CodeReviewDrift: PR Quality Regression Tracker
Automatically detects when your team's code review standards are slipping by analyzing PR approval velocity, comment depth, and test coverage trends across GitHub/GitLab.
The Problem
Engineering teams lose code quality incrementally as teams grow, deadlines tighten, or senior reviewers get overloaded. There's no system that alerts you when review rigor drops—you only notice when bugs ship. Teams manually audit PRs months later, too late to fix the pattern.
Target Audience
Engineering managers and tech leads at companies with 15-150 engineers who use GitHub/GitLab and care about maintainability (SaaS, fintech, healthtech).
Why Now?
AI makes it feasible to analyze comment quality and intent at scale without manual labeling. GitHub's API is mature. Engineering teams are scrambling to maintain velocity while preventing quality collapse post-Series A.
What's Missing
Existing tools measure code quality (CodeClimate, SonarQube) but not the *process* quality. No one tracks whether reviewers are actually engaging deeply or rubber-stamping PRs. This gap exists because it requires both API integration and AI-powered behavioral analysis.
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