SneakUp
With AI Features (Visual Search, NLP Smart Search, Recommendation Engine)
Production-grade streetwear eCommerce with AI visual search, NLP smart search & real-time order tracking
// VIDEO SHOWCASE
FROM PORTFOLIO PROJECT TO PRODUCTION GRADE
SneakUp is a production-grade, full-stack streetwear and sneaker eCommerce platform — my first project engineered to senior-level standards. Originally built as a MERN stack portfolio project, it underwent a systematic 6-phase transformation addressing every dimension of production software: server-side price calculation (preventing payment fraud), JWT refresh token rotation with SHA-256 hashing, 4-tier rate limiting, Zod validation on every input, full-stack testing (Jest, Vitest, Playwright, k6), CI/CD with 5 parallel GitHub Actions jobs, Sentry error tracking, Pino structured logging, and comprehensive documentation (20+ files including architecture decisions, incident runbooks, and security audits). The platform features AI-powered visual search (CLIP + FAISS), NLP smart search, a personalized recommendation engine blending collaborative filtering + content-based similarity + trending, real-time order tracking via Socket.io, Google OAuth, PWA support, and an admin analytics dashboard — all deployed across Vercel, Railway, Hugging Face Spaces, and MongoDB Atlas for ~$0–5/month.
Multi-service architecture with 4 independently deployable services. Backend follows a layered pattern: Controller → Service → Repository with an event-driven side-effect system via typed EventBus. 13 middleware in precise stack order. 6 MongoDB collections with TTL indexes for automatic cleanup.