From “it works on my AI” to “it works in production”.

01

What happened?

AI tools compress implementation time, but they do not remove operational work. Domains, secrets, data, observability, access, capacity, ownership and incident response still need explicit decisions.

02

Why this happens

• The repository does not represent the exact preview that stakeholders approved.
• Production configuration and data changes are mixed with application code.
• There is no release gate, owner, monitoring signal or rollback rehearsal.
• The hosting model does not match background jobs, state or expected traffic.

03

What you can check yourself

1. Commit and label the candidate version; document runtime and external dependencies.
2. Create staging with production-like configuration and safe representative data.
3. Run unit, integration, build, security and critical journey checks.
4. Review, approve, deploy, observe and record the result as one release.

04

How to test the fix safely

Use immutable or versioned artifacts where possible. Back up data, prefer backward-compatible migrations, deploy the approved version and verify both technical health and real user journeys before closing the release.

05

When you need a developer

Shipvise is designed to provide the control layer: staging, automated checks, AI or senior review, managed approval, deployment, hosting and rollback while you keep using your preferred AI coding tool.