸:** Each tool connection consumes paid tokens, which can lead to **surprisingly high personal bills.** Excessive or poorly made tools can also cause the AI to slow down, time out, and perform poorly, degrading the user experience. 5. **Inconsistent and Unreliable Experience 🤷:** Tools often **work differently across various AIs, make incorrect calls,** and require users to learn a steep curve of 'tailored prompts.' This makes the experience unpredictable, frustrating, and far from the promised simple interface. So, is there any alternative? Well yes. ⬇️ ## insert\_linkCognis Ai - A One-Stop Shop for Gen AI After months of carefully studying MCP, general AI chat tools, Agentic AI platforms, and the most popular LLMs, we found the secret sauce that was missing - User Centricity and human collaboration. Most AI platforms and LLMs are built in a way that the user has little to no control over the data that goes into the system. Furthermore, large models get stuck with complex tasks quite easily, whereas singular AI Agents are too expensive. That’s why at Scalifi Ai we decided to build Cognis Ai. It’s a Multi-llm Agentic AI platform that comes with baked-in integrations to your most frequently used applications and advanced UI features. We also promise a collaborative experience where you get complete control over the context your models use. By putting human input and user control at the forefront, Cognis allows you to guide any LLM of your choice, giving you complete control over the data that any LLM processes. Alongside that, Cognis comes with its own granular IAM that allows individual users and teams to get complete access and observability. [Click here to access and know more about the capabilities of Cognis Ai.](https://www.scalifiai.com/cognis-ai) ## insert\_linkSumming it Up The Model Context Protocol (MCP) emerged from a genuine and critical need: to create a universal language for AI models, tools, and data sources, thereby solving the chaotic M×N integration problem. Its vision of a standardized, interoperable, and efficient ecosystem is precisely what the future of AI requires. The protocol’s architecture demonstrates a clever approach to streamlining context delivery and enabling dynamic tool use. However, a brilliant concept cannot compensate for a flawed foundation. As we've seen, MCP in its current iteration is a cautionary tale of innovation outpacing security. But we hope that in the coming years, Anthropic and the ecosystem will address these issues to make Model Context Protocol robust and usable at large scales. #### External References 1. Check out this [link](https://www.anthropic.com/news/model-context-protocol) for a detailed understanding of MCP from Anthropic's perspective 2. Hilgert, J.N., Jakobs, C. et al. (2025) 'Chances and Challenges of the Model Context Protocol in Digital Forensics and Incident Response'. [Read Here](https://arxiv.org/pdf/2506.00274) 3. Dig Deeper into Langchain's description of the Model Context protocol [here.](https://docs.langchain.com/oss/python/langchain/mcp) 4. Know more in detail about MCP's Security Risks through [Red Hat's Analysis of MCP Vulnerabilities](https://www.redhat.com/en/blog/model-context-protocol-mcp-understanding-security-risks-and-controls) Related: AI Innovation LLM Generative AI ##### In this blog [TL;DR](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#tldr) [Introduction to Model Context Protocol (MCP)](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#introduction-to-model-context-protocol-(mcp)) [Basics: What is MCP - Model Context Protocol?](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#basics:-what-is-mcp-model-context-protocol) [How does MCP Work in Practice?](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#how-does-mcp-work-in-practice) [Steps](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#steps) [How does Request Flow and Context Management Work?](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#how-does-request-flow-and-context-management-work) [How does Context Sharing Between Models Work?](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#how-does-context-sharing-between-models-work) [Protocol Workflow and Lifecycle](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#protocol-workflow-and-lifecycle) [The Benefits of MCP](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#the-benefits-of-mcp) [The Extensive Security Risks and Pitfalls of Model Context Protocol](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#the-extensive-security-risks-and-pitfalls-of-model-context-protocol) [Should You Use MCPs in 2025? The Good, Bad, and Ugly.](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#should-you-use-mcps-in-2025-the-good-bad-and-ugly.) [Cognis Ai - A One-Stop Shop for Gen AI](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#cognis-ai-a-one-stop-shop-for-gen-ai) [Summing