Know what your AI coding spend is actually returning.
GitClear pinpoints which lines of code came from Copilot, Cursor, Claude Code, Augment, Codex, or Gemini — then scores durable output against rework, duplication, and defects. One ROI number your board can read.
◆ AI Quality Research Cited By ◆
One defensible number. Three inputs that can survive a board meeting.
Inspired by Google DORA. GitClear's AI ROI score combines attribution, output quality, and developer experience — so a single number holds up to scrutiny from finance, the board, and your own engineers.
Which model wrote which line?
No more "we think" or "estimated." GitClear combines three signals to produce line-level attribution with commit-grade confidence.
Did the code actually survive?
Diff Delta quantifies durable change vs. churn, per author. Human-authored and LLM-authored lines are measured side by side using the same yardstick.
Is the team better for it?
Productivity gains are hollow if developers are drowning. We fold in self-reported time savings and satisfaction scores — the SPACE framework, made actionable.
From three data sources to one number, in about ten minutes.
Connect your Git host, point GitClear at your AI vendor APIs. Your first scorecard renders before the kickoff call ends.
Three sources, unified
GitClear pulls from your Git provider, your AI vendor usage APIs, and a lightweight agent telemetry hook — no proxy, no man-in-the-middle.
Line-level authorship
Every line is tagged
authored_by_llm
with provenance: which model, which session, which developer accepted it. Ambiguous lines get flagged, not guessed.
Scorecard + dashboards
A boardroom-ready ROI scorecard plus drill-downs into durable change velocity, AI hotspot directories, and rollout readiness signals.
Most tools measure adoption. GitClear measures whether the output is worth it.
LinearB, DX, and Jellyfish can tell you how many developers use AI. None of them can tell you whether those developers are shipping durable code — because none of them do line-level attribution.
| Capability | GitClear | LinearB | DX | Jellyfish |
|---|---|---|---|---|
| Line-level AI attribution | ● Yes | ◐ PR-level | — No | — No |
| Durable vs. churned code scoring | ● Yes (Diff Delta) | ◐ Rework rate only | — No | — No |
| Human vs. LLM comparative outcomes | ● Yes | — No | — No | ◐ Aggregate only |
| Multi-vendor AI usage API support | ● Claude Code, Copilot, Cursor, Codex, Augment, Gemini | ● Yes | ◐ Copilot only | ● Yes |
| Free self-serve tier | ● Yes | ◐ DORA only | — Demo required | — Demo required |
| Published longitudinal research | ● 211M lines, 3 yrs | — Vendor claims | ◐ Survey-based | ◐ Partner research |
The largest public study of AI's effect on code quality — by a wide margin.
GitClear's AI Copilot Code Quality Report has been cited by MIT Technology Review, TechCrunch, and The New Stack. Our competitors quote our findings in their own marketing. We think you should get them from the source.
The numbers above are why this homepage exists. AI has not made developers 50% more productive — not in any codebase we've measured, and we've measured more of them than anyone.
It has created a new class of risk: code that ships faster than the team can reason about it. GitClear's ROI score is the one number that reflects both sides.
Read 2026 AI reportsFree until you want the scorecard.
Connect your repos and get the full dashboard for free, forever. Upgrade when you need the scorecard, AI attribution APIs, or unlimited contributors.
Free
- Diff Delta velocity
- PR review insights
- Basic DORA metrics
- No support
Pro
- Everything in Free
- AI ROI scorecard
- Line-level AI attribution
- Claude, Copilot, Cursor, Codex, Augment, Gemini
- Developer experience surveys
- Priority support
Enterprise
- Everything in Pro
- Self-hosted deployment
- Custom SLA
- Dedicated CSM
- DORA-compliant reports
See what your AI spend is actually returning.
Connect your repos. Get your scorecard in under ten minutes. No credit card, no sales call required — unless you want one.
