70% ↓ search time
Enterprise RAG: internal knowledge base for 200+ consultants
Fractional CTO•Management consulting firm•Aug 2023 — Feb 2024•Published 22 October 2023
Built a retrieval-augmented knowledge platform indexing proposals, playbooks, and case archives — with citation grounding and access controls.
Technology Stack
PythonOpenAIPineconeReactFastAPIAzure
Key Outcomes
- •70% reduction in time spent searching historical proposals and decks
- •Citation-grounded answers with source links for auditability
- •Role-based access preserving client confidentiality
Outcome: 70% less time searching proposals and institutional knowledge.
Context
A 200+ person consulting firm stored a decade of proposals across SharePoint, email, and local drives. Partners needed answers fast without breaching client confidentiality.
Problem
- Search tools returned filenames, not answers.
- Junior staff recreated slides that already existed.
- No guardrails against cross-client data leakage.
Approach
- Ingestion — PDF/PPTX parsers with client-level tagging.
- Chunking + embeddings — Pinecone with metadata filters.
- RAG answers — mandatory citations; “I don’t know” when confidence low.
- RBAC — Azure AD groups map to index partitions.
Results
Self-reported search time down 70% in a 30-day pilot; legal approved rollout after DPIA review.
Lessons
Citations are not optional in professional services. RBAC at the vector layer beats post-hoc filtering.
CTA
Need enterprise RAG with real access controls? Book a 30-min call.