GeoFlow - Multi-Agent Regulatory Compliance Assistant

Inspiration 🚀

TikTok launches hundreds of features globally, each needing compliance with dozens of privacy laws—from GDPR to Indonesia's Child Protection Law. With 95% of teens using social media "almost constantly," regulatory scrutiny is at an all-time high. Yet companies still play regulatory whack-a-mole, scrambling when auditors inquire.

We asked: What if we could automate regulatory detection before it becomes a blind spot?

What it does ⚡

GeoFlow analyzes feature descriptions and instantly determines: "Does this require geo-specific compliance logic?"

🎯 Binary Decisions: Clear ✅ FLAGGED or ❌ CLEAR with evidence-based reasoning
🌍 Multi-Jurisdictional: Evaluates compliance across global markets simultaneously
📋 Regulation Mapping: Identifies specific applicable laws when needed
📊 Audit Trails: Complete documentation for regulatory inquiries

Examples:

✅ "Feature reads user location to enforce France's copyright rules"
❌ "Geofences feature rollout in US for market testing" (business ≠ legal)

How we built it 🛠️

Multi-Agent Architecture: Five specialized AI agents working as a coordinated team:

Boss Agent: Orchestrates analysis
Regulatory Expert: Identifies applicable regulations
Compliance Critic: Evaluates risks
Risk Resolver: Develops mitigation strategies
Great Engineer: Provides technical guidance

Tech Stack:

LangGraph + LangChain for agent orchestration
ChromaDB for semantic search across legal documents
OpenAI + Mistral APIs
Next.js + React for the dashboard

Self-Evolving Intelligence: Uses biological evolution principles (Crossover and Mutation)—the system learns from human feedback and continuously improves accuracy.

Challenges we ran into 💪

Legal vs. Internal Jargon: Teaching agents to understand both complex regulations and internal codenames
Multi-Agent Coordination: Getting five AI experts to collaborate without chaos
False Positive Balance: Being cautious without over-flagging
Semantic Legal Search: Legal documents require deeper contextual understanding than typical text

What we learned 📚

Multi-agent beats single model for complex domain problems
Human-AI feedback loops are critical for regulatory nuance
Context is king in legal AI—semantic understanding preserves regulatory intent
Precision + explainability beats perfection without reasoning