Do 30% more with the same team
Most engineering analytics tools give you four DORA metrics and a dashboard. Gitrevio gives you answers to 100+ questions about your people, processes, code, and business — through AI chat, reports, alerts, or directly inside your AI tools via MCP.
Free for up to 19 contributors. $40/mo per IC after that. Every feature included.
Questions only Gitrevio can answer
Other tools tell you your cycle time is 4.2 days. Useful, but you already knew things were slow. Here's what Gitrevio tells you that nobody else can.
How long does it take a new hire to ship their first meaningful feature?
How far off are our sprint estimates from actual delivery?
Where is tech debt accumulating fastest, and what's the velocity cost?
How much does a typical feature actually cost us in engineering hours?
What percentage of our merged code is AI-generated, and is it any good?
Who's at risk of leaving, and what's the organizational blast radius if they do?
What percentage of planned sprint work actually gets completed?
Which files change most frequently but have no test coverage?
What's the ROI on our recent engineering hires?
Did adopting Copilot actually improve our throughput?
Most tools skip the hard part
They connect to GitHub, count your PRs, and show you a chart. That's step one. Gitrevio does three things.
Connect everything
GitHub, GitLab (cloud and self-hosted), LocalGit for on-premise code analysis, and SQLite uploads for custom data. Every commit, PR, and code metric in one place. Jira integration is coming soon.
AI makes sense of it
This is what competitors skip. Our AI workers and ML models classify activities, track onboarding curves, score attrition risk, compare plans to reality, detect anomalies, map knowledge silos, and calculate release risk — automatically, continuously. When a metric changes, Shapley-based attribution decomposes the shift into causal contributions so you know exactly what drove it. Lognormal estimation models give you realistic project timelines with calibrated confidence intervals, not gut-feel deadlines.
Available everywhere
Ask questions in AI chat. Build recurring reports. Set alerts on any pattern. Use our MCP server inside Claude or Cursor. Call the API. Plug in an AI skill. Or ask from Slack. The intelligence meets you where you already work.
What happens if Sarah leaves?
No tool in the market answers this question. Gitrevio does. Our What-If Simulator models the impact of team changes before they happen — attrition, hiring, team restructuring, reassignment.
It knows Sarah handles 38% of backend code reviews, owns three critical services, and mentors two junior engineers. Losing her doesn't just remove one person — it creates a 3-week review bottleneck, orphans 12,000 lines of undocumented code, and derails two onboarding tracks.
Now you can plan for it. Or better — use the attrition risk score to act before it happens.
MCP-native. Not an afterthought.
Some competitors recently bolted an MCP server onto their dashboard product. Ours was designed for it from the start. Every piece of engineering intelligence Gitrevio produces is available as an MCP tool — with role-based access control, 50+ pre-built prompts, and structured outputs your AI agents can reason about.
Use it in Claude Desktop, Claude Code, Cursor, Windsurf, or any MCP client. Your AI coding assistant suddenly understands your team — who reviews what, where the bottlenecks are, what the real sprint capacity is.
This isn't a chat widget on a dashboard. It's engineering intelligence as infrastructure.
One number. Twenty signals.
The Org Health Score combines delivery velocity, code quality, review efficiency, onboarding speed, sprint predictability, knowledge distribution, attrition risk, tech debt trajectory, and a dozen other signals into a single, actionable number.
It's not a vanity metric. Each component is drillable. Score drops? You can see exactly which signal moved, in which team, and what caused it. Every week you get a digest explaining what changed and what to do about it.
Your board wants one number? Give them a real one, backed by data from every corner of your engineering operation.
Your estimates are wrong. Here's how wrong.
Software projects follow lognormal distributions — they're rarely early and often very late. Your team estimates "6 weeks" and means it as a point estimate. Reality is a probability curve: 6 weeks if everything goes right, 11 weeks if it doesn't. Most planning tools ignore this. Gitrevio models it.
For every project, epic, and sprint, Gitrevio fits a lognormal distribution calibrated to your team's actual delivery history. You get p50, p75, and p90 completion dates — not a single guess that's wrong 80% of the time. The model learns from every completed project: how much scope creep your team absorbs, how often dependencies slip, how reviews stretch in practice.
This changes the conversation from "when will it ship?" to "is this project worth doing given realistic risk?" A feature with a 3-week p50 but a 9-week p90 needs a different staffing plan than one with a tight distribution around 4 weeks.
INTEGRATES WITH YOUR STACK
The problem with DORA-only tools
DORA metrics tell you how fast code moves from commit to production. Four numbers. That's the engineering equivalent of judging a company by its stock price — technically a metric, but it tells you almost nothing about what's actually going on.
Why do new hires take four months to become productive? What happens to three teams' velocity when your senior architect leaves? Are your sprint plans connected to reality, or are you planning fiction every two weeks? Is anyone burning out? Is the AI code your team is shipping as reliable as the code they write themselves?
These are the questions that keep engineering leaders up at night. DORA can't answer any of them.
Gitrevio can answer all of them — and a hundred more. Because we don't just count pull requests. We understand your engineering operation.