AI Prototype Stabilization

We started where many AI-generated products start: a working prototype that looked finished and behaved unpredictably the moment real users touched it. The team had moved fast with AI assistance and produced something that demonstrated the idea, but the codebase had no tests, no CI, fragile authentication, and an architecture that no single person could fully describe.

Our first move was a structured codebase review. We read the entire system, mapped the real architecture against the intended one, and produced a written list of the risks that would actually hurt the business. We did not pad the report. We listed the issues that would cause incidents, the issues that would block growth, and the issues that could be safely deferred.

From there we worked in small, reversible steps. We added the smallest set of automated tests that protected the regressions the team had already lived through. We stood up a CI pipeline that ran build, lint, and test on every change. We hardened authentication and tightened secret handling. We refactored only the parts of the system that blocked safe delivery and left the rest alone.

At the end of the engagement we handed back a system the team could operate with confidence, a written runbook, and a roadmap for the modernization work that came next. The team owned the system after we left and did not need us to keep it running.