Gitrevio
Gitrevio is an engineering analytics platform that connects to the tools engineering teams already use—GitHub, GitLab, Jira, Linear, and CI/CD systems—to turn raw engineering data into actionable business insights. It helps management understand where engineering time goes, where work slows down, and whether investments are paying off, without requiring teams to build reports manually. The platform uses AI-powered analysis and a library of 388 analytic decision functions to answer questions about delivery, rework, risk, and AI tool impact.
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Developer Tools
- Enterprise Software
- Software Only
Funding
Founders
Product
Problem
Engineering is often one of a company's largest investments, yet leadership struggles to see what they are getting for it. Data is scattered across GitHub, GitLab, Jira, Linear, CI/CD systems, and AI tools, making it difficult to connect the dots between engineering activity and business outcomes. Without a unified view, teams waste time building reports and management cannot easily identify where time is going, where work is slowing down, or where risk is growing.
Solution
Gitrevio connects to the systems engineering teams already use and analyzes the data over time to provide a clear picture of what is happening across the organization. Instead of asking teams to build reports or manually sift through multiple tools, users can ask questions and get answers based on actual engineering data. The platform measures signals behind engineering performance—including delivery cycle time, code changes, pull request reviews, rework, incidents, and AI tool usage—and translates them into insights about cost, risk, and value. Gitrevio goes beyond simple activity counting by using causal inference, forecasting, and statistical audit methods to explain what changes mean for the business and what to look at next.
Target Audience
Primary customers are engineering leaders, CTOs, VPs of Engineering, and team leads at software companies who need visibility into engineering performance, collaboration health, and return on investment. The platform also serves product managers and executives who want to understand delivery risk, rework trends, and the business impact of AI tool adoption.
Features
- 388 analytic decision functions across 9 method families and 16 problem domains, covering forecasting, causal inference, constrained optimization, and statistical audit
- MCP server exposing 18 tools, with three that search the catalog, read tool schemas, and execute analyses automatically
- Team health insights showing collaboration signals like review participation, cross-team pull requests, and knowledge sharing, plus risk signals like overload and communication gaps
- AI impact tracking that measures whether AI tools are creating value, with spend governance features to control costs
- What-If Simulator and plan-vs-reality analysis to model changes before committing resources
- Causal inference capabilities to understand why delivery is getting better or worse, not just what changed
- Delivery risk alerts and productivity flow insights with trend visualizations over 30-day periods
- Enterprise-grade features including SSO, data residency options, privacy controls, and a LocalGit deployment option