Your AI-built app works in the demo. You need it to work for customers.

AI prototype rescue and production hardening

A practical review and implementation engagement for AI-generated applications, unfinished MVPs, and prototypes that need authentication, data integrity, reliable integrations, and a release plan.

The situation

AI coding tools can help a founder reach a convincing prototype quickly. The next questions are different: which parts really work, what is stored, who can access it, and what happens when a service fails? This engagement starts by making the actual state of the application visible.

You do not need to know whether your prototype needs a rewrite. Bring the repository, the intended customer flow, and the problem you are seeing. I separate what can be kept from what needs repair, then scope a working release around the most important gaps.

How I approach it

Reproduce the customer journey

I run the app from a clean setup and follow its core flows with representative data. The initial review distinguishes implemented behavior from mocked screens, missing credentials, and actions that appear successful without completing.

Find the release blockers

Permissions, secret handling, data validation, migrations, and third-party integrations are checked before cosmetic cleanup. We prioritize faults by their effect on a customer or the business, not by the length of a lint report.

Repair with observable acceptance checks

Each agreed repair has a way to verify it. A sign-up must create the expected account, a paid action must reconcile correctly, and a failed request must leave recoverable state. This creates a basis for future changes.

Make the next change easier

The handoff includes an honest release checklist, environment documentation, and the tests or checks that protect critical behavior. Larger architecture changes are proposed where they reduce a concrete risk, not because the original code was written with AI.

What gets delivered

  • Codebase and critical-flow assessment
  • Prioritized release-blocker list
  • Scoped implementation of agreed repairs
  • Acceptance checks for customer workflows
  • Deployment and maintenance handoff

What it costs to get wrong

  • A prototype accepts payments before the corresponding fulfillment flow works.
  • Fixes in one generated component repeatedly break another undocumented dependency.
  • The team rewrites working parts while the actual release blocker stays unresolved.

Why you can believe this

My ai-os project is built around a principle relevant to rescue work: an agent's claim of completion is not a verification result. My independent app releases also provide evidence of taking software beyond the prototype stage.

Questions buyers ask

Can you take over code built with AI coding tools?
Yes. The origin of the code matters less than its current behavior and maintainability. I assess the repository, reproduce the important flows, and identify the work needed for a safe first release.
Will you need to rewrite my application?
That is a conclusion to earn through inspection. Some prototypes need targeted repairs; others have a structural problem. The initial assessment explains what can stay and why any larger change is recommended.
Can you review the app before I commit to a full build?
Yes. A bounded technical assessment can be a separate first engagement. The output is a prioritized list of findings and a release plan, so you can decide how to proceed.
What access do you need?
Start with a repository or code export, setup instructions, and a description of the critical user journey. Use development or staging accounts. Production access is only requested when there is a specific need and an agreed boundary.

Other capabilities