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Find out what will break in your AI-built app before your users do

Sixteen questions, no code access needed. You get a scored readiness report naming exactly which of the failure modes we find in Lovable, Bolt, Replit, Cursor, v0 and Base44 projects are probably in yours, ranked by what breaks first, with what each one takes to fix.

16 checks·About 4 minutes·Report instantly·No code access

Step 1 of 5

What built it, and is it live?

Different tools fail in different ways. This decides which write-up the report points you at.

Built with

Where is it now

Step 2 of 5 · Data and access

Who can reach your data?

These four are where the expensive failures live. "Not sure" is a real answer here and it is scored as one, because an unverified assumption about database rules is the same risk as a known problem.

Has anyone changed the database access rules from the development default?

Are any API keys or service keys used directly from front-end code?

If a logged-out person called your API directly, would the server refuse?

Could you restore yesterday's data if it were deleted today?

Step 3 of 5 · Money and running it

What happens when it goes wrong?

Pick "not applicable" where a question genuinely does not apply. Those drop out of the score rather than counting against you.

Is the amount charged calculated on the server, and do you handle the payment webhook?

Does the app run anywhere other than the tool's own preview?

When a request fails, does the user see a real message and do you get told?

Would you know within an hour if the app went down right now?

Step 4 of 5 · The codebase

What did the prompt loop leave behind?

Repeated rounds of "fix this" tend to generate new code beside the old code rather than replacing it. How much of that happened is the single biggest driver of what a repair costs.

Is there anything stopping one person hitting your API thousands of times?

Are file uploads size-limited, type-checked and access-controlled?

Does the same piece of logic exist in more than one place?

Is there any automated test, or a way to roll back a bad change quickly?

Step 5 of 5 · Cost, data and stores

Four last ones

These rarely take an app down, but each is cheaper to handle now than when a customer, an auditor or a store reviewer asks.

Do you know what one active user costs you in AI calls per month?

Could you export all your data today without the tool that built the app?

Has it been through App Store or Play Store review?

Do you know which country your users' data is stored in?

Last step

Who is this report for?

Your name goes on the report. It is built here in your browser and appears immediately, so there is nothing to wait for in your inbox.

First name

Last name

Email So Haidar can tell you whether it is worth repairing. One email, no sequence, reply "no thanks" and it stops.

🔒 Straight to my inbox and nowhere else. No list, no resale, no drip sequence. Relax.

What is an AI-app production readiness audit?

It is a check of whether an app generated by a tool like Lovable, Bolt, Replit, Cursor, v0 or Base44 is safe to run on real users. This one asks sixteen questions, no code access needed, and returns a scored report naming which known failure modes are probably present, ranked by what breaks first and what each takes to fix.

It exists because Foundreco repairs these for a living, and the same handful of problems turn up nearly every time. None of the sixteen checks is invented for the sake of a scary report. Each one is a failure Foundreco has been paid to fix, and they are written up in full on the rescue page and in why AI-built apps break the moment you deploy them.

What does it actually check?

Sixteen checks in four groups: data and access, money and running it, the state of the codebase, and cost, data ownership and store readiness. Five of them are scored critical because they lose data, leak data or lose money.

Group	Checks	Why it matters
Data and access	Database rules, keys in the browser, real authentication, restorable backups	Where the expensive failures live. A database still set to "allow all" from development is the most common single finding.
Money and running it	Server-side pricing and webhooks, deployment beyond the preview, error handling, monitoring	These do not take the app down on day one. They take it down on the first busy day.
The codebase	Rate limits, file uploads, duplicated logic, tests and rollback	How much the prompt loop duplicated is the single biggest driver of what a repair costs.
Cost, data, stores	AI cost per user, data export, store review, data location	Rarely urgent, always cheaper before someone asks.

Why "not sure" counts against the score

Because it should. Not knowing whether row-level security is switched on carries the same risk as knowing it is not, and it takes minutes to check. The report marks those findings separately so you can tell the difference between a confirmed problem and an unverified assumption, but it does not pretend the second one is free.

What it is not

It is not a code review, and it cannot find what you did not know to report. A questionnaire has no way to see a bug you have never noticed. What it can do is tell you which known failure modes are probably in your app and in what order they matter.

The version that looks at the actual code is the 72-hour audit at the start of a Vibe-Code Rescue, which then becomes one fixed price from $1,500.

Questions about this audit

Do I need to give you access to my code?

No. The audit is sixteen questions answered by you, and nothing connects to your project. That is also its limit: it works from what you report, so it cannot find a problem you have never noticed. The version that reads the code is the 72-hour audit that starts a rescue.

What does the readiness score mean?

It is the share of applicable checks you pass, weighted by severity. Critical checks count for more than the ones worth watching, and "not sure" costs 70% of a "no". Checks you mark not applicable drop out of both sides, so a web-only app is not penalised for having no App Store review.

My score was low. Does that mean I have to rebuild?

Usually not. If the data model is sound and the product does roughly the right thing, repair beats rebuild almost every time, even with a long finding list. Rebuild is the right answer when the data model itself is wrong. Nothing decides that from a questionnaire; it is decided by reading the code.

How much does fixing these usually cost?

A Vibe-Code Rescue starts with a 72-hour audit and then one fixed price from $1,500. Most land between $1,500 and $5,000, and the biggest driver is how much of the codebase was duplicated by repeated prompting. It is fixed before work starts, never hourly.

Is this just a sales pitch with a score on it?

Every finding names what breaks, what fixing it involves and roughly how long it takes, so you can hand the report to any developer and act on it without Foundreco. Plenty of people will do exactly that, which is fine. The report also says plainly when rebuilding is cheaper than repairing.

What happens to my email?

Haidar Mustafa may write once about your report. No sequence, no newsletter, no list sale. Reply "no thanks" and it stops. Foundreco has no sales team, so the reply comes from the person who would do the repair.

Your Turn

Send the project link and get a straight answer in 24 hours

Repair, rebuild, or leave it alone. Haidar Mustafa reads it himself and tells you which, including the answer that loses him the work.

Send the project link How a rescue works