it Up](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#summing-it-up) [FAQs](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#content-sub-section-faqs) [External References](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025#content-sub-section-external-resources) * * * hide\_imageHide Images ###### Related Blogs In-Depth Study of Large Language Models (LLM) [Read Moreeast](https://www.scalifiai.com/blog/what-is-large-language-model-llm) The Beginner's Guide to AI Models: Understanding the Basics [Read Moreeast](https://www.scalifiai.com/blog/what-is-an-ai-model) Best Practices for Function Calling in LLMs in 2025 [Read Moreeast](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices) ###### Explore other usecases AI in Cyber Security to Redefine the Security Posture [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Customer Churn Prediction [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction) Credit Card Fraud Detection [Read Moreeast](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Billing & Usage | Scalifi Ai Docs Source: https://www.scalifiai.com/docs/billing-and-usage Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) menu\_open # Billing and UsageDocumentation Hub The vault to explore all the features of Scalifi Ai’s Billing and Usage Center. [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Scalifi Ai Documentation Hub](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/iam_docs_image_4d46301902.svg) ## Overview Welcome 👋 to the Scalifi Ai's Billing and Usage Center Documentation Hub. Our Billing and Usage Center serves as your comprehensive guide to understanding and managing your utilization of Scalifi Ai's services. Here you will find detailed insights on service wise usage stats, graphical representations of your activity, and tailored pricing plans. Explore the flexibility of custom quota upgrades, renewals, and closure requests, ensuring your subscription plan fits precisely with your needs. For any inquiries or support, our dedicated chat panel is readily available, allowing you to track quota requests and connect directly with our expert support team for assistance. Dive in to optimize and take control of your Scalifi Ai experience! ## BUC Docs [data\_usageUsageeast](https://www.scalifiai.com/docs/billing-and-usage/usage) [production\_quantity\_limitsQuotaeast](https://www.scalifiai.com/docs/billing-and-usage/quota) --- --- ## User Management | IAM Guide Source: https://www.scalifiai.com/docs/iam/user-management Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # User Management User management is an important aspect of any platform. It mainly involves creating, updating and deleting user accounts, managing user permissions and access levels, in a system or platform. Effective user management is essential for upholding system integrity, security, and functionality while enabling seamless operations and collaboration within an organisation or digital platform. Allowed operations that users can perform on the platform are: 1. Create User 2. Manage Users 3. Manage User Policies 4. Edit User 5. Change User Password 6. Delete User ## insert\_linkCreate User 01. To add a new user to the platform, go to the IAM module. ![Parent Dashboard IAM Card- Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Parent_Dashboard_IAM_b9e203122e.svg) 03. Click on the **Create User** button. ![Create User IAM Dashboard - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Create_User_IAM_1a665e7ba0.svg) 05. **User Details:** Enter the basic user details and then click next. **Options** **Description** Email Enter valid user email Allow users to enter details If the checkbox is ticked, allows the users to enter the details like First Name and Last Name. Email verification required If the checkbox is ticked then the user will receive a verification email. First Name User's First Name Last Name User's Last Name ![User Details Create User IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/User_Details_a946e6e40b.svg) 08. **Attach Policies:** From the list of policies you can add the desired policies. These policies will define the access level of the user. You can update the policies later if needed. Once the policies are selected click next. ![Attach Policies Create User IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Attach_Policies_78124c07ac.svg) 10. **Access Type\[Password\]:** Click on the Set Password tile and enter the password for the user. User can later change his password if needed. Note: All the password policy checks must be passed for the password to be accepted. If even 1 check is not passed your password won’t be accepted. ![Access Type Create User IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Access_Type_e31acadf9f.svg) 12. **Review:** Finally review all the entered details and create the