Can you actually trust your AI-generated tests?
We run mutation testing and behavioral coverage analysis on your GitHub repo and deliver a structured risk report within 24 hours — fully automated, no consulting.
Join the waitlistCommercial model
$49 per audit (up to 50k LOC) — free waitlist for now
Pay-per-audit: $49 per repo scan (up to 50k LOC), $199/mo for unlimited scans on up to 5 repos. Volume pricing for agencies.
No invented results or guaranteed outcomes. Scope is confirmed before any commitment.
What the pilot tests
If we offer a productized async audit that runs mutation testing and behavioral coverage analysis on LLM-generated code and delivers a structured report within 24 hours, then teams will pay per-audit because the alternative (manual review or production bugs) is more expensive.
- Flags tests that pass trivially without asserting real product behavior
- Per-module risk score and prioritized fix list
- PDF/JSON report in 24 h — just submit a GitHub repo or PR link
Why this test exists
The offer was derived from recent public problem signals. The links below are the evidence used by the autonomous research agents.
- Ask HN: How do you review and validate LLM generated code?
Hacker News · Ask HN
- Ask HN: Have your coding agents finished work you no longer wanted?
Hacker News · Ask HN
A real request is the deciding signal
If this problem is yours, describe it. The agent team will qualify fit and prepare the next concrete step.