Rescuing AI-built apps: what breaks first, and how to fix it

Tools like Lovable, Bolt and Replit have made it possible for a founder with no engineering team to build a working app in days. That is a genuinely good thing. It lets people test an idea with real users before spending serious money.

The trouble tends to start after that. Real users arrive, a change breaks something unrelated, and each new prompt seems to fix one thing and damage two others. If that sounds familiar, your app is probably not doomed. It has reached the point where it needs engineering, and that point comes with a fairly predictable list of problems.

Security is usually the first real problem

This is what I would check first, because it can hurt you before anything else does. Generated apps often work perfectly for the person testing them and still let one user read another user's data.

Look for API keys in client-side code, database rules that are missing or too open, admin routes protected only by hiding the button, and server endpoints that trust whatever the browser sends. If your app handles payments, health details or personal data, get this reviewed before you do anything else.

Data that does not hold together

The database is often shaped by whatever the last prompt needed. You may find the same information stored in three places, no constraints to stop bad records, and no real migration history. Everything seems fine with ten users and starts to go wrong with a thousand.

This matters more than ugly code. Code can be replaced one piece at a time. Bad data lives on, and every feature built on top of it inherits the problem. Check your backups too. Many of these apps have never had a restore tested, and some have no backups at all.

Why every change breaks something else

Each prompt tends to be answered without a full view of the system, so the same logic ends up duplicated in several places with slight differences. Fix the price calculation in one and the others stay wrong. With no automated tests, nobody finds out until a customer does.

Error handling has the same pattern. The happy path was what got demoed, so failures are often swallowed silently. A payment fails, nothing is logged, and the user sees a spinner. Without logs or monitoring, you are left guessing what happened.

Fix it or rebuild it?

Founders often assume they must start again, and developers are often too quick to agree. A full rebuild is expensive and carries its own risks, so it should be the conclusion of a review, not the starting point.

Fixing in place usually makes sense when the data model is mostly sound, the stack is one a team can maintain and the problems are concentrated in a few areas. A rebuild starts to make sense when the data model is fundamentally wrong, the security problems run through everything, or the platform itself cannot support what the product needs next. Even then, the existing app is valuable. It is a working specification that has already been tested by real users, which most new projects never get.

A sensible order of work

Start by getting the code into version control you own, with a separate staging environment, so changes stop going straight to production. Then close the security holes, set up backups and add logging, so you can see what is really happening.

After that, write tests around the flows that make you money, such as sign-up and checkout. Those tests are what make the remaining cleanup safe. Only then start paying down the duplication and reworking the data, one area at a time, while the app keeps running.

None of this means the AI tool was a mistake. It got you to real users quickly, which is what it is best at. If you have reached the next stage and want an honest second opinion, our technical consulting team reviews and recovers exactly these codebases. For the wider picture, read how LLMs are reshaping software development.