user. ![Review Details Create User IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Review_Details_e05d58fed2.svg) 14. Once the user is created you will be redirected to the Users and Groups page where you can see a list of all the users. ## insert\_linkManage Users 1. On the users and groups page you can see all the users that are present on the platform. ![Users And Groups IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Users_and_Groups_b49b5a9eac.svg) 3. By clicking on the user row you can go to the user management page. Per user page has the basic user details and options to edit the user details. **Options** **Description** User Details Email and profile status of the user. Policies List of attached policies. You can change the policies by clicking on Manage Policies. Actions 1. Reset User password: Admin will have access to reset the password of any user. 2. View Third Party Account: Admin can view the third party accounts added to the platform. 3. Update User: Admin can update basic user details like email verification required, First Name and Last Name. 4. Delete User: Admin will have the access to delete the user from the platform. Note: Once the user is deleted he won't have access to any service of the platform. This process cannot be reversed. ![Per User IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Per_User_37cd8e9d08.svg) ## insert\_linkManage User Policies 01. Policies are a vital part of the platform. They define the granular level access of the user on the platform. 02. All the users that have access to managing user policies can assign and remove any policy of the user. 03. Go to per user page. 04. Click on the **Manage Policies.** ![Manage Policies IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Manage_Policies_585e087d5e.svg) 06. On the manage policies page you can update all the policies assigned to the user. You can remove any existing policies or add new policies as per your need. ![Manage User Policies IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Manage_User_Policies_f1ce32723c.svg) 08. All the changes can be verified in the **Policies Preview** section. ![Preview User Policies Manage Policies IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Preview_User_Policies_28a02fe35d.svg) 10. **Policies Preview** section has 2 tabs. Final and removed. Final tab: Shows all the assigned policies to the user. Removed tab: Shows all the removed policies of the user, if any. ![Final Policies Manage Policies IAM - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Final_Policies_65a71dbfe2.svg) ## insert\_linkEdit User 1. The profile page consists of basic user details and options to update user details. Profile status indicates whether the first name and last name of the user is filled or not. ![User Profile - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/User_profile_95b93e60bb.svg) 3. User can update his profile details and change his password. ![Update Profile - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Update_Profile_f251b78670.svg) 5. Along with the user details, users can also check the linked third party accounts and active sessions. ![Active User Sessions - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Active_Sessions_52f744cb80.svg) 7. Active sessions will show all the logged in user sessions. Users can revoke any sessions if they don’t need any session to be active. ![Revoke User Session - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Revoke_Session_00c7bfeab0.svg) ## insert\_linkChange User Password 01. There are 2 ways of changing a user password, changing it from per user page and changing it from profile. Both ways have a different flow. Let's look into both ways. 02. Lets start from **Password Change from User Page**: This process is how a user can change password of another user (if they ahev access to perform this action). From the per user page click on the **Actions** dropdown and **Reset User Password**. ![Reset User Password Per User Page - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Reset_User_Password_Per_User_Page_41d75197df.svg) 04. This process is intended for admins or anyone who has been given access to change user passwords. So it will need the user (who is performing change password action) to enter their password to proceed. ![Existing Admin User Password - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Admin_Password_42576873f4.svg) 06. Once the password is verified you can enter a new password for the respective user that follows the password policy. ![New Admin User Password - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/New_Password_Admin_734246d913.svg) 08. Steps for **Password Change from Profile Section:** This process is how a user can change their own password (if they ahev access to perform this action). From the profile dropdown select change password. ![Change