From 6-Hour Searches to 3-Minute Answers: RAG for Financial Compliance

How Ryzolv deployed a sovereign RAG system that cut compliance research time by 97% for a financial services firm.

Financial Services

18 weeks

97%

Reduction in search time

From 6+ hours to under 3 minutes per compliance inquiry

40,000+

Documents indexed

SEC filings, compliance memos, internal policies

96.8%

Retrieval accuracy

Measured against human expert baseline

18 weeks

Full deployment

From kickoff to production

The Challenge: Drowning in Regulatory Documents

A financial services firm managing over 40,000 regulatory documents faced a growing compliance bottleneck. SEC filings, internal compliance memos, policy documents, and regulatory guidance were scattered across SharePoint, file servers, and email archives with no unified search capability.

The compliance team spent an average of 6+ hours per inquiry manually searching across these fragmented systems. Each analyst would open multiple applications, run keyword searches that returned hundreds of irrelevant results, and read through pages of documents to locate the relevant citation or policy reference.

The manual process created real regulatory risk. Missed citations and outdated policy references were showing up in compliance reports. The firm's board flagged compliance response time as an operational risk after two quarterly audits identified gaps in documentation timeliness.

How Ryzolv Built the Solution

Cataloged 40,000+ documents across 4 source systems (SharePoint, file servers, email archives, compliance database)
Designed hybrid retrieval architecture combining dense vector search with sparse keyword search for maximum precision
Mapped role-based access control requirements so each user only retrieves documents they are authorized to view
Selected on-premise deployment for data sovereignty: no compliance data leaves the firm's infrastructure

Results: 97% Faster Compliance Research

The sovereign RAG system transformed the compliance team's workflow within the first month of production deployment. Search time dropped from 6+ hours to under 3 minutes per inquiry, a 97% reduction that the compliance director described as "the single largest productivity improvement in the department's history."

Retrieval accuracy reached 96.8% measured against the human expert baseline, meaning the system returned the correct source document and relevant section for nearly every query. Zero false positives on critical regulatory questions were observed after the initial tuning period.

The compliance team reallocated 40% of their capacity from manual search and retrieval to higher-value analysis and advisory work. Routine inquiries that previously required senior analyst time are now handled by junior staff using the RAG system, with the audit trail satisfying regulatory requirements for documentation of compliance research.

97%

Search time reduction

6+ hours down to under 3 minutes per inquiry

96.8%

Retrieval accuracy

Validated against 500 expert-graded queries

40%

Compliance workload reduction

Capacity reallocated to analysis and advisory

200+

Daily queries processed

Across 25 users with sub-second response times

Technology Stack

LangChainpgvector (PostgreSQL)FastAPIReactOpen-source embedding modelsHybrid search (dense + sparse)Confidence scoringAudit logging

Related Ryzolv Services

RAG & Knowledge Systems AI Governance & Compliance

Common Questions

Ryzolv validates RAG accuracy through systematic testing against human expert baselines before production deployment. For this engagement, we tested against 500 historical compliance queries with known correct answers, achieving 96.8% retrieval accuracy. The system uses hybrid search (combining dense vector and sparse keyword retrieval), re-ranking for precision, citation enforcement on every answer, and confidence scoring that routes low-confidence results to human review. Learn more about our RAG & Knowledge Systems approach.

Yes. Ryzolv specializes in sovereign RAG deployment where all data processing happens on your infrastructure. This engagement used pgvector (a PostgreSQL extension) for vector storage, open-source embedding models for document indexing, and FastAPI for the retrieval service. No data left the client's network at any point during indexing, search, or answer generation. See our LLM Fine-Tuning & Sovereign Deployment service for details on sovereign AI architecture.

Enterprise RAG implementations typically take 12-18 weeks from kickoff to production. This engagement completed in 18 weeks across 4 phases: data assessment (3 weeks), pipeline and indexing (5 weeks), RAG system build and testing (6 weeks), and production deployment with training (4 weeks). Timeline depends on document volume, source system count, access control complexity, and accuracy requirements. See our AI Strategy & Implementation service for how we scope engagements.