What this probe helps you decide
Whether a bounded AI-generated code slice should be promoted, held, sandboxed, repaired, or scoped for custom work before anyone builds on it.
Best lifecycle moment
After AI-generated code looks complete but before demo, handoff, implementation spending, or production claims.
What you submit
A bounded AI-generated code slice, public repo, or safe project surface plus the decision the report should support.
What product or surface this targets
Tell us the product, service, feature, SDK, app, workflow, dependency choice, AI-generated slice, or legacy module inside the repo/project that the probe should focus on. The repo is evidence; it is not automatically the whole target.
What we inspect
Visible tests, wiring, config, dependencies, hallucinated assumptions, unsupported packages, deployment gaps, and false-closure seams.
What the report returns
CA$500 buys one false-closure decision report for a bounded AI-generated code slice: verdict, findings, evidence, highest-risk seams, and next stabilizing move.
Sample report / example
See the RuView AI Code Stability sample: a false-closure report for a codebase whose docs and structure imply more runtime confidence than the public evidence supports.
After delivery
If you repair a named false-closure seam, a small Closure Verification follow-up can check whether that specific seam improved, closed, regressed, or still needs rerun. It reviews the accepted evidence surface only and does not certify production readiness or guarantee stability.
What this does not include
- Implementation
- Production certification
- Security audit
- Full code review
Boundary of claim
This probe checks visible support for AI-generated code. It does not make the code production-ready or certify that all behavior is correct.
Best-fit examples
- AI-built feature with shallow tests.
- Generated app that appears complete but has unwired paths.
- Prototype with invalid config, unsupported dependencies, or deployment gaps.