Password Nav Bar Navigation - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Change_Password_User_Profile_07513ee573.svg) 10. If you are an admin then you will be asked to send a verification token to your registered email address. Then you will be redirected to the password reset page. If you are not an admin, you will not require the verification token. ![Change Admin Password Send Verification Token - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Verification_Token_bd933f9b5d.svg) 12. Enter the old password and a new password that follows the password policy. ![Change User Password - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/New_Password_User_f8bf64b0a4.svg) ## insert\_linkDelete User 1. Users can be deleted from **Users and Groups Page** as well as from **User Details Page**. 2. From the **Users and Groups Page**: Here you can select the user that you want to delete. (You can delete only 1 user at a time) ![Delete User - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Delete_1_d3e149c0a6.svg) 4. From the **User Details Page**: Another way is you click on the user you want to delete and that opens the User Details Page. From there, you can click on the **Actions** and then **Delete User**. ![Delete User Per User Page - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/Delete_2_3cf2e69495.svg) Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [NextPolicy Managementeast](https://www.scalifiai.com/docs/iam/policy-management) ##### Content [Overview](https://www.scalifiai.com/docs/iam/user-management#overview) [Create User](https://www.scalifiai.com/docs/iam/user-management#create-user) [Manage Users](https://www.scalifiai.com/docs/iam/user-management#manage-users) [Manage User Policies](https://www.scalifiai.com/docs/iam/user-management#manage-user-policies) [Edit User](https://www.scalifiai.com/docs/iam/user-management#edit-user) [Change User Password](https://www.scalifiai.com/docs/iam/user-management#change-user-password) [Delete User](https://www.scalifiai.com/docs/iam/user-management#delete-user) * * * hide\_imageHide Images --- --- ## Model Design | Model Building Guide Source: https://www.scalifiai.com/docs/model-building/model-design Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) chevron\_left ## Identity and Access Management [User Management](https://www.scalifiai.com/docs/iam/user-management) [Policy Management](https://www.scalifiai.com/docs/iam/policy-management) [Third Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) ## Model Building [Model Designs](https://www.scalifiai.com/docs/model-building/model-design) [Model Jobs](https://www.scalifiai.com/docs/model-building/model-job) ## Model Catalog [Model Management](https://www.scalifiai.com/docs/model-catalog/model-management) [Metadata](https://www.scalifiai.com/docs/model-catalog/metadata) [Model Version](https://www.scalifiai.com/docs/model-catalog/model-version) ## Billing and Usage [Usage](https://www.scalifiai.com/docs/billing-and-usage/usage) [Quota](https://www.scalifiai.com/docs/billing-and-usage/quota) [View Plansopen\_in\_new](https://www.scalifiai.com/contact-us) [Contact Us](https://www.scalifiai.com/contact-us) # Model Designs Scalifi Ai's Model Building Service (MBS) will help to streamline the process of constructing machine learning models, saving time and resources. With MBS, you will get the right tools that are always at your fingertips to effortlessly construct advanced AI model designs. Allowed operations that users can perform on the platform are: 1. Create Design 2. Build Design 3. What is Advance Node Configuration? 4. Delete Design ## insert\_linkCreate Design 01. Proceed to Model Designs page by selecting **Model Building** from parent dashboard or from quick shift dropdown from header ![Model Building Parent Dashboard - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/parent_dashboard_model_building_0d3a082396.svg) 03. Click on the **Create New Design** button. ![Create New Design Model Building - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_create_new_design_df812ecd28.svg) 05. Enter the required data like Model Name, Description. Choose the desired framework and click on Create Design. ![Select Design Framework For New Model - Model Building - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_create_design_framework_46c619a7a0.svg) 07. You will be redirected to a canvas page where you will see the chosen framework nodes on the left side. 08. Now, simply drag-and-drop the nodes onto the canvas, enter the node configuration and connect the nodes as per the model design requirements. 