# Enterprise RAG System

Client: Large institution
Canonical path: /case-studies/enterprise-rag

## Summary

A retrieval-augmented generation system for searching institutional documents, chunking knowledge sources, and answering staff queries through a focused interface.

## Outcome

Created a practical path from unstructured documents to reliable internal knowledge access.

## Challenge

The institution had unstructured documents and needed a practical internal search surface that returned grounded answers rather than confident unsupported summaries.

## Evidence

- Knowledge shape: Unstructured documents
- Answer mode: Citation-first
- Interface: Custom staff UI
- Primary risk controlled: Unsupported answers

## Approach

1. Designed the retrieval pipeline around document chunking, metadata, and citation display.
2. Built a focused staff-facing UI instead of exposing raw embedding/search internals.
3. Prioritized answer provenance so users could inspect the source document behind each claim.
4. Outlined a production path for permissions, refresh cadence, and evaluation of retrieval misses.

## Timeline

1. Document audit
2. Chunking strategy
3. Vector search
4. Answer UI
5. Grounding review

## Risks

- Stale documents
- Permission leakage
- Bad chunk boundaries
- Citation mismatch

## Stack

- Vector database
- FastAPI
- LangChain/LlamaIndex
- React frontend

## Next Evidence Step

Replace provisional metrics with client-approved screenshots, raw artifacts, and final numbers when available.