Knowledge Retrieval
Production RAG for Enterprise Documents
A production retrieval system that turns scattered internal documents into fast, accurate, source-backed answers.
85%+
retrieval accuracy
Hours
saved per team weekly
100%
answers with citations
The challenge
Answering routine questions meant digging through PDFs, wikis, and ERPNext records by hand. Answers were slow, inconsistent, and dependent on whoever happened to know where things lived.
The approach
- 01
Consolidated the document sources and built an ingestion pipeline with semantic chunking.
- 02
Implemented hybrid retrieval, dense embeddings plus a BM25 fallback, with cross-encoder reranking for precision.
- 03
Surfaced answers through a simple chat interface, every response linked back to its source.
- 04
Tuned retrieval against real questions and added evaluation to catch regressions before they shipped.
Architecture
Production RAG for Enterprise Documents architecture. Main flow: Documents, then Chunk + embed, then Hybrid retrieve, then Rerank, then Cited answer. Connected paths: Vector search feeding Hybrid retrieve; BM25 keyword as a conditional path from Hybrid retrieve.
Curious how this was built?
The full experience and skill set behind it are on the resume.
View resume