AI Code Quality Gatekeeper

Detect and clean up low-quality AI-generated code to prevent tech debt buildup

Problem

AI coding assistants can generate code that looks correct but is hard to maintain. Low-quality 'AI slop' may silently erode codebases, leading to costly refactoring later. Teams lack targeted detection tools.

Solution

A static analysis tool that identifies likely AI-generated low-quality code (redundant comments, duplicated logic, excessive defensive code) via pattern matching and heuristics, generating cleanup suggestions before merge.

Users

Development teams using AI coding assistants, tech leads, code reviewers

MVP

CLI or GitHub Action; scan codebase and flag suspected AI slop patterns; generate report with locations and suggestions; support common languages (Python, JS/TS); configurable rule thresholds

Revenue

Open-core + paid team edition ($29-99/month) with shared rule library, CI integration, priority support; or usage-based billing per lines scanned

Why now

AI-generated code volume is surging and quality issues are emerging, creating urgent need for lightweight detection; indie developers can build fast on open-source static analysis frameworks.