Production-readiness reviews
I can look over an AI-built prototype, early-stage product or existing app and give you a clear view of what is safe to keep, what is risky, and what should change before customers, payments or sensitive data depend on it.
UK founders, solo builders and small teams with a prototype or early product that needs a calm developer review before launch, fundraising, handover or further build work.
Quick answers
A production-readiness review is a focused technical check of whether an app is ready for real users. It looks at the product flows, data handling, reliability, deployment shape and maintainability risks that can turn a promising prototype into a painful launch.
Yes. AI-generated apps can be useful starting points, but they often need a human review before people rely on them. I look for the places where the app only appears finished: duplicated logic, weak auth, missing edge cases, confusing data models, brittle integrations and code that will be hard to change later.
You get a clear written summary, a prioritised fix list, and a recommendation on what to do next. The point is not to produce a thick report; it is to give you enough technical clarity to decide whether to launch, pause, rebuild or invest in focused fixes.
What this covers
Production-readiness reviews
AI-built app reviews
Technical health checks
Prioritised fix list and next steps
Launch, pause, rebuild or fix recommendation
Risk areas
Authentication and access-control mistakes
Fragile user flows or missing error states
Sensitive data handling and privacy risk
Payment, email or third-party integration issues
Duplicated code, unclear state and brittle AI-generated logic
Deployment, environment and maintainability problems
Examples
A founder has built a working demo in Lovable, Bolt, v0, Cursor or Replit and needs to know what is safe to keep before adding users.
The app looks convincing enough to show people, but the team wants a developer's view of the risks before promises are made.
A small team has taken over a codebase and needs a practical read on whether to fix, rebuild or narrow the next phase.
Questions
Yes. I can review AI-generated apps from tools such as Lovable, Bolt, v0, Replit or Cursor and explain which parts are useful, fragile or risky before you rely on them.
It can include obvious security and data-handling risks, but it is not a replacement for a formal penetration test.
Often, yes. If the app is a good fit, the review can turn into focused rescue or build work on the highest-priority fixes.
No. It can also be useful before a funding conversation, customer demo, handover, rebuild decision or new phase of development.
Related writing
Thoughts on the vibe coding movement, AI product builders, and where generated prototypes still need engineering judgement.
My experience using AI pair programmers since GitHub Copilot, from early autocomplete to agentic tools like Cursor and Claude Code.
Send a note with a little context and I will let you know whether it sounds like something I can help with.
