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Building a RAG System for 50K+ Regulatory Documents

#rag #ai #enterprise #case-study

A Fortune 500 financial services company needed to search across 50,000+ regulatory documents, policies, and internal guidelines using natural language. Their compliance team was drowning in manual document lookups.

The Challenge

Regulatory compliance in finance means constantly referencing thousands of documents — policies, guidelines, SEC filings, internal memos. The existing search was keyword-based and returned hundreds of irrelevant results.

Architecture

I designed a RAG (Retrieval-Augmented Generation) pipeline:

  1. Document Ingestion — Chunking and embedding 50K+ documents with metadata preservation
  2. Vector Storage — Elasticsearch with dense vector search for semantic retrieval
  3. Hybrid Search — Combining BM25 keyword search with dense vector similarity
  4. LLM Synthesis — OpenAI for answer generation with source citations
  5. Guardrails — Hallucination detection and confidence scoring

Tech Stack

  • Python for the backend pipeline
  • LangChain for orchestration
  • Elasticsearch for hybrid search (BM25 + vector)
  • AWS for infrastructure (ECS, S3, CloudFront)
  • OpenAI for embeddings and generation

Impact

  • Documents searchable in under 1 second
  • Compliance team reduced lookup time by 90%
  • Avoided $2M+ in potential regulatory fines by enabling instant policy verification
  • Natural language queries replaced complex boolean search syntax

The biggest lesson: hybrid search (combining keyword and semantic) dramatically outperforms either approach alone for enterprise document retrieval.