Most AI builds stall after the prototype. The model works in a notebook, then breaks the moment real users, real data, and real billing show up. I build the whole thing: the interface people actually use, the backend that keeps it running, and the AI layer wired in properly.

A complete web application built with Next.js, TypeScript, and Tailwind

LLM integration (OpenAI, Claude, or Gemini) with streaming responses, retries, and sane error states

Auth, database, and file storage set up with Supabase or Postgres

Usage tracking and rate limiting so your token costs stay predictable

Deployed to Vercel with environment configs and a handover walkthrough

Founders validating an AI product with real users

Agencies who need an AI feature added to an existing platform

Teams with a working prototype that needs to become a real product

Short call to map the core user flow and pick the stack

Fixed scope and timeline, no hourly guessing

Staging link updated as I build, so you see progress weekly

Handover with full source, docs, and two weeks of post launch support