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Built with AI.
Ready for production?

Your application works. Answer 13 questions about security, data, tests and operations to see how ready it is for real users, and what to fix first.

Answer the questions

01 / Secrets & securityAre API keys and passwords kept out of the code repository and out of the browser?
02 / Authentication & accessIs access checked on the server for every protected page and API action, not only hidden in the interface?
03 / BackupsAre backups automatic, stored separately, and has a restore actually been tested?
04 / Automated testsDo automated tests cover critical flows such as sign-up, payment and core actions, and run before each release?
05 / DatabaseDo database changes run through versioned migrations, with queries protected against injection?
06 / DependenciesAre dependencies pinned to versions and checked for known vulnerabilities?
07 / DeploymentCan you deploy a fix and roll it back in minutes through a repeatable process?
08 / ArchitectureCould a new developer understand how the main parts fit together from the code and a short README?
09 / External APIs & AI usageAre third-party and AI model APIs called from the server with timeouts, retries and spending limits?
10 / Logging & monitoringWould you be alerted to errors, and could you trace what happened from the logs?
11 / PerformanceDo key pages and API calls stay fast with realistic data volumes?
12 / ScalabilityDo you know what would break first if usage grew ten times?
13 / Public pages & SEOAre public pages rendered on the server with unique titles, descriptions and a sitemap?

The score is calculated in your browser from your answers; nothing is sent. “Not sure” counts as a partial risk, because unknowns are risks in production.

Why it matters

Working is not the same
as production ready

AI tools make it possible to build a working application quickly. The gaps usually sit where a demo never fails: access control, secrets, data changes, backups and monitoring. Our production readiness checklist for AI-generated code explains each area in more detail.

Self-check

Your own answers, scored in your browser. A quick way to find blind spots.

Code audit

HUB engineers review the repository, infrastructure and data flows, and verify each area.

Repair

Prioritized fixes, tests for critical flows and a safer release process.

Handover

Documentation and monitoring, so your team can keep shipping safely.

Questions

About the
self-check

Want the answers verified in the actual code? That is what an AI code audit does.

AI Code Rescue
Does the score look at my code?

No. It is calculated only from your answers, in your browser. A code audit checks the same areas in the repository itself.

Which tools does it apply to?

Any application generated or heavily assisted by AI tools, such as Lovable, Bolt, Replit, v0, Cursor, Claude Code, Codex or GitHub Copilot, and conventional code as well.

Why does “Not sure” lower the score?

In production, an unknown is a risk. Finding out whether backups restore or access is enforced is often the most valuable first step.

Is anything sent to HUB?

No. Only if you choose to request an audit is a summary copied into an email draft that you review and send yourself.

What does your software
need to do next?

A new build, a difficult codebase or a system that needs support. Let’s talk.

Discuss a project