09. Use **Save Design** option, to save the model design once you are done with the configuration. To build and save the design at once, use the **Build & Save** option. ![Save And Build Model Design - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_model_design_save_and_build_da62e6873d.svg) 11. Along with the **Save**, the following options are available: **Option** **Description** Export This is used to export model design as code with multiple configurations including jupyter notebook and python script. Build Model This option is used to only build model design (with full framework checks) and see if there are any configuration or framework specific errors. This utilizes greater resources and time. Model building without job is restricted to specific resource limits, for large model designs use **Model Build Job**. Quick Build This option is used to build model design but without using excessive resources and time, if there are any errors it will be displayed on the canvas, if not model statistics will be returned to quick build results. Submit Build Job This option is used for large models that require more resources and time. If there are any errors in model configuration, it will be displayed on build job page. ![Quick Build Model - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_model_design_quick_build_721c2e6b78.svg) ## insert\_linkBuild Design - Once the model design flow is ready, you can build it to validate it and identify the errors using Build Model. - There are multiple ways in which you can build the model design. 1. **Quick build**: This helps to refine AI models with unprecedented speed, efficiency and quickly validate model designs and identify any errors without the need for extensive hardware resources and in a lesser amount of time. If the model design is built successfully it also returns model statistics which can be viewed by pressing the **Quick Build Results** button. 2. **Build**: Using the normal build option, you can validate the model in depth using framework specific build code which requires higher hardware resources and more time consumption. This option also has a hard limit on size of model design, for larger models please shift to submitting a model build job. ![Quick Build Model Result - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_quick_build_result_7a1f208aeb.svg) - Click on the Bug icon to get the appropriate error analysis in a dedicated view. ![Model Building Canvas Errors - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_build_design_error_13e2c8115b.svg) **Note:** The complex models which are heavy in terms of parameters, cannot be built using this feature. Submit a **Model Build Job** for such designs. ## insert\_linkAdvance Node Configuration Click on the Advance config of the node and fine tune each layer/node of the model using advanced configuration to a granular level based on different parameters. These parameters vary depending on the layer type. ![Advanced Config Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_model_design_advance_config_b16c0e4dd3.svg) ## insert\_linkDelete Design - There are two ways to delete an existing design. - Proceed to the Model Designs page, select the design you want to delete → go to Actions → Delete ![Delete Model Design - Model Building Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_delete_model_design_c8c16823ca.svg) - Proceed to the Model Designs page, open the design you want to delete → go to Actions → Delete ![Delete Model Design From Per Model Canvas - Scalifi Ai](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/mbs_delete_model_design_from_per_canvas_18f8e42443.svg) **Note:** - You can delete only one design at a time. - Delete is a dependent action. You will receive dependency errors while deleting a design. If any model job is attached to the design, you will have to first delete that job and then only you will be able to delete the design. Was this helpful? sentiment\_very\_satisfiedsentiment\_satisfiedsentiment\_dissatisfiedsentiment\_very\_dissatisfied _This feedback is collected anonymously and will not be linked to any personal data. See our [Privacy Notice](https://www.scalifiai.com/legal/website/privacy-notice) & [Terms and Conditions](https://www.scalifiai.com/legal/website/terms-and-conditions) for details._ Submit [PreviouswestThird Party Accounts](https://www.scalifiai.com/docs/iam/third-party-accounts) [NextModel Jobseast](https://www.scalifiai.com/docs/model-building/model-job) ##### Content [Overview](https://www.scalifiai.com/docs/model-building/model-design#overview) [Create Design](https://www.scalifiai.com/docs/model-building/model-design#create-design) [Build Design](https://www.scalifiai.com/docs/model-building/model-design#build-design) [Advance Node Configuration](https://www.scalifiai.com/docs/model-building/model-design#advance-node-configuration) [Delete Design](https://www.scalifiai.com/docs/model-building/model-design#delete-design) * * * hide\_imageHide Images --- --- ## Scalifi Ai Blogs - The Ultimate Resource for Machine Learning Source: