Engineering Compliance Intelligence: How We Built Enterprise AI That Works
A production RAG system that delivered 60% efficiency gains without compromising regulatory accuracy
The Challenge
A global organization operating under strict marketing regulations needed to automate compliance review for images, PDFs, videos, and audio files. Traditional manual processes created bottlenecks, while early AI experiments produced unreliable outputs that hallucinated rules and failed audit requirements.
Why Standard RAG Failed
Context contamination when mixing document types
Models invented compliance positions that didn't exist
Ambiguous regulatory language caused contradictions
No traceability to source documents
"Success required deliberate orchestration, not plug-and-play automation."
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Three-Tier System Architecture
Frontend Layer
- React 18 + TypeScript
- Radix UI components
- Role-based access
- Bilingual (EN/NL)
Backend Layer
- Supabase PostgreSQL
- Row-Level Security
- Edge Functions
- JWT Authentication
AI Layer
- Azure OpenAI GPT-4o
- Cognitive Search
- Document Intelligence
- Video Indexer
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Download the 20-page technical report with architecture diagrams, RAG pipeline design, and code patterns.
The Five-Stage RAG Pipeline
The Breakthrough: Document-Type Segmentation
Generic RAG failed because retrieving all document types together caused context contamination. We segmented by type (claims, rules, training, dossiers) with weighted retrieval.
Solving the Hard Problems
Hallucination Prevention
Models invented rules. Solution: Mandatory citation to source documents with SharePoint URL tracking. Every compliance judgment must include direct quotes with hyperlinks. If the model can't provide a source, the response is rejected.
Prompt Governance
Success came after 47 iterations. Prompt versioning and A/B testing became critical. We treat prompts as code: version control, peer review, performance metrics, and rollback capability. Two-phase prompting improved consistency by 35%.
Multimodal Processing
Handled PDFs, images, video, and audio through Azure Document Intelligence and Video Indexer. Each content type has a specialized extraction pipeline. Video transcripts include speaker identification and timestamp indexing.
Security & Compliance
Row-level security, complete audit trails, and role-based access for regulated environment. Every API call is logged with user context. Data residency requirements met through Azure region selection. SOC 2 Type II compliant.
Measurable Impact
Key Lessons for Enterprise AI
Complete Tech Stack
Frontend
Backend
AI Services
Security
Storage
DevOps
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This case study demonstrates that production RAG requires architectural discipline, metadata governance, and iterative calibration—not just API integration.
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