Vibe coding cleanup for apps that worked in the demo and broke in the wild

Vibe coding got you further than anyone expected. A working signup, a dashboard, a payment flow, all in a weekend. Then a hundred people used it at once, or one of them opened the network tab, and it turned out the thing that looked finished was a demo with a domain name pointed at it.

That is not a failure of nerve, it is a failure of the middle. Models are excellent at producing code that runs and indifferent to code that keeps running: no auth boundaries, no database indexes, secrets in the client bundle, and every screen quietly refetching the entire table. A vibe coding cleanup specialist is just an engineer who reads what the model wrote, decides what is worth keeping, and rebuilds the rest properly.

Every cleanup we do finds a version of the same short list. That is not a coincidence, it is a property of how the code was produced. A model optimises for the request in front of it: make this page work. Nothing in that request implies that the endpoint behind the page should check who is calling it, that the table should have an index, or that the key should live on the server.

So you get software that is locally correct and globally wrong. Each screen works. The system does not. And because all of it runs, nothing tells you until the moment it matters: a bill, a breach, or a launch where the database gives up at forty concurrent users.

The instinct with inherited AI code is to throw it away, and it is usually the wrong instinct. What you built encodes real product decisions: which fields you actually need, what the flow is, which features users ignored. That knowledge cost you weeks and it is not written down anywhere.

So we treat the app as a specification rather than a codebase. The screens, the flows, and the data you have collected stay. The parts that decide whether it survives, the schema, the auth boundary, the queries, the deploy path, get rebuilt on purpose. You do not lose the product. You lose the fragility.

What catches most people is not the engineering, it is the cost of running code nobody supervised. A page that refetches the whole table on every render is invisible while you are the only user. At a thousand users it is a database bill growing faster than your revenue, and the fix is not a bigger plan, it is an index and a rewritten query.

The same applies to model spend. AI features written by AI tend to send far more context than the task needs, retry without a cap, and reach for the largest model available because that is what the example used. Cleaning that up is often the fastest money we save anyone.

This works when there is a real product underneath: people using it, or a launch date that matters, and code that broadly does what you meant. It is a repair job, and repair jobs need something worth repairing.

It is the wrong service if you have a prototype with no users and no evidence anyone wants it. Cleaning that up is paying engineering prices to harden a guess. In that case we will point you at a scope sprint and a proper first build instead, which costs less and answers a more useful question.