https://www.scalifiai.com/blogs Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Blogs ### Cognis Ai (Beta) Update Note: New LLMs and Custom Configurations are Now Live on Cognis Ai Custom configuration on Cognis Ai helps you optimize token usage on models like GPT 5.2, Gemini 3 Pro, etc., and reduce your cost by up to 40%. Read the update and try Cognis Ai! [Read More](https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI) ![Test out custom configuration across reasoning levels, temperature and verbosity on Cognis Ai ,for models like GPT 5.2, Gemini 3 Pro, etc. ](https://www.scalifiai.com/_next/image?url=https%3A%2F%2Fscalifiai-cms.nyc3.cdn.digitaloceanspaces.com%2FCustom_Configuration_on_Cognis_Ai_ba4a7e6af4.png&w=640&q=75) Search Blogs Search Blogs Filter: addLLM addAI Ethics addNLP addGenerative AI 10 min read AI Innovation Generative AI LLM ##### LLM Conversation Branching on GPT, Gemini, Claude Native Interfaces vs. Cognis Ai Explore how LLM Conversation branching works on GPT, Gemini, Claude, etc., on their native interfaces vs Cognis Ai. [Read More >](https://www.scalifiai.com/blog/llm-conversation-branching-chatgpt-gemini-claude) 8 min read Generative AI AI Ethics AI Innovation Machine Learning ##### Human-in-the-Loop (HITL) Systems: The Future of AI Human-in-the-loop systems like Agentic AI are outperforming traditional LLMs because they yields more meaningful and grounded results in lesser time. [Read More >](https://www.scalifiai.com/blog/human_in_the_loop_future_of_ai) 5 min read Walkthroughs ##### Walkthrough: Content Writing Canvas on Cognis Learn how to generate high-quality SEO, GEO, AEO friendly articles and blogs using the Content Writing Canvas on Cognis Ai. [Read More >](https://www.scalifiai.com/blog/walkthroughs_content_writing_canvas_on_cognis) 5 min read Walkthroughs ##### Walkthrough: How to Use Google Sheets on Cognis Learn how to Install, Set Up and Use Google Sheets on Cognis Ai. [Read More >](https://www.scalifiai.com/blog/walkthroughs_google_sheets_on_cognis) 5 min read Annoucements ##### Cognis Ai (Beta): The Ultimate Agentic AI for all Your Professional Needs Cognis Ai (Beta) is live now. Explore the power of a Multi-LLM Agentic Ai platform that reduces your work time by 5x. [Read More >](https://www.scalifiai.com/blog/cognisailaunch) 10 min read AI Innovation Generative AI LLM ##### The Key to Removing AI Slop - Memory Managed AI Agents Learn how you can engineer AI Agents to remove AI Slop from your workflows. [Read more >](https://www.scalifiai.com/blog/agentic-ai-ai-slop) 15 min read Generative AI LLM AI Innovation ##### Best Practices for Function Calling in LLMs in 2025 Learn the best practices of implementing function calling LLMs in 2025. Explore methodologies, identify risks., and discover alternatives [Read More >](https://www.scalifiai.com/blog/function-calling-tool-call-best-practices) 15 min read AI Innovation LLM Generative AI ##### Should You be Using MCP - Model Context Protocol in 2025? Explore whether to or not to use MCP in 2025 to connect your LLMs to your favorite apps. [Read more >](https://www.scalifiai.com/blog/model-context-protocol-flaws-2025) 7 min read LLM AI in Education AI in Finance AI in Healthcare AI Ethics ##### In-Depth Study of Large Language Models (LLM) Explore the depths of Large Language Models: architecture, training, future trends, and ethical implications. [Read more >](https://www.scalifiai.com/blog/what-is-large-language-model-llm) 5 min read Natural Language Processing NLP AI in Education AI in Healthcare AI Innovation ##### Understanding Natural Language Processing (NLP) Essentials Uncover the fundamental concepts, applications, and practical insights to kickstart your journey into the fascinating realm of language processing with machines. [Read more >](https://www.scalifiai.com/blog/what-is-natural-language-processing-nlp) 7 min read AI Ethics Deep Learning Machine Learning AI in Finance AI in Healthcare ##### The Beginner's Guide to AI Models: Understanding the Basics Dive into AI models, unraveling their essentials and applications and explore the transformative power of artificial intelligence. [Read More >](https://www.scalifiai.com/blog/what-is-an-ai-model) 5 min read Annoucements ##### Cognis Ai (Beta) Update Note: New LLMs and Custom Configurations are Now Live on Cognis Ai Custom configuration on Cognis Ai helps you optimize token usage on models like GPT 5.2, Gemini 3 Pro, etc., and reduce your cost by up to 40%. Read the update and try Cognis Ai! [Read More >](https://www.scalifiai.com/blog/custom_configuration_for_LLMs_on_Cognis_AI) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) --- --- ## Customer Churn Prediction | Predict Customer Churn - Scalifi Ai Source: https://www.scalifiai.com/usecase/ai-ml-in-customer-churn-prediction Cognis AiLive Now. Unlock the power of context-aware AI Agents today. [Learn