AI tools made building your app fast. They didn't tell you whether the result is secure, tested, or ready for real users and real data.
Founders who built an app with AI coding tools and want an honest answer before putting it in front of real users.
AI-generated code usually looks right. That's exactly the problem: it can compile, run, and demo beautifully while hiding exposed API keys, missing authorization checks on the endpoints that guard other people's data, injection flaws, and untested edge cases. Audits of AI-built projects consistently find security vulnerabilities in the majority of them.
The standard advice is to review the code carefully before launch. Honest answer: if AI wrote most of your app, that advice isn't for you, and pretending to follow it helps nobody. What you can use is the evidence around the code — security findings, dependency alerts, test results, untested critical flows — translated into decisions you can act on.
Qualyn connects to your GitHub repo, reads the evidence around your code, and returns an answer you don't need an engineering background to use: what looks ready, which risks matter now, and what would change the answer before you launch.
Qualyn reads the evidence teams already discuss and turns it into a release call that can be inspected before production.
You cannot know from the app working in a demo. Safety depends on evidence: security findings, dependency vulnerabilities, test results around critical flows, and how the app handles failure. If none of that evidence exists, that absence is itself the answer.
Exposed API keys and secrets, missing authorization checks on server endpoints, injection vulnerabilities, permissive database rules, and vulnerable dependencies. Most of these are invisible in the running app and only show up in the code and its findings.
No. You need the evidence about the code explained in plain English. Qualyn reads the repo and reports what is risky and what to fix first, without requiring you to read the code yourself.
Check for exposed secrets, security and dependency findings, whether authentication and payment flows have real tests, what happens when things fail, and whether you would even know if the app was breached or down.
For apps handling payments, health data, or sensitive personal data, a manual audit is worth it. Automated evidence first is the right order: it is faster, cheaper, and tells the auditor where to look.
Yes. If your app's code lives in a GitHub repository, Qualyn can analyse it. It does not matter which tool generated the code — the evidence model is the same.
Connect GitHub and turn release evidence into a clear answer: safe to ship, why, and what would change the answer.