AI-powered regulatory compliance automation for multi-jurisdictional crypto operations
Cryptocurrency businesses face an unprecedented challenge in navigating the complex, ever-changing regulatory landscape across multiple jurisdictions. Manual compliance processes are time-consuming, error-prone, and struggle to keep pace with regulatory updates, creating significant operational risks and compliance gaps.
A sophisticated AI agent leveraging Retrieval-Augmented Generation (RAG) and LangGraph orchestration to automate regulatory compliance monitoring. The system continuously ingests regulatory updates, analyzes their impact, and generates actionable compliance reports in real-time.
6-Node LangGraph RAG System: Query Analysis → Document Retrieval → Multi-jurisdiction Analysis → Gap Identification → Cost/Timeline Extraction → Structured Output. Grounded in 17 official regulations to prevent hallucinations
Phase 1: RAG Foundation - ChromaDB setup, regulation embedding strategy, vector database optimization
Phase 2: LangGraph Workflow - 6-node agent orchestration for explainability and debugging
Phase 3: Multi-jurisdiction Logic - Coverage for 5 jurisdictions (US, EU, Singapore, UK, UAE)
Phase 4: Output Structuring - Severity levels, exact costs, deadlines, and actionable gap identification
Phase 5: Optimization - Deployment size reduction from 50GB+ → 85MB for HuggingFace constraints
Transferable skills and capabilities beyond the technical implementation
Identified real problem wasn't "following rules" but avoiding $50K-200K legal fees per jurisdiction. This insight led to RAG system that provides specific costs and deadlines.
Grounded system in 17 official regulations to prevent hallucinations. Domain expertise catches subtle errors AI alone misses (example: VARA STO vs VAL distinction).
Debugged VectorDB returning 0 regulations - root cause was creating NEW instance on every search. Implemented singleton pattern for consistency.
HuggingFace 50GB limit exceeded with torch dependencies. Removed local models entirely, switched to HuggingFace Inference API. Optimized from 50GB+ to 85MB.