Set up in minutes. First insights in hours.
Three steps between you and understanding what's really happening in your engineering team.
Connect your tools
Sign in, add your GitHub or GitLab organization via a Personal Access Token with read access. We never ask for write permissions. Takes about five minutes.
If you want code-level analysis (cyclomatic complexity, code quality metrics), install LocalGit on your infrastructure. It's a lightweight agent that analyzes code locally and sends only metadata to Gitrevio. Your source code stays with you.
We support GitHub, GitLab (cloud and self-hosted), LocalGit for on-premise code analysis, and SQLite uploads for custom data. Jira integration is coming soon. More integrations ship regularly.
AI processes everything
This is the part most tools skip. Raw data isn't intelligence — it's noise. Gitrevio runs your data through multiple AI workers and ML models that extract signal.
Activity classification figures out what people are actually doing. Onboarding models track how new hires ramp up. Risk models identify signs of attrition. Plan-vs-reality analysis shows where estimates diverge from delivery.
First results appear within hours. The models get sharper over the first few weeks as they learn your team's patterns.
Ask anything, anywhere
Open the chat and ask a question. "Why is the mobile team's velocity dropping?" Gitrevio pulls from all your data sources, runs the relevant analysis, and gives you an answer — not a dashboard to interpret.
Build recurring reports that arrive in your inbox. Set alerts that trigger when patterns change. Connect the MCP server to Claude or Cursor so your AI tools have engineering context.
The intelligence meets you where you work. No new tool to learn. No dashboard to remember to check.