Pattern Audit
A lightweight codebase audit service that scans your code for pattern consistency and tells you, in one page, how much 'AI leverage' you are leaving on the table.
Engineering managers and staff engineers preparing a modernization pitch to leadership
- Static scan that scores pattern consistency, framework spread, and naming drift across the repo
- One-page executive summary sized for the CTO to forward to the board
- Comparison of your scores to anonymized benchmarks from similar-sized engineering orgs
- Suggested 'pattern-cleanup' refactor list ordered by AI-leverage gained per hour invested
The trending essay argues AI output quality is determined by codebase consistency — so engineering leaders now have a quantifiable argument for cleanup work they previously couldn't justify; the audit makes that argument board-ready.
Real pain documented in HN threads like 'Ask HN: Maintaining code quality with widespread AI coding tools' (item 43952366) and the Codex protocol discussion (47075162), but the use case narrows to engineering leaders making modernization pitches — a slice of the broader market.Ask HN: Maintaining code quality with widespread AI coding tools ↗Open source protocol that improves AI code quality in any IDE | Hacker News ↗
Crowded adjacent space: The Code Registry, a001.ai AI codebase audits, codebase-digest, Panto, and Greptile all already serve codebase analysis/audit; the 'board-ready one-pager' framing is a thin differentiator that incumbents can replicate quickly.How to Audit a Codebase Using AI - C# Corner ↗AI Codebase Audit: What to Expect and How It Works | A001.AI ↗
Engineering leaders have modernization budgets (Gartner and NEA confirm growing spend), but code-audit services are already commoditizing from free OSS to enterprise consulting — pricing power is moderate, not strong.Innovation Insight: AI-Augmented Code Modernization Tools ↗Beyond Beating the SWE-Bench: How AI Will Modernize Enterprise Code ↗
The 'AI leverage from consistency' narrative is real and durable in the near term, but as base models improve, code-consistency penalties shrink and audit categories will commoditize fast — not a 10-year moat.
Lightweight service is buildable with existing LLMs plus static analysis (tree-sitter/AST parsing) — producing a one-page executive report is straightforward; no hard infra or data moat required.