AI Code Quality Guardian

A set of quality gates and rules that catch common AI-generated code failures before they reach production. Ensures AI-assisted code meets standards for testing, documentation, and reliability.

AI-generated code is flooding repositories faster than review processes can adapt, and teams using Copilot, Cursor, or Claude for coding are already reporting subtle bugs, missing edge cases, and hollow test coverage that slip past standard linters. The closest substitutes are general static analysis tools like SonarQube or DeepSource, but neither is tuned specifically for the failure patterns AI models produce — hallucinated dependencies, plausible-looking but incorrect logic, and documentation that describes intent rather than actual behavior. The $1k–5k/mo revenue band is realistic for a focused devtools product sold to small engineering teams on a per-seat or per-repo basis, though it implies staying small unless there's a clear expansion path to enterprise. The biggest risk is that the major AI coding tools — GitHub Copilot, Cursor — simply absorb this functionality natively, commoditizing the core value before any independent product builds enough switching cost to survive.