What You’ll Build
In about three hours, you will have an AI compliance checker that scans internal documents, marketing materials, customer communications, and policies against regulatory frameworks like GDPR, FCA, HIPAA, or your custom compliance rules. The system flags potential violations, explains the regulatory basis, and suggests compliant alternatives. Scanning 100 documents takes under 15 minutes on a dedicated GPU server with all content staying on-premises.
Compliance teams are outnumbered by the volume of content they must review. A single missed violation in a marketing email or policy document can result in six-figure fines. Manual review does not scale, and sending regulated content to external AI services creates its own compliance risks. Self-hosted compliance checking on open-source models keeps your sensitive documents private while providing continuous automated monitoring.
Architecture Overview
The checker has three layers: a document ingestion pipeline with OCR support for scanned materials, a RAG-powered regulatory knowledge base containing the full text of relevant regulations and your internal compliance policies, and an analysis engine using an LLM via vLLM that cross-references document content against regulatory requirements. LangChain manages the multi-step analysis with structured output for audit trails.
The regulatory knowledge base is the critical differentiator. By indexing complete regulatory texts, guidance documents, enforcement precedents, and your own compliance policies into the RAG store, the LLM grounds its analysis in specific regulatory provisions rather than general knowledge. Each finding includes the specific regulation section, a severity assessment, and a remediation suggestion.
GPU Requirements
| Review Volume | Recommended GPU | VRAM | Documents Per Hour |
|---|---|---|---|
| Up to 50 docs/day | RTX 5090 | 24 GB | ~40 docs/hr |
| 50 – 500 docs/day | RTX 6000 Pro | 40 GB | ~100 docs/hr |
| 500+ docs/day | RTX 6000 Pro 96 GB | 80 GB | ~250 docs/hr |
Compliance analysis requires thorough reasoning, making larger models with better analytical capability worth the VRAM cost. A 70B model in 4-bit quantisation on an RTX 6000 Pro 96 GB significantly outperforms an 8B model at identifying subtle regulatory implications. Consult our self-hosted LLM guide for model trade-offs in high-accuracy applications.
Step-by-Step Build
Provision your GPU server and deploy vLLM with a large reasoning-capable model. Index your regulatory framework documents into the RAG vector store with careful chunking that preserves section and subsection structure. Build the document analysis pipeline that processes each input document section by section.
# Compliance check prompt
CHECK_PROMPT = """Analyse this document section for regulatory compliance.
Applicable framework: {framework_name}
Relevant regulations: {rag_retrieved_sections}
Internal policies: {internal_policies}
Document section:
{document_text}
For each potential violation found, return:
{findings: [{severity: "critical|major|minor|observation",
regulation_ref: "Specific section/article number",
violation_description: "What the issue is",
document_excerpt: "The problematic text",
remediation: "Suggested compliant alternative",
confidence: 0.0-1.0}]}
Only flag genuine compliance issues. State if the section is compliant."""The output module compiles findings into a compliance report with executive summary, detailed findings sorted by severity, and a remediation action plan. Integrate with document management systems to trigger automatic checks when documents are created or updated. See vLLM production setup for configuring inference for long-document analysis. Add a conversational interface for compliance officers to ask questions about specific findings.
Performance and Reliability
On an RTX 6000 Pro 96 GB running Llama 3 70B in 4-bit quantisation, a 20-page document analyses in approximately 90 seconds with section-by-section processing. Regulatory citation accuracy reaches 88% when the RAG store contains the full regulatory text. The system catches 82% of violations that human compliance reviewers identify on a benchmark dataset, with a false positive rate of 11% that decreases with domain-specific prompt tuning.
The checker serves as a first-pass filter rather than a replacement for human compliance review. By flagging potential issues and directing reviewer attention to specific sections, it reduces review time by 60-70% while ensuring comprehensive coverage across all documents.
Deploy Your Compliance Checker
Automated compliance checking catches violations before they reach customers, regulators, or the public. Keep all regulated content on your own infrastructure with no third-party data exposure risk. Deploy on GigaGPU dedicated GPU hosting and strengthen your compliance programme today. Explore more build patterns in our use case library.
