AI & LLM
Production RAG Systems
Production-ready retrieval systems with hybrid search, citations, evaluation and security, answering from client documents with verifiable sources and measured accuracy.
We delivered
A deployed RAG service with API and chat interface, an ingestion pipeline with scheduled re-indexing, an evaluation suite with a baseline accuracy report, and an access-control integration with the client's identity provider.
Document ingestion pipelines with structure-aware chunking. Hybrid retrieval combining dense vectors, BM25 and rerankers. Grounded answers with citations back to source passages. Document-level access control and audit logging.
Used for internal knowledge assistants over policies, wikis and tickets, customer-facing product documentation Q&A, and compliance-sensitive search where citations are mandatory.
Built with vector databases (Qdrant/pgvector), BM25 and rerankers, Claude/OpenAI APIs, LangChain/LlamaIndex and Ragas-style evaluations.
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