See which AI tools are actually earning their license.
GitClear attributes every line of code to the model that wrote it via Claude, Cursor, Copilot, Codex, Augment or Gemini. Then durable output is scored against rework, defects, review time, and more.
One comprehensive scorecard. Ten minutes to get started. No sales call required.
| prompt category |
Apex-R 2.4
premium reasoning
|
Apex-F 2.4
premium fast
|
Core 3.1
balanced workhorse
|
Lite 1.9
cheap, verbose
|
Local-32B
self-hosted
|
|---|---|---|---|---|---|
|
implement_feature
outcome: durable Diff Delta @ 90d
|
1.85×
$79.2 / success
80% @30d · n=262
|
1.70×
$79.8 / success
78% @30d · n=87
|
0.97×
$98.9 / success
74% @30d · n=612
|
0.37×
$212 / success
60% @30d · n=349
|
0.11×
$436 / success
49% @30d · n=146
|
|
root_cause_bug
outcome: verified diagnosis, no recurrence
|
2.34×
$81.9 / success
82% @30d · n=131
|
1.62×
$109 / success
71% @30d · n=44
|
0.81×
$154 / success
64% @30d · n=306
|
0.19×
$558 / success
44% @30d · n=175
|
0.04×
$1381 / success
34% @30d · n=73
|
|
dry_cleanup
outcome: duplication removed, retained @ 90d
|
1.63×
$52.1 / success
90% @30d · n=66
|
n=22 insufficient
|
1.00×
$55.5 / success
90% @30d · n=153
|
0.60×
$75.8 / success
86% @30d · n=87
|
0.17×
$156 / success
70% @30d · n=36
|
|
explain_code
outcome: explanation accepted, next task lands
|
1.60×
$21.0 / success
97% @30d · n=112
|
1.74×
$18.0 / success
97% @30d · n=37
|
1.00×
$21.5 / success
94% @30d · n=262
|
0.51×
$34.8 / success
83% @30d · n=150
|
0.15×
$70.0 / success
69% @30d · n=62
|
|
address_pr_feedback
outcome: thread resolved without reopen
|
1.71×
$50.3 / success
89% @30d · n=103
|
1.78×
$44.7 / success
92% @30d · n=34
|
0.98×
$57.1 / success
87% @30d · n=240
|
0.42×
$110 / success
73% @30d · n=137
|
0.11×
$238 / success
59% @30d · n=57
|
|
build_failing_test
outcome: fails before fix, passes after
|
1.66×
$32.1 / success
94% @30d · n=47
|
n=16 insufficient
|
1.04×
$33.6 / success
91% @30d · n=109
|
0.48×
$59.9 / success
78% @30d · n=62
|
0.14×
$117 / success
65% @30d · n=26
|
◆ AI Quality Research Cited By ◆
Your bronze tables already hold the answer. We build the tables that relate it.
GitClear specializes in enriching Databricks bronze tables — raw AI assistant telemetry, git history, pull request events, issue trackers — into the silver and gold tables that relate facts nobody could join before: which model earned its inference spend on which kind of task, and how much of its output was still in production 90 days later. The two panels at the top of this page are those tables.
We build these pipelines on Apache Airflow with Astronomer, the same orchestration we run for billion-dollar enterprises. Bronze ingest → silver work episodes → gold marts, rebuilt nightly in Unity Catalog, against your own warehouse — your data never leaves your Databricks account.
- Medallion modeling (bronze → silver → gold) in Unity Catalog, with lineage that survives an audit
- Containerized, idempotent Airflow DAGs on Astronomer — backfill a year of history without babysitting it
- Diff Delta as the unit of output, so moves, renames and reformatting never inflate a model's numbers
- Episode-grain facts, so cost, durability and revision risk can all be asked about the same row
Four surfaces. One defensible ROI score.
Every AI stat in GitClear originates from deep analysis of code changes — so when a number doesn't look right, you can always drill into the code that produced it.
Every line tagged with the model that wrote it.
GitClear cross-references your Git history with vendor AI usage APIs and agent telemetry hooks to produce commit-grade provenance — no guessing, no aggregate estimates.
- Claude, Copilot, Cursor, Codex, Augment and Gemini APIs supported out of the box
- Attribution precision maximized via telemetry hooks
- Access via a robust API, for your own analysis or internal reporting
Find the folders where AI is creating more work than it saves.
Not every directory responds to AI the same way. GitClear surfaces the folders where AI-assisted code has elevated defect and duplication rates — so you can coach, gate, or restrict tool access before it compounds.
- Per-directory AI %, defect Δ, duplication Δ
- Risk score normalized against your own baseline
- Exportable as quarterly engineering review artifact
See human vs. LLM code, measured by the same yardstick.
GitClear's Diff Delta metric works the same way whether a line came from Claude or a senior staff engineer. Compare durable change velocity, rework rate, and review time across cohorts — without apples-to-oranges caveats.
- Cohort views by team, repo, or AI tool usage level
- Side-by-side weekly trends — AI power users vs. non-adopters
- Statistical significance flags on every delta
Inspired by Google DORA. Built for the AI era.
Three inputs, one defensible number — so finance, your board, and your own engineers can all read the same scorecard without arguing about what it means.
Attribution
AI usage APIs plus commit heuristics plus agent telemetry hooks — not survey estimates. Every line traceable to the model that wrote it.
Output quality
Diff Delta quantifies durable change vs. churn. Human and LLM code measured with the same metric, across the same time window.
Developer experience
Self-reported hours saved and satisfaction scores. Productivity gains don't count if your best engineers are walking.
Industry Leading
AI Code Quality Research
Works with the tools your team already pays for.
GitClear plugs into your Git host and your AI vendor APIs directly — no proxies, no middleware, no code changes. First scorecard renders in under ten minutes.
See what your AI spend is actually returning.
Connect your repos. Get your scorecard in under ten minutes. No credit card, no sales call — unless you want one.