Stabilize the foundation
Lock down auth, roles, database access, storage permissions, secrets, environment variables, and third-party integrations.
Productionize AI prototype
Turn the prototype that works into the app you can launch.
AI tools are excellent at getting a product into a working state. Productionization is the next step: hardening the app so it can handle data, users, payments, deploys, errors, and growth without surprising you.
How do you productionize an AI prototype?
Start by finding the production blockers. A founder does not need every line rewritten before launch. The right sequence is to fix the issues that can expose data, break workflows, create bills, or make recovery impossible.
Make release safe
Add repeatable deployment steps, migrations, backups, rollback paths, branch discipline, and a clear distinction between staging and production.
Prepare for usage
Check performance, error handling, observability, support workflows, alerts, dependency risk, and the failure paths that a demo never exercises.
The deliverable is a remediation roadmap.
The written review gives you a prioritized plan, not a vague list of concerns. Each item should explain the risk, the business impact, and what an engineer should fix next.
Launch blockers
Issues that can expose private data, break critical workflows, create uncontrolled spend, or prevent recovery from a bad deploy.
Near-term fixes
Issues that should be fixed soon after launch, such as observability gaps, brittle dependencies, weak error handling, or missing operational docs.
Cleanup
Quality improvements that matter, but should not distract from the production risks that actually block launch.