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Turning AI-generated code into production

A startup had a working AI prototype – but it was not production-ready: no authentication, no tests, no deployment, open security gaps. Within 4 weeks it became an application serving real users.

The problem

The prototype had been built quickly with AI support and looked impressive in the demo – but the foundations for production were completely missing: users could not log in securely, some data lived in the browser, there were no tests and no automated deployment. All of that had to be solved before the first real user.

The solution

First I fully understood the existing code (reverse engineering), then made it production-ready step by step: real authentication, clean data storage, tests, CI/CD and closed security gaps. The prototype was not thrown away – it was built out.

Step-by-step approach

• Code analysisFully understood the inherited AI code, documented data flows and vulnerabilities.
• Authentication & rolesReal user management with JWT auth and role-based permissions instead of a demo login.
• TestsUnit and integration tests for critical paths – including error and edge cases.
• CI/CD pipelineAutomated builds, tests and deployment steps so releases are no longer manual work.
• Security hardeningInput validation, secure password storage and closing typical vulnerabilities.