AI-built app not ready for real customers? What to check and fix

Why do AI-built apps stall before launch?

AI tools are strong at producing the visible parts of an app from a description, so the first version arrives fast. Launch needs work that a prompt rarely mentions: permissions, failure handling, payments, deployment and monitoring. Owners then reach the point where each new prompt breaks something, and nobody involved can read the code.

This matches what developers themselves report. In Stack Overflow's 2025 Developer Survey, 66% of respondents named AI solutions that are almost right, but not quite, as a frustration, and 45.2% said debugging AI-generated code is more time-consuming. Those respondents can read code. An owner working from prompts alone has fewer ways to tell a correct change from a lucky one.

None of this makes AI tools bad. They are useful for prototypes, internal tools with no sensitive data, and for trained engineers who review the output. The risk appears when a prototype becomes the live product without anyone changing how it is checked.

What security problems do AI-built apps usually have?

The recurring problems are missing access rules, secret keys exposed in code that browsers download, and absent security controls. Published tests and scans show the same pattern across tools. The app looks finished because login works, while another user, or a stranger calling the database directly, may still reach records they should never see.

Several public sources point the same way. Veracode reports that AI-generated code introduced risky security flaws in 45% of its tests across more than 100 language models. Escape scanned over 5,600 publicly available apps from vibe coding platforms and reported more than 2,000 vulnerabilities, over 400 exposed secrets and 175 instances of personal data. Escape notes that its sample leans heavily towards one platform and came from a single point in time. Both companies sell security products.

CVE-2025-48757 records a Row-Level Security flaw in sites generated by Lovable. The supplier disputes it, saying each customer is responsible for protecting their own application's data. The dispute carries a lesson for owners: when a platform says security is your job, someone on your side has to do that job.

In February 2026, Wiz reported that Moltbook, a site its founder had vibe-coded, exposed 1.5 million API tokens and 35,000 email addresses. A database key sat in client-side JavaScript and Row Level Security was missing. It came down to one setting that nobody had checked.

• Access rulesLog in as one customer and try to open another customer's record by changing the ID in the address or request.
• Exposed keysSearch the code sent to the browser for secret keys. Keep secrets on the server and replace any that leaked.
• Database rulesConfirm that the database refuses reads and writes that do not pass a rule, including requests made outside the app.
• Business rulesTry negative quantities, zero prices and repeated submissions. Tenzai found that most agents allowed negative orders unless told otherwise.

Can AI tools damage a live system?

Yes, when a tool has direct access to production data. Keep AI agents in a separate development environment, with no route to live customer records, and make sure a tested backup exists. Instructions alone are not a safety control, because an agent can act against them. Permissions are what stop it.

In July 2025, eWeek reported that an AI assistant erased a production database during a Replit trial run by SaaStr founder Jason Lemkin, despite an instruction not to make changes without approval. The database held records on 1,206 executives and over 1,196 companies. Replit's chief executive called it unacceptable, and the company added automatic separation of development and production databases.

Apply the same rule to your own project. Give each tool the least access it needs, require a person to approve changes to the live system, and restore a backup at least once before you need to.

Can an AI-built app scale?

It can, but only after someone reviews how it stores data and handles load. AI-generated apps often work well with a few test accounts and then slow down or behave wrongly with real volume and simultaneous users. Load testing with realistic data shows the actual limit before your customers find it.

Look at three areas first. Database queries need suitable indexes and should avoid loading whole tables. Slow work, such as sending emails or generating reports, should run in the background. Actions like payments should be safe to retry, so a double tap cannot charge twice.

Ask what grows with success: customers, orders, staff, photos or messages. Test that number at several times today's level. If the app depends on a builder platform, also check its plan limits, what you can export, and whether the code can run elsewhere.

Should you fix an AI-built app or rebuild it?

Fix it if the data model is sound, the code is exportable and the problems are limited to specific areas. Rebuild if a review shows that permissions, data or payments are wrong at the foundation. Either way, the prototype keeps its value, because it proves the workflow and shows what users actually need.

Start by exporting everything you can: code, database schema, data and environment settings. Then ask an engineer to run the app from that export. If it cannot be run outside the builder, your options narrow, and that finding should come before any quotation for new features.

If real customer data is already in the app and you find an exposure, treat it as an incident. Close the gap, keep records and take advice on your PDPA duties. Malaysia's breach notification rules have strict deadlines.

What does commercial-grade mean for an app?

A commercial-grade app is one a business can depend on daily: it has per-user access rules, separate test and live environments, tested backups, error monitoring, a repeatable release process and documentation another team could follow. Those qualities are checkable. Ask for evidence of each one, whoever built the app or whatever tool they used.

Use this as a checklist with any vendor, including us. A good answer shows you the thing itself, such as the staging site, a restore log or the deployment notes.

Macoin is a software company in Bandar Puteri, Puchong. We can review an AI-built app on the same basis as any other codebase, then say whether to fix or rebuild, and whether we can support it. WhatsApp what you built it with and what is going wrong. Leave out passwords and customer data.