AI-Generated Code Quality Auditor

Developers using AI coding assistants worry about code quality and maintainability at scale (60k+ lines). A tool that analyzes AI-generated code for architectural issues, technical debt, and reproducibility problems could help teams validate 'vibe coded' applications before production.

The explosion of "vibe coding" via Cursor, Copilot, and Claude has created a genuine new problem: codebases that work but are structurally unsound, and engineering teams that can't easily tell the difference until something breaks in production. No clear incumbent owns this specific niche — SonarQube covers general static analysis but wasn't built around the patterns AI models tend to produce (repetitive abstractions, shallow error handling, inconsistent naming conventions). The $2k–$15k/mo revenue band is realistic for a dev-tools PLG motion where a single engineering team at a mid-size company represents a viable account, though it implies staying small unless there's a clear path into the enterprise security/compliance buyer. The biggest risk is that the major AI coding assistants — Cursor, GitHub Copilot — ship native quality guardrails themselves, collapsing the market before it fully forms.