Enterprise Knowledge Retrieval & RAG Pipeline
Private Hybrid Vector Retrieval with Verified Source Citations
A private Retrieval-Augmented Generation (RAG) system with hybrid dense-sparse vector search, enabling staff to query technical documentation and operational policies with cited sources.
The Architectural Challenge & Engineering Solution
The Challenge
Internal support and operations staff spent hours searching through fragmented documentation across PDFs, Notion, and internal databases, causing delayed ticket response times and inconsistent answers.
Engineering Approach
Architected a private hybrid vector indexing pipeline using Qdrant and LangChain, complete with strict tenant metadata isolation, PII sanitization middleware, and clickable source document links.
Production Architecture Highlights
Technology Stack
Streamlined internal knowledge discovery, reduced repetitive tier-1 support queries, and ensured private enterprise data isolation without model retraining.
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