Enables AI-powered document analysis and querying for project documentation using vector embeddings stored in Redis. Supports document upload, context-aware Q\&A, automatic test case generation, and requirements traceability through OpenAI integration.
Enables LLMs to search and retrieve information from large technical documentation (OpenAPI specs, markdown) via intelligent chunking and semantic search.
Enables AI assistants to interact with IBM Engineering Lifecycle Management (ELM), including DOORS Next Generation, EWM, and ETM, for managing requirements, work items, tests, and project builds through natural language.
Enables natural language queries on technical specifications and automated code compliance checks using local RAG with vector search, integrated via MCP.
Enables AI assistants to search through structured databases and unstructured content (documents, videos, files) using natural language queries with semantic understanding.
