How to Use This Checklist
Launching an AI MVP requires more checks than traditional software. This checklist covers technical, product, security, and business items you need to verify before going live.
There are 47 checks in six areas. Work through them in order, mark each as pass, fail or not applicable, and don't launch with a fail in the security or AI model sections. Items that don't apply to your product still deserve a line explaining why.
Step 1: Technical Infrastructure
- Verify deployment pipeline works end-to-end.
- Confirm database backups are running.
- Test auto-scaling under load.
- Verify SSL certificates.
- Confirm monitoring and alerting is active.
- Test rollback procedures.
- Confirm environment variables and secrets are set separately for each environment and are not stored in the code.
- Test that the app recovers cleanly after a restart or a failed deploy.
Step 2: AI Model Readiness
- Test model outputs against edge cases.
- Verify prompt engineering handles unexpected inputs gracefully.
- Confirm fallback behavior when the AI model fails or times out.
- Test rate limiting and cost controls.
- Verify model output quality meets minimum standards.
- Run the evaluation set of real inputs and record the results before launch.
- Confirm the model and prompt versions are recorded, so a drop in quality can be traced to a change.
- Set a per-user and per-day spending limit on model usage.
- Check how the app behaves with very long, empty and non-English inputs.
Step 3: Security and Privacy
- Verify data encryption at rest and in transit.
- Confirm access controls and authentication.
- Test for prompt injection vulnerabilities.
- Verify PII handling complies with regulations.
- Confirm audit logging is active.
- Confirm API keys and secrets are not exposed in the browser or in logs.
- Verify users can only see their own data.
- Check what user data is sent to the model provider and that it matches your privacy policy.
Step 4: User Experience
- Test all user flows end-to-end.
- Verify error messages are helpful and non-technical.
- Test on mobile devices and different browsers.
- Confirm loading states during AI processing.
- Verify response times are acceptable.
- Confirm users are told when they are dealing with AI, and what it can and can't do.
- Check that the core flow works with a keyboard and a screen reader.
Step 5: Business Readiness
- Set up analytics tracking for key metrics.
- Prepare support documentation and FAQ.
- Test payment processing if applicable.
- Prepare communication templates for user feedback.
- Set up user feedback collection mechanism.
- Confirm your privacy policy and terms match what the product does.
- Name who answers support requests, and how quickly.
- Check that you can see what each user costs you in model usage.
Step 6: Post-Launch Plan
- Define success metrics and targets for week 1.
- Schedule daily monitoring check-ins for the first week.
- Plan iteration cycle based on user feedback.
- Prepare scaling plan if demand exceeds expectations.
- Decide in advance what result in the first month means continue, change direction or stop.
- Set up a weekly review of failed or low-confidence AI outputs.
- Keep a list of the questions users ask that the product can't answer.
Where This Checklist Comes From
This checklist is based on launching 18+ AI MVPs at SpeedMVPs. The full 47-item checklist is on this page, so there is nothing to download. Use it alongside the AI MVP to production timeline, and see how to validate your AI MVP idea for the checks that come before the build. To have the team run these checks with you, book a strategy call.
