VerifAI: AI verification & Validation Solution

Tech Mahindra’s VerifAI brings the power of generative Al to provide a comprehensive AI validation and verification offering, designed to help organizations transition their AI use cases from Proof-of-Concept to Production efficiently and ethically.

AWS-Powered AI/GenAI model validation & verification application:

The solution's robust architecture is deployed on AWS cloud platform and uses Amazon Elastic Kubernetes Service (EKS) allowing full flexibility to run the application. application with Amazon Bedrock LLM.

1. Flexibility to choose LLM: Powered by Amazon Bedrock LLM, supports various LLMs models available on Amazon Bedrock
2. Robust Security and Scalability: Application leverages AWS Cognito for enterprise-grade protection and user management. It also uses AWS ELB (load Balancer) for high availability and MSK for managed streaming.
3. User Management and Content Delivery: Utilizes Amazon Cognito for secure authentication
4. Storage Powerhouse: Application also uses Amazon S3 for scalable content storage
5. Intelligent Monitoring: Features Amazon Cloud Watch and Managed ELK to collect and track metrics and monitor logs.
6. Flexibility to customize the application: Choose what capabilities, APIs and tools you need and customize it as per your requirement.

The application is a 360-degree validation framework with customizable metrics, pre-built solutions for faster value realization, and a microservices-based architecture for seamless integration. VerifAI also emphasizes ethical AI practices, ensuring that AI systems perform as intended and adhere to legal and ethical standards. VerifAI is a comprehensive solution for validating and verifying the outcomes of AI and GenAI projects. Through this solution, Tech Mahindra will help enterprises validate the end-to-end lifecycle of AI-based projects, thereby enabling them to scale their AI initiatives with speed.

Key Features:

Automated Validation Approach:

• RAG Evaluation: Automates RAG evaluation with automated QnA generation and develop RAG Application with Test Driven Methodology levering automated tools

• Agentic AI Evaluation: Evaluation of Agentic AI agents by measuring how the agents, tools and supervisors are performing

Enterprise level Security:

• Load & Security Test: Application tests GEN AI Application endpoints to simulate user loads and seasonality and visualize the performance It also generates security attacks on Gen AI applications to prevent and test cybersecurity threats.

• Guardrails: Firewall Gen AI applications by Detecting and Preventing harmful prompts and cyber security attacks to safeguard the applications

**Insightful Monitoring & Observation : **

• Continuously monitors GEN AI applications for various quality metrics such as biases, hallucination, PII (and few other metrics) for timely detection of any anomalies

• Prompt Performance Tools: Uses specially developed tools to test how good your prompts perform before using them into your solution. It also allows VerifAI APIs to build your own testing, monitoring and governance solution.

Benefits:

1. Powerful application for AI/Gen AI project validation: It focuses on ensuring that data is validated and protected to prevent biases and breaches. This involves thorough testing of AI models to ensure reliable outputs and continuous monitoring to meet business requirements.
2. High quality output: Application enhances output quality by establishing a robust evaluation framework. This framework includes 360° validation, ensuring the reliability and effectiveness of solution.
3. Reliability & effectiveness: The solution enhances output quality by establishing a robust evaluation framework. This framework includes 360° validation, ensuring the reliability and effectiveness of our solutions
4. End to End Services: The application offers strategic consulting, access to a pool of experienced talents, and the implementation of both TechM VerifAI technical solutions and AWS key functionalities as enabler.
5. Regulatory Compliant: It adds the benefit of validation in the discovery and pre-development stages with customizable schedule for verification to scale responsibly at speed.
6. Quicker Adoption: It is easy to use APIs with playbook. A dedicated team of data scientists can be enabled on the solution with ease making it easy to adopt.
7. Improved Decision Making: Evaluating GEN AI use cases helps in identifying and addressing issues early, which can reduce development costs and improve resource allocation based on continuous performance monitoring.
8. Cost Effective: Identifying and addressing issues early leads to reduced development costs as well as better ROI.
9. Fostering Innovation: application enables GEN AI use cases drive transformation across the organization by enhancing efficiency, fostering innovation, and improving service quality.