I build complete AI-powered SaaS products end-to-end — not prototypes, not demos. From the first line of code to a deployed, observable, paying-customer-ready application.
What I deliver:
• Full-stack application architecture (frontend + backend + database + infrastructure)
• Authentication & user management (JWT, OAuth, magic links)
• Subscription billing with Stripe (one-time, recurring, usage-based)
• Admin dashboards for internal team management
• AI features integrated where they actually add value (RAG, classification, generation, search)
• Multi-tenant data isolation when needed
• Email systems (transactional + marketing)
• Production deployment with monitoring and error tracking
• CI/CD pipelines so future features ship safely
My approach:
I focus on what actually moves an MVP — clear scope, fast iteration, production-readiness from day one. No half-built features, no demo-only auth, no "we'll add observability later." Every MVP I ship has real auth, real billing, real monitoring, and a real path to scale.
Stack: React · Next.js · Node.js · FastAPI · MongoDB · PostgreSQL · Stripe · OpenAI · NextAuth · AWS · Docker · GitHub Actions
Recent work:
• Lecture Study — AI-powered EdTech MVP turning YouTube lectures into structured study sessions. Built end-to-end including auth, persistent user state, AI study guide generation, MCQ engine, and per-lecture RAG chat.
• RAG Workspace — Multi-tenant SaaS knowledge-base platform with workspace isolation, role-based admin console, JWT auth, and document management.
• DashBot — Embeddable customer-support chatbot with full analytics dashboard, deployed on Railway with Qdrant + Groq.
Best fit for: Founders raising on a deployed MVP (not slides) · solo founders who need a technical partner for a single sprint · companies validating an AI feature before committing engineering headcount.