Automated Compliance Audits with RAG and LLM

How RAG and LLM Automate Compliance Checks

Manually checking hundreds of pages of contracts for regulatory compliance is slow, expensive, and error-prone. Lawyers spend up to 40% of their time on monotonous audits, and legislative changes emerge weekly. We designed an AI system that automates compliance checks using RAG and LLMs, cutting audit time by 20x and reducing penalty risks. The system runs 24/7, processing up to 1000 pages per day, and requires no constant human involvement.

Even an experienced compliance officer cannot track all changes: in the last period over 600 regulatory acts were adopted in Russia. Human factors lead to 15–20% missed violations. The AI system solves this through automatic gap analysis, change monitoring with alerts, and a unified knowledge base in the RAG repository. Accuracy reaches 95% for standard documents (contracts, policies) thanks to fine-tuning on legal corpora and few-shot prompts.

Why Manual Compliance Checks No Longer Work

The volume of the regulatory base is growing exponentially. According to the Central Bank of Russia, the number of clarifications on Law 115-FZ increased by 30% per year. Even a team of five compliance officers cannot track all changes manually. Key problems:

• Time costs — checking one contract (50 pages) takes 4–6 hours.
• Missed violations — human inattention yields 15–20% defects.
• Outdated databases — regulation updates quarterly, though acts change daily.

How the AI System Checks Compliance

The key component is RAG (Retrieval-Augmented Generation). For each document, the system retrieves relevant regulatory requirements, and then the LLM generates a structured response.

class ComplianceCheck(BaseModel): document_type: str jurisdiction: str violations: list[Violation] warnings: list[Warning] compliant_aspects: list[str] overall_status: Literal["compliant", "minor_issues", "violations", "critical_violations"] applicable_regulations: list[str] recommendations: list[str] def check_compliance(document_text: str, doc_type: str, jurisdiction: str) -> ComplianceCheck: regulations = regulation_rag.search( f"{doc_type} compliance requirements {jurisdiction}", top_k=10 ) return llm.parse( build_compliance_prompt(document_text, regulations, doc_type), response_format=ComplianceCheck ) 

Performance Metrics

Manual vs AI System Comparison

RAG System Architecture for Compliance

The regulatory document base is built on a vector database (Qdrant) with 1536-dim embeddings. Sources:

• Federal laws and subordinate legislation
• Regulatory requirements from the Central Bank, FAS, Rospotrebnadzor
• Technical Regulations of the EAEU
• International standards (ISO, IFRS, SOX)
• Regulatory clarifications and letters

Updates: subscription to official legal databases (ConsultantPlus, Garant) with automatic indexing of new documents. Each act is assessed for impact: what internal procedures need to be changed? Alerts are sent to responsible persons.

Case study: compliance automation in a bank. A bank with 50,000 internal documents required quarterly audits of 10,000 contracts. Manual audit took 3 months and missed 18% of violations. After implementing the AI system: audit time — 5 days, miss rate — 3%. Savings amounted to 8 million rubles per year by reducing outsourced lawyer headcount.

What Is Included in the Work

1. Audit of current regulatory documents — compilation of applicable regulations list.
2. Building the RAG database — cleaning, chunking, indexing hundreds of documents.
3. Prompt development and validation — tuning the LLM for specific compliance tasks.
4. Integration with document management — REST API for automatic document upload and result retrieval.
5. Dashboard for compliance officers — status visualization, audit history, notifications.
6. Team training — documentation, webinars, 3-month support.

Our team's experience: 5+ years in AI/ML, over 20 NLP system implementations, including for legal departments in the financial sector. We guarantee compliance with 152-FZ and GDPR for personal data processing. Contact us for a consultation on adapting the system to your jurisdictions — we will assess your document volume and propose the optimal solution. Order development by reaching out to discuss details.

Implementation Timeline

How to Order Development

Contact us — we will assess your document volume, number of jurisdictions, and priority compliance areas. Discuss the details with our experts to receive a commercial proposal within 2 days.