For engineering leaders rolling out AI code review
The board approved the AI budget.Soon they'll ask what it changed.
Most engineering leaders answer that question with a feeling. You'll answer it with your team's own PR data - review turnaround, throughput, and quality signals, before vs after AI - in one board-ready view.
- Read-only GitHub access
- No credit card
- First numbers in 5 minutes
The question is coming
It's the Monday leadership sync. Halfway through, the CFO looks up from the spend report: 'The AI licences renewed last month. What did they change?'
The room turns to you. You have an adoption count. You have a feeling that PRs move faster now. You have a Slack thread where two seniors argued about whether the review queue got better or worse.
That's not an answer. That's a guess wearing a badge.
Here's the part that stings: the real answer already exists. It's sitting in your GitHub history right now - every PR, every review, every timestamp, before and after the rollout. You just can't see it yet.
The industry-wide answer is: it's complicated
84% adoption, trust falling
84% of developers now use AI coding tools. Trust in the output fell from 40% to 29% in a year.
Stack Overflow Developer Survey 202598% more PRs, 91% more review time
Teams with high AI adoption merge 98% more pull requests. Time spent in review rises 91%.
Faros AI, telemetry across 10,000 developersChurn up, duplication up 8x
Code churn nearly doubled from 3.1% to 5.7%, and duplicated code blocks grew 8x.
GitClear, 2025 researchSo when the board asks 'is it working?', the honest industry answer is: it depends on the team. Your job is to know which side your team is on. The good news - that answer is cheap to get.
Find out which side your team is on
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Before and after, from real pull requests
CodePulse compares developers using AI tools against those who aren't, on the numbers the board actually cares about: cycle time, review turnaround, PRs per week. Every figure is computed from your merged pull requests. Not a calculator. Not a survey. Not vibes.
- Cycle time, review turnaround, LOC per day and PRs per week, with the difference shown as a percentage
- Tracks 11 tools including GitHub Copilot, Cursor and Claude Code
- The comparison marks itself invalid when groups are too small to mean anything
Numbers your engineers won't revolt over
Individual before/after analysis is opt-in, and it needs at least three developers in each group so nobody becomes a talking point. Measurement here is a flashlight, not a spotlight: your engineers see the same dashboards you do.
- Team-level by default; individual analysis only with explicit opt-in
- Minimum group size of three protects everyone from becoming 'the slow one'
- Built anti-surveillance from day one - it's the reason teams keep it
Is review keeping up with the new volume?
More AI code means more pull requests landing on the same reviewers. CodePulse splits cycle time into its four phases, maps who reviews whom, and flags rubber stamps: large diffs approved in under a minute with no comments. If your seniors are quietly absorbing the extra load, you'll see it this week - not at their exit interview.
- Four-phase cycle time: coding, waiting for review, in review, merge
- Review Network shows exactly who carries the review load
- Rubber-stamp flags catch approvals that never really happened
One thing we won't pretend: AI tool usage in CodePulse is self-reported by your developers. We don't scan code to guess who used what - that kind of detection is unreliable, and pretending otherwise would poison your numbers.
See your review pipeline this afternoon
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The thing that sold me was our first planning meeting after connecting it. I'd been telling the CTO we were slow because of tech debt - turns out almost a third of our PRs were just sitting there waiting on the same two reviewers. We redistributed reviews and the difference was noticeable within a couple weeks.
From another engineering manager
"Our retros used to be kind of a blame game. Now I just pull up the dashboard and it's right there. We found out review turnaround was way worse than anyone thought, almost three days on average. Got it down to same-day once we actually saw it."
Free
Up to 10 active developers, no expiration. Flat pricing after that - your bill doesn't grow when your team does.
$57,536 a year
Median Jellyfish contract on Vendr: quote-only pricing and a 2 to 9 month rollout before you see a number.
Jellyfish contract figures: Vendr median contract data. CodePulse pricing: pricing page. Vendr Jellyfish contract data
- No credit card, no sales call
- Read-only GitHub access - we can't push, merge or change anything
- We never clone your repos or read source code
- Setup takes about five minutes on your work GitHub account
- If the first sync doesn't show you something you didn't know about your review pipeline, close the tab and keep the free tier anyway
No credit card required
Frequently Asked Questions
Read-only metadata: commit timestamps, PR titles and status, review events, changed-file paths and line counts. Never file contents or diffs, and we never clone your repository. GitHub tokens are encrypted per organization.
No credit card required
Your next board meeting is already on the calendar.
Walk in with the chart. Connect your work GitHub account and get review turnaround, throughput, and AI before/after numbers - free.