Get the AI ROI answer
Jellyfish's AI Impact module
can't give you.
Jellyfish counts Cursor licenses and Copilot seats. That's adoption, not impact. GitClear performs commit-level AI authorship attribution — line by line — so when finance asks "what is AI actually shipping?" you have an answer with receipts.
What Jellyfish customers say — in their own words, counted.
"The complexity of the product can be overwhelming."
"Interpreting some of the more advanced metrics still requires manual context-setting."
Adoption metrics don't survive a CFO meeting. Authorship metrics do.
Every AI Impact dashboard on the market starts by asking the wrong question — "who has a Copilot seat?" — and ends by reporting suggestion-acceptance rates that don't track to anything finance recognizes as output.
The DX team — Jellyfish's most thoughtful competitor in this category — admitted this directly in their Q4 2025 report:
"The percentage of AI-authored code is one of the most talked-about metrics in the industry right now — and also one of the hardest to measure accurately… we've also introduced a self-reported measure to establish a consistent baseline."
"Self-reported" is the tell. When the leading vendor concedes the metric is too hard to measure and falls back to surveys, the question for buyers becomes: who actually measured it?
License & seat telemetry. Suggestion-acceptance rates.
Counts how many engineers have Copilot or Cursor seats. Pulls suggestion-acceptance percentages from IDE plugins. Tells you adoption is up.
It does not — and structurally cannot — tell you which lines of merged code came from an AI tool versus a human.
Line-level AI authorship. Computed from the diff. Per commit.
GitClear analyzes every merged line and classifies authorship — AI-assisted vs human — using a published, defensible methodology built on 211M lines of research.
And about that "21 mentions of complex configuration."
A side-by-side of the actual setup paths. We're not editorializing — these are the steps each tool requires before a metric appears on a dashboard.
- 01 Sign up with GitHub / GitLab / Bitbucket OAuth ~30s
- 02 Select repos to connect ~1m
- 03 GitClear backfills 90 days of history ~10m
- 04 Diff Delta + AI attribution dashboard live live
- 01 Sales call → SOW → procurement → DPA 2–4 wks
- 02 CSM kickoff, scoping, Jira/Linear field mapping 1–2 wks
- 03 Allocations & investment categories taxonomy 1–2 wks
- 04 Repo ingestion, signal calibration, QA passes 1–2 wks
- 05 Dashboard go-live, exec readout, training +1 wk
Six weeks of onboarding is not a feature.
When the CFO asks "is this metric trustworthy?" — what do you point at?
This is the question that quietly kills engineering analytics rollouts. A dashboard goes up, leadership asks how the numbers are computed, and the answer comes back: "proprietary signals."
That answer doesn't survive a finance review. It doesn't survive a board deck. And it doesn't survive the moment a skeptical staff engineer pokes at one of the charts.
GitClear publishes its Diff Delta methodology, its AI authorship classification rules, and the 211M-line dataset the model was calibrated against. The math is in the open. Engineers can audit it. Finance can trust it. Board members can cite it.
This is not a competitive accident. Jellyfish's most negative G2 reviews — 36 mentions of "metrics issues" — cluster precisely here: numbers that the customer's own engineers couldn't defend in their own building.
# GitClear Diff Delta — published formula sketch cognitive_load = f(added, deleted, churned, refactored) ai_authorship = classifier(diff_features, commit_signature, model_provenance) noise_filter = remove(boilerplate, autoformat, generated_files) # Auditable. Reproducible. Cited.
"Here's the published methodology paper, the diff features used, and the 211M-line dataset it was calibrated against."
"It's based on proprietary allocations modeling. I'd need to loop in our CSM to walk through the signal definitions."
Our pricing is on the page. Theirs is on a sales call.
Volume discounts published. Annual prepay discount published. No "contact sales" wall.
- Diff Delta + AI authorship attribution
- DORA + cycle-time analytics
- Self-serve repo connect, same-day
- Published methodology — fully auditable
- No required CSM, no onboarding fees
Average contract value per Sacra SRC
- No public pricing — sales call required
- Annual contract required up front
- CSM-led implementation (per G2 reviews)
- 6–11 weeks before first dashboard
- "Proprietary" allocations methodology
Connect a repo. See your AI ROI today.
No sales call. No SOW. No six-week onboarding. Just OAuth, a repo, and the dashboard you actually came here for.