Enterprise RAG.
Answers grounded in a large document corpus. A retrieval system built around relevance, traceability, and continuous evaluation.
Python · LangChain · Pinecone · OpenAI · Next.js · DeepEval · RAGAS
The problem
A large document collection is only useful if an assistant can retrieve the right context and keep answer quality measurable as the system changes.
The engineering
Built a RAG pipeline over a large document corpus, combining hybrid retrieval with cross-encoder re-ranking.
Automated DeepEval quality scoring in CI to keep answer quality measurable as the system changes.
Monitored latency, token cost, and retrieval-quality drift to make quality and operating behavior visible.
The outcome
Delivered a retrieval pipeline with automated quality scoring in CI, so regressions in latency, cost, and retrieval quality surface early.