Production checklist for non-technical founders launching an AI-built app.

The direct answer: you do not need to understand every line of code to decide whether an AI-built app is ready to launch. You do need clear evidence that the live product handles people, data, money and failure safely—and a named way to respond if it does not.

Start with the outcome, not the code

A production checklist is not a coding exam. It is a way to make sure the app you are about to put in front of people is the app you have actually checked. AI can create a convincing demo quickly, but a demo proves only that one path worked once. A launch introduces real devices, real data, people who click twice, services that fail and customers who expect help.

Make one simple document with four columns: the thing that could go wrong, the evidence that it is covered, the person responsible, and the next action. “The developer said it is fine” is not evidence. A test, a screenshot of the live setting, a confirmed record or a written recovery step is.

1. Identify what is really live

Record the production URL, the hosting account, the database, the domain, the branch or repository that deploys, and the providers that handle payments, email and AI. This stops a common mistake: checking a local preview while customers are using a different version or database.

2. Check who can see and change what

Test as a visitor, a normal user and an administrator where those roles exist. Signing in tells the app who someone is; permission checks determine what they may do. A normal user should not be able to change a URL to see another customer’s record or reach an admin action. Ask for evidence from the live app, not an assurance that this is handled somewhere.

3. Protect secrets and customer data

List the data you collect and where it is stored. API keys, database passwords and payment secrets should be in protected server-side settings, never browser code, screenshots or a public repository. If a secret has ever been exposed, replace it. Deleting it from the visible file does not make the old value safe.

4. Follow the journeys that matter

Choose three to five journeys where failure would cause real harm: creating an account, signing in, saving important work, making a purchase, receiving a paid item, deleting an account, or contacting support. Run them in the production environment. Try a normal mistake too: an invalid email, a refresh during a payment, a double click, or a connection loss. Write down what happens.

5. Treat payments as their own system

If you charge customers, use a reputable payment provider to handle card data. Your server should decide the price, verify the provider’s payment confirmation and fulfil the order only once. A success page is not proof of payment. Read the detailed vibe-coded app payments guide before your first customer transaction.

6. Put boundaries around AI cost and behaviour

For every AI feature, decide who can call it, how often, how much input it may receive and what a request can cost. Keep provider secrets server-side. Add limits and logging so you notice a loop, abuse or a surprise bill early. Also check what the model is allowed to do with customer data and what the user sees when the model is unavailable or gives a poor answer.

7. Make failure visible

You need to know about important errors before a customer does. Capture server failures, failed payments, failed jobs and important delivery failures. Logs should be useful without containing passwords, access tokens or unnecessary personal information. Decide who receives the alert and what they do first.

8. Prove recovery, not just backup

For data that matters, confirm a backup exists and test restoring it in a safe place. Know how to return to the previous deployment and how to disable a risky feature. Keep account access for the domain, host, database and payments provider documented privately. A backup you have never restored is only a promise.

9. Launch in a controlled way

Use a small first group where possible. Watch the critical journeys, check payment and email records, and make it easy for early users to report a problem. This is not an excuse to launch a known dangerous feature; it is a way to learn from real use without pretending the first release is final.

What to ask a technical helper or AI assistant

Ask them to trace a journey from browser to server to database or provider, show the evidence, and state what could not be verified. Ask what happens if the same request arrives twice, if an external service is down, or if the wrong person tries to access data. Good answers distinguish proven behaviour from assumptions.

KHDS Ship Doctor is built for this kind of evidence-led review inside your coding workspace. The free builder creates a starter instruction; the £49 one-off toolkit adds reusable workflows, diagnostic areas, reporting and safe remediation guidance. See its public redacted sample report, then use the wider vibe coding security checklist for a second pass.

Frequently asked questions

What should a non-technical founder check before launch?

Check the live version, access, data, payments, AI costs, monitoring, critical journeys and recovery.

Does an AI-built app need staging?

It is useful when changes could affect customers or real data. At minimum, use a safe way to test critical changes before release.

Can I do this without coding?

Yes. Ask for evidence, follow the important journeys and make sure every important risk has an owner and a recovery plan.