A year or two ago, a founder with an idea and no developers had two options: learn to code, or pay someone to build a first version. Now there's a third. You open an AI app builder or coding assistant, describe what you want, and by the weekend you have something that logs people in, saves data and looks surprisingly polished.
People call it "vibe coding," and honestly, we're fans. It means founders arrive at their first meeting with us holding a working prototype instead of a slide deck. We can click through it, see exactly what they mean, and skip weeks of guesswork.
But we've noticed a pattern. The prototype works well with one user (the founder) on a good day. Then real customers arrive, and problems show up that the demo never revealed.
This isn't a criticism of AI tools. They do exactly what they're asked: make the feature work. What they usually don't consider is the person trying to break in, the payment that fails halfway through, or the day two hundred people sign up at once. Those are still your job, or the job of whoever you hire.
Here's the checklist we work through when someone brings us an AI-built app and asks, "Is this ready?"
Key takeaways
- The most common and most expensive problems are exposed secrets, broken access control and database rules left open.
- Payments, validation and permissions must be enforced on the server, never trusted from the browser.
- Most AI-built prototypes can be hardened rather than thrown away, and even when they can't, they make an excellent specification.
1. Look for keys and passwords in the wrong places
This is the first thing we check, because it's the most common problem and the most expensive. AI-generated code often puts API keys, database passwords or payment keys directly into the front-end code, which every visitor's browser downloads. Anyone curious enough to open the developer tools can read them.
Secrets belong in server-side environment variables: never in the browser, and never in a public GitHub repository. If a key has ever been exposed, even briefly, assume it's compromised and replace it.
2. Check who can see what
Logging in is the easy part. The harder question is whether the app checks, on every single request, that this user is allowed to see this particular record.
Try it yourself. Log in as one user, open an invoice or profile page, and look at the URL. If there's an ID in it, change the number. If you can now see someone else's data, you have a broken access control problem. It sits at the top of the OWASP list of web security risks for a reason, and generated code misses it all the time.
3. Switch on your database security rules
Many AI builders set you up on hosted databases like Supabase or Firebase because they're quick to start with. Both are excellent, but both depend on access rules you have to configure (Row Level Security in Supabase, Security Rules in Firebase). If those rules are off, or left in a permissive test mode, your whole database may be readable by anyone who finds your project address.
Go through every table. Then test with a second, ordinary account and confirm it can only see its own data.
4. Validate everything on the server
Checking a form in the browser is good for usability, but it does nothing for security, because anyone can skip the browser and send requests directly. Every piece of data your server receives needs checking again on the server: right type, sensible length, nothing that could be read as a database command.
5. Don't trust the browser about payments
We've seen prototypes that mark an order as paid when the browser says "payment successful." That message can be faked. Payment status has to come from your payment provider directly, through signed webhooks that your server verifies.
While you're there, test the unhappy paths: a declined card, a refund, a customer who clicks "pay" twice.
6. Make sure you'll know when something breaks
If your app crashes at 3 a.m. your time while a customer in New York or London is using it, how would you find out? Probably from an annoyed email the next day.
A production app needs error tracking, uptime monitoring and alerts that reach a person. It also needs friendly error messages. A raw error dump on screen looks broken, and it can leak details about how your system works.
7. Test your backups by restoring one
Most hosted databases offer automatic backups. Few founders have ever tried restoring one. Do it once, on a copy, and time it. A backup you've never restored is a hope, not a plan.
8. Test with more than one user
Prototypes are usually tested by one person clicking around. Things change when many people use the app at once: slow database queries, missing indexes, huge images, and code that calls an external API once for every item in a list. AI tools write that last one surprisingly often.
The worst time to discover this is the day your launch post takes off.
9. Cover the privacy basics
If you collect personal information from people in the EU, UK, US, Canada or India, laws such as GDPR, UK GDPR, CCPA, PIPEDA or the DPDP Act may apply to you, even as a small startup. At a minimum you'll want:
- a real privacy policy;
- a reason for every piece of data you collect;
- a way for users to delete their account;
- a list of the third-party services that receive user data, including any AI provider your app sends text to.
10. Make sure a human can maintain it
AI tools produce code quickly, and often produce the same logic three slightly different ways in three different files. It works until you need to change something, and then fixing one thing breaks two others.
Before you build further on top of it, give the code a sensible structure, add tests for the flows that matter most (sign-up, payment, anything involving money or data), and make sure another developer could pick it up without a week of archaeology.
If you only have time for part of this list, do the first five before you take a single payment or store real customer data. Those five cover the problems most likely to turn into a data leak or lost money.
Fix it, or start again?
Founders often ask whether they need to throw the prototype away. Usually they don't.
If the data structure makes sense for the business and the code is reasonably easy to follow, hardening what exists is faster and cheaper. If every change causes new bugs, or the app is heading somewhere much bigger (say, a SaaS product for companies that will send you a security questionnaire before they sign), a structured rebuild is often the better investment.
Even then, the prototype is far from wasted. It's the clearest specification you could give a development team, and it shows exactly what you want your customers to experience.
Frequently asked questions
Is code written by AI tools safe to launch?
It can be, but don't assume it. AI tools optimise for "the feature works," not for security, error handling or scale. Review secrets, access control, database rules, validation and payments before real users arrive.
What's the single most common problem in AI-built apps?
Exposed secrets: API keys or database credentials sitting in front-end code or a public repository. It's quick to fix, but any key that was ever exposed should be replaced.
Do I need to rebuild my AI-built prototype?
Usually not. If the data model fits your business and the code is reasonably clear, hardening it is faster and cheaper. A rebuild makes sense when every change breaks something or you're selling to enterprise customers with strict security reviews.
How long does it take to make a prototype production-ready?
It depends on the size of the app and what the review finds. The first five checks in this list are often quick to fix; monitoring, load testing and code restructuring take longer. A review gives you a clear list before you commit to anything.
Where we come in
At SoftVue Technology, we help founders who have a working AI-built prototype and want it made safe, fast and ready for paying customers. Sometimes we review it and fix the gaps. Sometimes we rebuild it properly in Next.js, React, Node.js, Python or Flutter and deploy it on AWS, Google Cloud, Azure or Cloudflare. We'll tell you which makes sense for your situation.
There's no advance payment. We agree the scope, do the work, and you review the result before paying. If it doesn't meet what we agreed, you owe nothing. Delivered work comes with a 30-day bug-fix warranty, and ongoing support is available after that. We're happy to sign an NDA, and we schedule calls in your time zone.
If you've built something with AI and you're not sure it's ready for real customers, send us a link. We'll take a look and tell you plainly what needs attention.
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