AI app pre-launch checklist: 10 checks before real users

What is an AI app pre-launch checklist?

An AI app pre-launch checklist is a final, evidence-based review of an application built or heavily changed with AI tools. It checks whether the app is safe enough, understandable enough, and operationally ready for the users and data it will handle.

The checklist is not a substitute for a professional security audit. Its job is to stop a convincing demo from being mistaken for a production-ready product.

Pre-launch checklist for an AI-built app

• Define the launch boundary: write down the first user, the core task, the data involved, and what is explicitly out of scope.
• Test the critical journey end to end: sign-up, the main action, the success state, the failure state, and a repeat attempt.
• Verify identity and permissions on the server for every read, write, export, delete, admin, and billing action.
• Search for secrets and personal data in frontend code, repositories, logs, URLs, error messages, and third-party tools.
• Test invalid input, empty states, timeouts, rate limits, expired sessions, duplicate clicks, and partial failures.
• Check database constraints, migrations, backups, retention, deletion behavior, and whether users can access another user’s records.
• Remove mock data, debug endpoints, test credentials, overly broad CORS, development flags, and unfinished feature branches.
• Confirm production environment variables, domains, email delivery, payment webhooks, storage permissions, and third-party limits.
• Set up monitoring for errors, failed critical actions, suspicious access, cost spikes, and service dependencies.
• Document how to roll back, restore a backup, revoke a key, disable a broken feature, and contact users after an incident.

When is an AI-built app ready to launch?

It is ready for a defined launch when the critical user journey works with realistic data, server-side access rules have been checked, known high-impact risks have an owner, and you can detect and recover from the failures that matter. Readiness is a decision about a specific scope and audience—not a permanent claim that the code is perfect.

Start with a small, observable release. Limit data and permissions, invite real users deliberately, watch the critical path, and keep a rollback plan. Expand only after the evidence supports expansion.