LTIMindtree Data And AI Tracer

TRACER is a comprehensive, end-to-end automated framework designed for “black-box” testing of both Core and Generative AI applications, with a strong emphasis on Responsible AI principles. Built to support iterative development and production environments, TRACER enables organizations to track multiple AI projects across releases using configurable YAML-driven architecture. It integrates a rich orchestration layer, evaluation microservices, and a robust data layer to facilitate seamless project configuration, automated testing, and explainable reporting. The framework leverages Azure components strategically to enhance scalability, performance, and reliability. Azure Web App hosts the core application interface, enabling users to interact with TRACER through a secure and scalable environment. Azure Static Web Apps are used for lightweight, fast-loading dashboards and result visualization interfaces, ensuring a responsive user experience. Azure Cosmos DB for MongoDB serves as the backbone for storing evaluation results, project configurations, and ground truth versions, offering high availability and global distribution. Azure Blob Storage is employed for storing large datasets, engineering drawings, and benchmark generation artifacts, supporting multimodal evaluation functions.

TRACER’s uniqueness lies in its ability to evaluate AI systems using a wide array of pre-built functions such as hallucination detection, LLM vulnerability analysis, RAG component-level testing, and engineering drawing critique. It supports explainable AI through detailed metric visualization and qualitative/quantitative assessments, even in the absence of ground truth. The framework also includes red teaming datasets to test for bias, toxicity, and other vulnerabilities, ensuring ethical and secure AI deployment. TRACER maps directly to Responsible AI dimensions—Ethical, Explainable, Efficient, and Comprehensive—by offering features like LLM evaluation, benchmark dataset generation, and compliance checking against global regulations.

Its modular design allows for flexible integration with external libraries like LangChain, LangGraph, RAGAS, DeepEval, and FastAPI, while internal libraries ensure seamless orchestration and logging. The system supports agent-driven testing of ML pipelines, hyperparameter tuning, and model analysis, providing actionable recommendations for improvement. With its patent filed in India, TRACER stands out by offering a trustworthy, robust, and user-friendly solution that enhances trust and assurance among business users, reduces time and effort through automation, and simplifies AI assurance with intuitive configuration and visualization. Ultimately, TRACER empowers enterprises to accelerate their AI adoption journey by delivering measurable business outcomes and fostering responsible innovation.