Enterprise RAG Knowledge Assistant (10M-Doc Scale)

Engineered a large-scale enterprise Retrieval-Augmented Generation (RAG) architecture capable of indexing and querying over 10 million documents. Optimized vector search and embeddings to serve 500+ concurrent users, achieving a 60% reduction in query latency and delivering precise, context-aware answers in real-time.