Solutions
I built an app with AI, but it's not ready for real users
AI coding tools get you to a working demo fast — then stall at exactly the parts that make a real product: secure payments, correct data, access control, and code you can keep building on. That's normal, and it's fixable without starting over.
Tools like Lovable, v0, Bolt and Cursor are genuinely impressive at the start. You describe what you want, and within a day you have something you can click through and show people. That speed is real, and it's why so many good products now begin this way. But the demo is the easy 80%. The moment you need to charge a card, keep two users' data separate, enforce who can see what, or trust that a number in the database is actually correct, the ground gets much harder — and that's exactly where these tools tend to stall.
There's also a quieter risk. Studies of AI-generated code consistently find it ships with more security vulnerabilities than code written by an experienced engineer — exposed API keys, missing access checks, injection holes — because the model optimizes for something that runs, not something that's safe. None of this means your work is wasted. The prototype proved the idea and the interface; what it's missing is the engineering underneath. You don't have to start over — you have to build the foundation the demo skipped.
01 / Analysis
Signs this sounds like you
- 01It works in the demo but breaks when real users or real data show up
- 02You're nervous about taking payments through it — and you should be
- 03Adding a feature means the AI tool loops, breaks other things or burns credits
- 04No one has actually reviewed the code for security or correctness
02 / Analysis
Why it happens
- 01AI-generated code carries more serious issues than human code — exposed keys, injection, broken access control
- 02The tools are great at the first 80% and stall on payments, custom logic and integrations
- 03There's no real architecture underneath, so every change gets riskier
- 04It was never reviewed by an engineer before being treated as production
03 / Analysis
How I fix it
- 01I audit the AI-generated codebase and give you an honest verdict on what to keep
- 02I put real foundations underneath: data model, auth, roles and architecture
- 03I fix the security holes AI tools miss and add tests on the flows that matter
- 04I wire up real payments and integrations, then deploy it properly
How I'd approach it
I start with an honest audit, not a rebuild. I read through the AI-generated code and tell you plainly what's worth keeping — usually the interface, the flows and a good chunk of the product logic — and what has to be replaced because it can't be trusted with real users. You get a clear verdict instead of a salesman's pitch, so you know exactly what you're paying for and why.
From there I put real foundations underneath the parts we keep: a proper data model, real authentication and roles, and an architecture that makes the next feature easier instead of scarier. I close the security holes AI tools leave open, add automated tests on the flows that actually matter — payments, sign-up, anything touching money or private data — then wire up real payment and integration providers and deploy it the way a production app should be deployed. The point is to keep the momentum your prototype built, not throw it away and lose months.
The outcome
A product that keeps the momentum of your AI prototype but is now secure, correct and safe to charge money for — on foundations you can grow.
Proof from related work
AI coding tools get you to a working demo fast — then stall at exactly the parts that make a real product: secure payments, correct data, access control, and code you can keep building on. That's normal, and it's fixable without starting over.
Common questions
Are you going to throw away my prototype and start over?
Almost never. The prototype already did the hard creative work of proving the idea and shaping the interface, and that's worth keeping. I keep what's sound, replace only the parts that can't safely go to production, and I'll show you exactly which is which before any work starts.
Is my AI-generated code actually salvageable?
In most cases, yes. The front end and the product flows are usually fine or close to it; what's typically missing is the layer underneath — data integrity, access control, security and tests. I audit it first and give you a straight answer, so if starting fresh really is cheaper I'll tell you that instead of billing you to patch something broken.
Is it safe to take payments through my app as it is now?
If no engineer has reviewed it, I'd hold off. Payment flows are where small mistakes get expensive — mischarges, failed webhooks, or data that doesn't reconcile — and AI tools rarely get them fully right. I wire payments up against a real provider, add tests around the money paths, and make sure charges and records stay consistent before you go live.
How do you handle the security problems in AI code?
I go looking for the specific failures these tools are known for: exposed keys and secrets, missing or broken access control, and injection points where user input reaches your database. I fix them, add real authentication and role checks, and lock down the parts that were left open — so a curious user or a bot can't reach data or actions that aren't theirs.
The service that fixes this
AI Prototype to Production →
Vlad Sedenko
Web Product Developer · 10+ years
I personally scope, build and ship the fix — no account managers, no hand-offs. You work directly with the developer doing the work.
LinkedIn →