How do you prevent RAG from returning outdated information?

Document metadata includes timestamps; users can filter by date or sort by recency.

What embedding model and chunk size worked best?

text-embedding-3-large with 256-token chunks and 20% overlap provided best recall/latency trade-off.

How does this case study work?

Raise a request, talk to experts, fund the project, expert works, review and approve payment. All remote, all through our platform.

Building Production RAG for Enterprise Search

Executive Summary

A global consulting firm with 50,000 employees spent 4 hours per project on internal research—accessing past deliverables, methodologies, and expertise. Building a production RAG system reduced search time to 30 seconds, increased knowledge reuse by 300%, and generated $50M in annual productivity savings.

Key Outcomes

▹ 4 hours → 30 seconds per research query
▹ 500,000 documents indexed and searchable
▹ $50M annual productivity savings

Client Situation

Consultants wasted hours searching across SharePoint, email, and file shares for prior work. Critical knowledge was siloed across 20+ systems.

Key Challenges

⚠ Average 4 hours per project finding relevant past work
⚠ 80% of knowledge never reused across teams
⚠ New consultants took 6 months to become productive

Existing Architecture

SharePoint search with keyword matching, plus manual knowledge management via Excel trackers.

Keyword search missed semantic meaning
No cross-system unified search
No answer extraction—users still read full documents

Solution Design

Production RAG system with hybrid search, multi-document QA, and citation tracking for source verification.

Key Decisions

✓ Hybrid search (vector + keyword) for best recall
✓ Document chunking with overlap (256 tokens, 20% overlap)
✓ Citation tracking to original sources for trust

LangChainPineconeGPT-4KubernetesPostgreSQL

Implementation

Pilot with 3 practice areas before firm-wide rollout, iterating on chunking strategy and search quality.

Phase 1: Phase 1: Data Ingestion
Indexed 500K internal documents across SharePoint, email, and file shares.
Phase 2: Phase 2: Search Interface
Built Slack bot and web interface for consultant search.
Phase 3: Phase 3: Production Scaling
Rolled out to 5,000 daily users with 99.9% uptime.

Technical Challenges

Access control across document sources

Impact: RAG system returning documents user shouldn't see

Resolution: Integrated with Okta for per-document ACL filtering post-retrieval

Hallucination in critical answers

Impact: Legal team rejecting uncited responses

Resolution: Forced citation inclusion + retrieval confidence scoring

Results

Time to find relevant document: Before4 hours
After30 seconds
Improvement99.8% reduction
Knowledge reuse rate: Before20%
After80%
Improvement4x increase
New consultant ramp-up time: Before6 months
After2 months
Improvement67% reduction

Lessons Learned

📘 Hybrid search (vector + keyword) improved recall by 40% vs pure vector
📘 Document chunking strategy significantly impacted answer quality—256 tokens with 20% overlap was optimal
📘 Consultants trusted RAG more when citations were clearly displayed

What We Would Do Differently

💡 Implement incremental indexing for real-time document updates
💡 Build feedback collection for continuous fine-tuning

Role Relevance

RAG engineers designed the retrieval pipeline that transformed enterprise knowledge access, balancing recall, latency, and security constraints.

Critical Skills Demonstrated

RAG pipeline designHybrid search strategiesAccess control integrationLLM evaluation frameworks

Related Roles

RAG Engineer LLM Engineer ML Engineer

Frequently Asked Questions

How do you prevent RAG from returning outdated information?: Document metadata includes timestamps; users can filter by date or sort by recency.
What embedding model and chunk size worked best?: text-embedding-3-large with 256-token chunks and 20% overlap provided best recall/latency trade-off.