How to take an AI-built app to production.
AI app builders are genuinely good at getting you to a working v1. You describe what you want, and minutes later there is something you can click. That is real, and it is worth using. The trouble starts later, once the prototype has real users and real data, and the same speed that got you here starts quietly working against you.
I'm Luna Lamb. I have spent nearly two decades building production software, and I founded and shipped AutomatePOD and SimplifyLocal, so I have not just been hired to clean these problems up, I have hit them in my own code and fixed them. Here is what actually breaks in AI-generated codebases, how to tell how bad yours is, and how to decide between patching, refactoring, and rebuilding.
Tell me what you built and where it is stuck. If it makes sense to look at the code, a read-only invite you can revoke afterward is all I need. NDA if you want one. No pitch.
What actually breaks in AI-generated codebases.
None of this means the tools failed. They got you to a working demo, which is what they are for. These are the things a demo does not need and a real product does.
Security and access control
AI tools wire up authentication so the happy path works, but authorization is where they cut corners. You get endpoints that check whether you are logged in but not whether the record is actually yours, and admin actions guarded only by a hidden button. It looks fine in a demo where you are the only user, and it is a real problem the moment you are not.
Data isolation between users
The moment you have more than one customer, every query needs to be scoped to the right account. Generated code frequently fetches by ID alone, so any logged-in user can read or edit someone else's data by changing a number in the URL. On Supabase- and Firebase-backed apps this shows up as row-level security or rules that were never really turned on.
Secrets and API keys
Keys and tokens end up in client-side code or committed to the repository, because that is the shortest path to a working feature. Anything in the browser bundle is public. Rotating a leaked key after launch, once it is wired through half the app, is a bad afternoon.
No tests
There usually are not any. That means every change is a manual click-through, regressions ship silently, and nobody can refactor with confidence because there is no safety net telling them what broke.
Structure and maintainability
Logic copy-pasted across a dozen files, one 2,000-line component, no clear boundaries. It runs, but every new feature takes longer than the last, and eventually a small change breaks something unrelated. This is the quiet tax that makes a growing AI-built app feel like it is fighting you.
Error handling and edge cases
The happy path is covered. Empty states, failed network calls, partial data, concurrent edits, expired sessions, the things that make software feel broken to real users, usually are not.
How to audit an AI-built app.
If you want to gauge the state of a codebase before deciding what to do with it, this is the order I work in. You can run most of it yourself.
- 01Read the auth and data layer first. Pick any endpoint that returns user data and ask what stops a different user from calling it. If the answer is that the UI does not show the button, that is a hole.
- 02Grep for secrets. Search the client bundle and the repository for API keys, tokens, and connection strings. Anything sensitive that reaches the browser needs to move server-side.
- 03Check tenant scoping. Try to reach another account's data by changing an ID. On Supabase or Firebase, review the row-level security policies or rules directly rather than trusting the UI.
- 04Look for tests and CI. None is a finding, not a surprise. It tells you how much safety you are building from zero.
- 05Map the shape of the code. How many places would you have to touch to change one thing? That number is your maintainability tax.
Refactor or rebuild?
This is the question everyone asks, usually while frustrated. It is worth answering calmly, because the wrong call is expensive either way.
- Refactor when the data model is basically right and the problems are in how the code is organized. Most AI-built apps are here: the idea is sound, the schema is close, and what is needed is structure, tests, and closing security holes. You keep the work that got you traction.
- Rebuild when the foundation is wrong: the data model cannot represent what the business actually needs, the framework choice fights you at every turn, or the generated code is so tangled that understanding it costs more than rewriting it. This is rarer than it feels in a moment of frustration.
- The honest default is refactor. Rebuilding throws away validated product decisions along with the messy code, and a rewrite usually re-learns the same lessons the slow way.
Signs it is time for a real engineer.
- You have paying users or real data, and you are afraid to deploy.
- You have hit a bug you cannot reproduce or fix, and the AI tool keeps making it worse.
- You need something the tool will not do, a real integration, a compliance requirement, custom logic, and you have hit the platform's ceiling.
- You are spending more time fighting the codebase than building the product.
If any of that sounds familiar, that is what AI Code Rescue is for. Tell me what you are building on the contact page and I will tell you honestly what it needs.
Rescue guides by tool.
AI Code Rescue is a core part of what I do. These are the tools I see most often, and what rescuing an app built with each one actually involves.
Lovable
Built an app with Lovable and hit a wall? What Lovable does well, where its apps break as they grow, and what it takes to make one production-ready.
Rescue a Lovable app →Bolt
Built something with Bolt.new that is getting hard to maintain? What Bolt is great at, where its apps break as they grow, and how to make one production-ready.
Rescue a Bolt app →v0
v0 builds beautiful React UIs fast. Here is where v0 projects fall short as real apps, and what it takes to turn one into a production-ready product.
Rescue a v0 app →Replit Agent
Replit Agent builds and hosts a whole app in one place. Here is where those apps hit limits as they grow, and what it takes to make one production-ready.
Rescue a Replit Agent app →Base44
Base44 builds business apps fast with a batteries-included backend. Here is where its apps hit limits, and what taking one to production really involves.
Rescue a Base44 app →Common questions.
What does it mean to take an AI-built app to production?
It means turning a working prototype into software real users can depend on: closing the security and data-isolation holes AI tools leave open, moving secrets server-side, adding tests so changes are safe, and reorganizing the code so it can be maintained and extended. The app already works in a demo; production is about making it hold up when the people and the data are real.
Is AI-generated code bad?
No. AI tools are genuinely good at getting you to a working first version, and that head start is valuable. The issue is not that the code is bad, it is that these tools optimize for a working demo, not for security, maintainability, or scale. What they leave behind is fixable.
Should I refactor or rebuild my AI-built app?
Refactor, in most cases. If the data model is roughly right and the problems are in structure, tests, and security, you keep the product you have validated and clean up around it. Rebuild only when the foundation genuinely cannot support what the business needs, which is rarer than frustration makes it feel.
How much does it cost to fix an AI-generated app?
It depends entirely on what is there. A focused security-and-tests pass is a very different job from re-platforming an app off a locked-in builder. The honest first step is small and low-risk: an audit. I read the code, tell you plainly what is wrong and how serious it is, and scope the work from there, so you are deciding with facts instead of guessing.
Why hire you instead of an agency?
You work directly with the person who reads your code and writes the fixes, not a salesperson who hands you off to a junior. I have built and shipped my own products, so I have fixed these exact problems in code I owned, not just billed for them by the hour. And if it is a quick fix, or does not need me at all, I will tell you.
Tell me what you’re trying to build. If I’m the right fit, I’ll tell you how I’d approach it, and if I’m not, I’ll point you somewhere better.
Tell me what you’re buildingI read every message myself. Usually a reply within a couple of business days.