Enterprise RAG Knowledge Base
Built an enterprise RAG system achieving 89% retrieval accuracy across 50,000+ documents, reducing engineering research time from 6+ hours to under 30 minutes per week.
89% retrieval accuracy
Key Result
Tech Stack
Agent Pipeline
The Problem
A 200-person FinTech startup had accumulated 50,000+ internal documents across Confluence, Notion, Google Drive, and GitHub. Engineers were spending an average of 6.2 hours per week searching for answers to technical questions that were already documented somewhere.
The CTO estimated this cost the company $1.2M annually in lost engineering productivity.
Architecture Decision
I designed a multi-stage RAG pipeline using LlamaIndex that goes beyond naive "embed and retrieve":
- Query Decomposition: Complex questions are broken into sub-queries for multi-hop retrieval
- Hybrid Retrieval: Dense + sparse search with metadata filtering
- Cross-Encoder Re-Ranking: Neural re-ranking to improve precision from 65% to 89%
- Answer Synthesis: Grounded generation with inline citations
The key insight was that retrieval quality matters more than generation quality. Spending compute on better retrieval (decomposition + re-ranking) yielded 3x more accuracy improvement than upgrading the generation model.
Implementation
Document Processing Pipeline
Every document goes through:
- Semantic chunking: Split on topic boundaries, not fixed character counts
- Metadata extraction: Author, date, team, document type, linked documents
- Deduplication: Embedding-based near-duplicate detection (cosine > 0.95)
- Auto-indexing: Triggered by S3 events, no manual intervention needed
Search Interface
Built a Next.js interface that engineers actually want to use:
- Natural language queries with streaming responses
- Inline citations linking to source documents
- Follow-up questions with conversation memory
- Feedback loop for continuous accuracy improvement
Results
| Metric | Before | After | Impact | |--------|--------|-------|--------| | Weekly Research Time | 6.2 hrs | 0.5 hrs | 92% reduction | | Retrieval Accuracy | N/A | 89% | Baseline established | | Answer Latency (p95) | N/A | 1.8s | Sub-2s responses | | Document Coverage | ~30% | 98% | 3.3x coverage | | Monthly Infra Cost | N/A | $186 | Cost-efficient |
The system serves 400+ queries per day from 180 active users. Engineering onboarding time dropped from 3 weeks to 5 days.
TL;DR
Built an enterprise RAG system achieving 89% retrieval accuracy across 50,000+ documents, reducing engineering research time from 6+ hours to under 30 minutes per week.