Do your AI-generated tests actually validate your product?
A CI tool that catches LLM-written tests that pass without proving anything — empty assertions, missing edge cases, coverage disconnected from your spec.
Join the waitlistCommercial model
Early access — join the waitlist. Planned pricing: $12/developer/month (5-seat minimum, $60/mo floor).
Per-seat SaaS: $12/developer/mo, minimum 5 seats ($60/mo floor). Enterprise self-hosted license available at $8K/yr.
No invented results or guaranteed outcomes. Scope is confirmed before any commitment.
What the pilot tests
If we provide a CI-integrated tool that statically analyses LLM-generated test files for assertion quality (detects trivially-passing tests, missing edge-case patterns, and coverage gaps against a spec), then teams will pay per-seat because the alternative is slow manual review or shipping bugs.
- Runs inside GitHub Actions / GitLab CI on every PR that contains AI-attributed code
- Flags 'vacuous' tests: ones that always pass regardless of the logic under test
- Posts a structured PR comment with an assertion-depth score and actionable findings
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
- Ask HN: How to Claude Like Anthropic
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.