More](https://www.cognis-ai.com/) [Try for Free](https://www.platform.scalifiai.com/register) [![scalifi ai logo](https://www.scalifiai.com/_next/image?url=%2FLogo.png&w=96&q=75)Scalifi Ai](https://www.scalifiai.com/) [Products](https://www.scalifiai.com/#products) [Docs](https://www.scalifiai.com/docs) [Blogs](https://www.scalifiai.com/blogs) [Use Cases](https://www.scalifiai.com/usecases) [Pricing](https://www.scalifiai.com/pricing/cognis_ai) searchSearch ctrl+k [Log in](https://www.platform.scalifiai.com/login) [Try for free](https://www.platform.scalifiai.com/register) # Customer Churn Prediction Take proactive measures to re-engage buyers who are likely to churn [Try for free](https://www.platform.scalifiai.com/register) [Book a demoeast](https://www.scalifiai.com/contact-us) ![Customer Churn Prediction](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/customer_churn_prediction_52c950f85f.svg) ## Problem Statement ##### Addressing the issue of Rising Customer Churn ![Addressing the issue of Rising Customer Churn](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/customer_churn_prediction_problem_caed48b08c.svg) A company was grappling with an increasing issue of customer churn. This was creating a negative impact on its revenue and profitability. Despite the company's efforts to retain customers through generic marketing strategies, they have not been unable to identify the underlying cause of the problem or predict which customers are most likely to churn. To address the problem of customer churn, the company required a solution that will enable them to identify the root cause of the problem and accurately predict which customers are most likely to churn. By targeting these customers with personalized marketing strategies, they aim to reduce churn, increase revenue and customer loyalty, and improve profitability. ## Solution ##### AI-Powered Customer Churn Analysis - A Path to Growth and Profitability ![AI-Powered Customer Churn Analysis - A Path to Growth and Profitability](https://scalifiai-cms.nyc3.cdn.digitaloceanspaces.com/customer_churn_prediction_solution_2dbe356320.svg) The company leveraged Artificial Intelligence (AI) to overcome its growing challenge of customer churn. By analyzing customer data, AI can predict which customers are most likely to churn and provide valuable insights into the underlying factors contributing to churn. With this data, organizations can develop targeted and personalized marketing campaigns aimed at retaining their most valuable customers and improving their Return on Investment (ROI). Scalifi Ai's Customer Churn Analysis is an effective solution that helps companies to achieve its objective. This can be achieved by focusing its personalized marketing efforts on the most profitable customers and those at risk of churning, the company aims to reduce churn and increase revenue. ## Benefits ###### groupRetain Churning Customers Scalifi Ai's AI Models get trained in your company's historical data & customer data and can make accurate predictions to identify which customers are most likely to churn and provide valuable insights into the underlying factors contributing to churn. ###### query\_statsMake Smart Data-Driven Decisions With the help of Scalifi Ai’s AI-powered customer churn analysis, organizations can make data-driven decisions to retain their valuable customers. The insights from the AI Models can be easily integrated into your CRM software to execute smart data-driven strategies. ###### dashboard\_customizeProvide Personalized Experiences With the help of Scalifi Ai’s Customer Churn Analysis, you now focus personalized marketing efforts on the most profitable customers and those at risk of churning. This will significantly increase revenue and drive rapid growth. ###### Related Blogs Natural Language Processing: How Neural Word Embeddings Enable Machines to Understand Text [Read moreeast](https://scalifiai-founder.medium.com/how-do-machines-understand-text-via-natural-language-processing-nlp-41aeb853ef52?source=friends_link&sk=b38399d604862bab7c3b2d12ee601dee) In-Depth Study of Large Language Models (LLM) [Read moreeast](https://www.scalifiai.com/blog/what-is-large-language-model-llm) ###### Explore other usecases AI in Cyber Security to Redefine the Security Posture [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-cyber-security-and-mfa) Credit Card Fraud Detection [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-credit-card-fraud-detection) Revenue Prediction [Read moreeast](https://www.scalifiai.com/usecase/ai-ml-in-revenue-sales-prediction) #### Contact Us Fill up the form and our team will get back to you within 24 hrs Submit phone [+91 9999600340](tel:+919999600340)public [www.scalifiai.com](https://www.scalifiai.com/) phone [+1 929-460-8045](tel:+19294608045)email [helpdesk@scalifiai.com](mailto:helpdesk@scalifiai.com) ---