Regeolation

Inspiration

Modern software often faces complex regulatory requirements across different jurisdictions (e.g., GDPR in Europe, PDPA in Singapore, HIPAA in the U.S.). Developers lack efficient tools to automatically detect compliance risks early in the development cycle. JusticeGang addresses this by embedding compliance analysis directly into GitHub repositories and pull requests, offering real-time detection and guidance.

What it does

JusticeGang is an AI-powered compliance assistant designed to help developers and auditors detect geo-specific regulatory issues directly within their GitHub workflows. The platform transforms compliance detection from a blind spot into a traceable, auditable process by combining automated code scanning, regulatory analysis, and interactive assistance.

How we built it

Regeolation is built as a modern, full-stack AI-powered compliance detection platform using cutting-edge technologies: Frontend: Next.js 15 with React 19, TypeScript, and Tailwind CSS for a responsive, modern UI Backend: FastAPI + Python with LangChain integration for AI-powered analysis AI Engine: OpenAI GPT-4 integration with fine-tuning capabilities for domain-specific compliance knowledge Database: Supabase (PostgreSQL) with real-time capabilities and Row Level Security GitHub Integration: Octokit API + GitHub Actions for automated PR analysis Architecture: Monorepo structure with pnpm workspaces for efficient development The system automatically analyzes GitHub pull requests using AI to detect geo-specific compliance requirements, providing real-time feedback and comprehensive reporting.

Challenges we ran into

Complex Database Schema: Designing a flexible schema that could handle multiple regulatory frameworks, user jurisdictions, and compliance analysis results GitHub Actions Integration: Debugging webhook signatures, PR analysis triggers, and automated compliance checking workflows AI Model Integration: Fine-tuning LLMs for domain-specific compliance knowledge and handling complex regulatory scenarios Multi-jurisdiction Support: Building a system that could automatically detect and apply different compliance rules based on user location and repository settings Real-time Updates: Implementing efficient polling mechanisms for compliance analysis results and dashboard updates *Type Safety: Maintaining strict TypeScript and Python type safety across the full stack while integrating multiple APIs

Accomplishments that we're proud of

Complete AI-Powered Pipeline: Built an end-to-end system that automatically detects compliance issues in code changes Real-time Compliance Analysis: Successfully integrated GitHub Actions with AI analysis for instant compliance feedback Modern Tech Stack: Successfully built a production-ready application using the latest technologies (Next.js 15, React 19, FastAPI) Automated Workflow: Implemented a seamless developer experience where PRs automatically trigger compliance analysis Comprehensive Dashboard: Built a professional UI that provides clear compliance insights, analytics, and actionable recommendations Scalable Architecture: Designed a system that can handle multiple repositories, users, and regulatory frameworks

What we learned

AI Integration Complexity: Successfully integrating LLMs with real-world compliance scenarios requires careful prompt engineering and domain knowledge GitHub API Mastery: Deep understanding of GitHub's webhook system, OAuth flows, and Actions for automated compliance checking Regulatory Knowledge: Gained insights into how different jurisdictions handle data protection, child safety, and content moderation Full-Stack Development: Mastered the challenges of building a complex application with multiple integrations and real-time features Database Design: Learned to design schemas that can handle complex compliance relationships and regulatory requirements DevOps Integration: Successfully integrated CI/CD pipelines with compliance checking for automated quality assurance

What's next for Regeolation

AI Model Fine-tuning: Implement custom fine-tuning of Mistral models with domain-specific compliance knowledge and internal jargon Advanced Analytics: Add machine learning insights for compliance trend analysis and risk prediction Multi-Language Support: Extend compliance detection to support more programming languages and frameworks Enterprise Features: Add team collaboration tools, audit trails, and compliance reporting for enterprise customers Regulatory Updates: Build automated systems to keep regulatory frameworks updated with latest changes API Marketplace: Create an ecosystem where developers can build compliance plugins and integrations Global Expansion: Support more jurisdictions and regulatory frameworks worldwide