Claude AI reads every merged PR, understands the changes, classifies severity and component, and calculates an objective score—no manual review required.
Engineering teams struggle with consistent, objective evaluation of developer contributions.
Manual PR reviews take 20+ hours per week for engineering managers.
Different reviewers apply different criteria, leading to unfair evaluations.
Developers lack clear metrics on how their contributions are valued.
Severity and business impact are difficult to measure consistently.
GitRank automates the entire evaluation pipeline so you never have to manually score a PR again.
When a pull request is merged to your repository, GitHub sends a webhook to GitRank with the PR details.
Every aspect of AI evaluation is designed to be accurate, transparent, and fair.
AI automatically classifies which area of your codebase the PR affects—Auth, Payments, API, UI, and more.
AI evaluation powers multiple workflows across your engineering organization.
Problem
Manual evaluation of bug fixes takes hours and creates disputes over payout amounts.
Solution
AI scores every fix objectively. Payouts are calculated automatically based on severity and component.
Start evaluating PRs with AI in under 5 minutes. Free for open source projects.
GitRank fetches the full PR diff via GitHub API—including changed files, additions, deletions, and linked issues.
The diff is sent to Claude, which reads the code changes, understands the context, and determines the component and severity.
The AI verifies eligibility criteria: Is it fixing an issue? Are tests included? Does the implementation match the claim?
Final Score = Base Points (from severity) × Multiplier (from component). The score is posted as a comment on the PR.
GitRank Bot
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Each PR is classified into severity levels (P0-P3) based on the nature and impact of the changes.
Claude reads the full context—PR title, description, linked issues, and commit messages—not just the diff.
AI checks if the PR meets your criteria: issue linked, tests included, implementation matches claims.
Every evaluation includes a full breakdown of how the score was calculated and why.
Disagree with the AI? Review and adjust any evaluation with full audit trail support.
Problem
Quantifying developer contributions for reviews relies on subjective impressions.
Solution
Pull objective metrics showing total impact, severity breakdown, and component expertise.
Problem
No visibility into code quality trends or which areas need attention.
Solution
Track quality metrics over time. Identify bug hotspots and areas with declining scores.
