Overview
Implementation of PrivyLoop, an open source privacy monitoring platform with dual deployment models (self-hosted + cloud service). The system automates privacy settings analysis across major platforms using browser extension scraping, AI-powered explanations, and a comprehensive dashboard for change tracking.
Architecture Decisions
Monorepo Structure
- Single repository with Business Source License for dual deployment model
- Feature flagging system to enable/disable cloud vs self-hosted features
- Package-based organization: core, enterprise, web, extension, shared
Technology Stack
- Frontend: Next.js 15 with shadCN/ui components and Tailwind CSS
- Backend: Next.js API routes with Drizzle ORM
- Database: PostgreSQL (self-hosted) vs Supabase (cloud)
- AI Processing: Google Gemini API for privacy analysis
- Browser Extension: Manifest V3 with secure, scalable architecture
Deployment Strategy
- Self-hosted: Docker Compose with minimal dependencies (PostgreSQL only)
- Cloud service: Vercel + Supabase + managed services
- Pricing model: Free (3 cards) → Pro ($4.99/mo) → Premium ($7.8/mo with AI agent)
Implementation Strategy
Phase 1: MVP (Months 1-6)
- Core monorepo structure and feature flagging
- Basic dashboard with 3-platform support (Google, Facebook, LinkedIn)
- Browser extension with security architecture
- Self-hosted Docker deployment
- Cloud service with Free/Pro tiers
Phase 2: Scale & AI Agent (Months 7-12)
- Premium tier with LangGraph + Inngest AI agent
- Advanced analytics and reporting
- Additional platform support (5+ platforms)
- Enterprise features and compliance
Task Breakdown
Task Summary:
- Total tasks: 10
- Parallel tasks: 5 (001, 004, 006, 007, 008)
- Sequential tasks: 5 (002, 003, 005, 009, 010)
- Estimated total effort: 26-33 days (208-264 developer hours)
Critical Path: 001 → 002 → 003 → 007 → 009 → 010 (foundation → database → auth → extension → integration → deployment)
Success Criteria (Technical)
Performance Benchmarks
- Dashboard Load Time: <2s on 3G networks
- Extension Response: <500ms for privacy page scanning
- Database Queries: <200ms for dashboard data retrieval
- API Response: <1s for AI analysis generation
Quality Gates
- Test Coverage: >80% unit test coverage, >70% integration coverage
- Security Scan: No high/critical vulnerabilities in security audit
- Extension Approval: Successful submission to Chrome Web Store and Firefox
- Accessibility: WCAG 2.1 AA compliance verification
Business Metrics
- Self-hosted Adoption: 100+ GitHub stars, 50+ Docker pulls within 3 months
- Cloud Service Growth: 1,000+ registered users, 60% 30-day retention
- Platform Coverage: 3+ major platforms with 95%+ accuracy in MVP
📋 Epic created from: docs/privyloop-PRD.md
Overview
Implementation of PrivyLoop, an open source privacy monitoring platform with dual deployment models (self-hosted + cloud service). The system automates privacy settings analysis across major platforms using browser extension scraping, AI-powered explanations, and a comprehensive dashboard for change tracking.
Architecture Decisions
Monorepo Structure
Technology Stack
Deployment Strategy
Implementation Strategy
Phase 1: MVP (Months 1-6)
Phase 2: Scale & AI Agent (Months 7-12)
Task Breakdown
Task Summary:
Critical Path: 001 → 002 → 003 → 007 → 009 → 010 (foundation → database → auth → extension → integration → deployment)
Success Criteria (Technical)
Performance Benchmarks
Quality Gates
Business Metrics
📋 Epic created from: docs/privyloop-PRD.md