Venture Capital RAG System
Production RAG system for querying a VC firm's internal knowledge base with natural language
Built a production RAG system that allows teams to ask natural-language questions over internal investment documents, memos, and playbooks. The system retrieves relevant passages via vector search and generates grounded answers with source references, improving internal knowledge access and reuse. The system runs on AWS with an asynchronous ingestion pipeline that processes new documents, extracts text, and generates embeddings for retrieval. Designed for reliability and internal use rather than demo-style outputs, with emphasis on traceable answers and consistent behavior.
The hard part
Ensuring answer reliability was the main challenge. Investment teams need traceable responses, so the system was designed to prioritize retrieval grounding and source attribution over creative generation. This required careful prompt design, retrieval tuning, and document preprocessing so that answers consistently referenced real internal material.
What I did
Designed and implemented the end-to-end RAG architecture. Built the ingestion pipeline for document processing, text extraction, chunking, and embedding. Implemented the retrieval and generation pipeline, backend APIs, and document indexing workflows. Deployed and managed the system on AWS and supported iteration based on real user queries and feedback.