You built your product fast with AI tools — Cursor, Claude Code, Lovable, whatever got you to a working version. It runs, but it's not ready for real users: shaky error handling, weak auth and security, a messy database layer, and AI calls that break under real traffic or run up a surprise bill. I take that AI-built backend and make it production-ready — cleaning up the structure, hardening the APIs and data layer, fixing auth and security, adding proper error handling and retries, and making the AI integration reliable and cost-controlled. You keep your momentum; I make it something you can actually launch and maintain. (Backend, APIs, database, and AI integration — I focus on what's under the hood, not the frontend.)

Review & plan — Where the AI-generated code is fragile, insecure, or will break under load, prioritized by what matters first.

Backend hardening — Cleaner structure, proper error handling, retries, input validation, and removing the fragile patterns AI tools tend to leave.

Security & auth — Fixing the weak or missing authentication and access control that AI-generated code commonly ships with.

Database cleanup — Sorting out the data layer: schema, queries, indexing, migrations.

Reliable AI integration — Making LLM calls production-grade: error handling, retries, fallbacks, and cost control so they don't break or overspend.

Deployment — Getting it running properly (Docker, AWS), set up so your team can maintain it.