What does an AI production readiness audit check?
Deployment
Reproducible builds, environment configuration, and a deploy that can be repeated by someone else.
Rollback
A tested path back to the previous version, including database migrations that can be reversed.
Backups
What is backed up, how often, and whether a restore has ever actually been performed.
Monitoring
Uptime, latency, and error-rate signals that show a problem before customers do.
Alerting
Who is paged, for what, and whether the alerts are actionable or already ignored.
Error handling
User-facing failure states, retry behaviour, and errors currently visible only in a console.
Logging
Structured logs with enough context to debug an incident, and no secrets or PII inside them.
Load behaviour
Measured behaviour at expected peak concurrency, and the component that saturates first.
Security baseline
Auth, access control, secrets, headers, and dependency exposure at a minimum launch standard.
Data safety
Migrations, constraints, and the destructive operations that have no confirmation or audit trail.
Rate limiting and abuse
Protection on public endpoints, signup flows, and anything that costs money per call.
Third-party dependence
Provider outages, quota limits, and degraded-mode behaviour for each integration.
Compliance basics
Data residency, retention, consent, and deletion requests, where they apply to you.
Runbook
Documented steps for the failures most likely to happen in the first month.
Ownership
Who maintains this after launch, and what they need to know that only the AI tool currently knows.
Other audits
Each audit answers a different question. Most engagements combine two or three.
Build with
confidence.
AI builds the product. Vibecop makes sure it won’t break in production, fail under scale, or expose your users to risk. One audit. Fewer expensive surprises.