Large institution
Enterprise RAG System
A retrieval-augmented generation system for searching institutional documents, chunking knowledge sources, and answering staff queries through a focused interface.
Created a practical path from unstructured documents to reliable internal knowledge access.
Challenge
The problem that had to be made measurable.
The institution had unstructured documents and needed a practical internal search surface that returned grounded answers rather than confident unsupported summaries.
Knowledge shape
Unstructured documents
Answer mode
Citation-first
Interface
Custom staff UI
Primary risk controlled
Unsupported answers
Approach
Designed the retrieval pipeline around document chunking, metadata, and citation display.
Built a focused staff-facing UI instead of exposing raw embedding/search internals.
Prioritized answer provenance so users could inspect the source document behind each claim.
Outlined a production path for permissions, refresh cadence, and evaluation of retrieval misses.
Project timeline
Document audit
Chunking strategy
Vector search
Answer UI
Grounding review
Case study packet
Downloadable evidence brief.
Generated packet for sales follow-up, client review, and future replacement with approved screenshots, raw artifacts, and final metrics.
Evidence cards
4
Risk items
4
Canonical page
/case-studies/enterprise-rag
Manifest
/reports/case-study-packets/manifest.json
Risk register
Consulting pattern
Prototype quickly, then turn the demo into an evaluation surface.
The reusable Edxperimental pattern is to make the workflow measurable: inputs, expected output, acceptable failure, operational risk, and a repeatable benchmark before production expansion.
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