A pipeline, not a dashboard
Other tools connect to GitHub and show you charts. Gitrevio connects to everything, processes data through purpose-built AI, and delivers intelligence wherever you need it.
Every signal, one place
Most tools connect to one or two sources. Gitrevio pulls from everything your engineering team touches — and correlates it automatically.
GitHub & GitLab
Commits, pull requests, reviews, issues, deployments, actions. Cloud and self-hosted GitLab.
Jira (coming soon)
Sprints, tasks, epics, story points, cycle time. Integration is in beta — contact us for early access.
LocalGit (on-premise)
Cyclomatic complexity, code quality, file-level metrics. Runs on your infrastructure — source code never leaves.
SQLite Uploads
Upload SQLite databases with custom data. OKRs, incident data, customer feedback — anything you want correlated.
CI/CD Pipelines
Build times, failure rates, deployment frequency. The last mile of your delivery pipeline.
Continuous Sync
Hourly by default, configurable. Every data source stays current without manual exports or CSV uploads.
This is what everyone else skips
Raw data isn't insight. Counting commits isn't understanding. Our AI workers and ML models transform raw engineering data into intelligence you can act on.
Activity Classification
ML models that categorize what developers actually do — coding, reviewing, debugging, meeting, context switching — not what their commit messages say.
Onboarding Analysis
Track each new hire's ramp-up curve. Benchmark against your org's average. Identify what accelerates productivity and what blocks it.
Attrition Risk Scoring
Behavioral patterns that predict when someone might leave. Declining engagement, reduced code review, fewer cross-team interactions. Act before it's too late.
What-If Simulator
Model the impact of changes before they happen. What if Sarah leaves? What if we add a hire to mobile? What if we split the platform team?
Plan vs Reality Engine
Continuous comparison of sprint plans against actual delivery. Not just 'did we finish?' — where and why the drift happens, every sprint.
Knowledge Graph
Who knows what. Where knowledge silos exist. Bus factor per service, per repo, per feature area. Cross-train before it's an emergency.
Anomaly Detection
Unusual patterns surfaced automatically — sudden productivity drops, dual employment signals, work-hour anomalies, emerging bottlenecks. When something changes, Shapley-based attribution decomposes the shift into provably fair causal contributions, not just correlations.
Release Risk Scoring
Per-PR risk assessment based on file hotspots, author history, review coverage, test impact, and change complexity. Code blast radius analysis — formal reachability on dependency graphs — computes which files, services, and teams are transitively affected. Runs locally via LocalGit; source code never leaves.
Context Switching Analysis
How often people bounce between repos, projects, and task types. The hidden tax on engineering output that no standup captures.
Org Health Score
Twenty signals — delivery velocity, code quality, review efficiency, sprint predictability, knowledge distribution, and more — into one composite, drillable number.
Sprint Autopsy
Automated sprint retrospective with data-driven root cause analysis. What planned work got dropped and why. What unplanned work appeared and from where.
Technical Debt Radar
Automated identification of where tech debt is accumulating, how fast, and what it costs in team velocity. Prioritized by business impact.
Probabilistic Estimation
Lognormal distribution models calibrated to your team's delivery history. Every project, epic, and sprint gets p50/p75/p90 completion dates — not a single point estimate that's wrong 80% of the time.
Contributor Typologies
ML-derived behavioral profiles from actual work patterns — review habits, code style, collaboration graph, learning trajectory. Data-driven, not 'senior/junior' labels. Understand how people actually work, not how their title describes them.
Optimal Reviewer Assignment
Smart heuristic PR routing that balances file expertise, queue depth, review quality history, and timezone. Finds the best assignment to minimize wait time while maximizing review quality.
Intelligence that reaches you
A dashboard nobody visits is expensive wallpaper. Gitrevio delivers intelligence through whatever channel you already use — and through channels designed for AI agents, not just humans.
AI Chat
Ask questions in natural language. Get answers with data, context, and recommendations. Understands follow-up questions and can generate charts on the fly.
MCP Server
18 purpose-built tools with role-based access, 50+ pre-built prompts, structured outputs. Works in Claude, Cursor, Windsurf, and any MCP client.
AI Skills
Packageable capabilities for agentic workflows. Automate sprint reviews, risk checks, onboarding monitoring, and board-ready reporting.
Reports
Build custom reports through conversation. Schedule weekly, monthly, or triggered delivery. Sprint retrospectives, onboarding progress, quality trends.
Alerts
Define conditions in natural language. Get notified in Slack, Teams, or email when patterns change, risks emerge, or thresholds are crossed.
Slack & Teams Bot
Ask Gitrevio questions directly in your team chat. Get answers without switching context. Share insights in channels.
REST API
Full programmatic access to every piece of intelligence. Build custom integrations, feed data into internal tools, power executive dashboards.
Dependency Heat Maps
Visual maps of which teams block each other most. Where wait times pile up. Which handoffs consistently fail.
Built for privacy
Every customer gets a dedicated database. Not a shared table with row-level filtering — a completely separate database with its own credentials, its own encryption keys, and its own retention policies.
AI queries run through read-only SQL users that can only SELECT — no writes, no deletes, no schema access. Your data is used to answer your questions, and nothing else.
And with LocalGit, your source code never leaves your servers. We analyze metadata — complexity scores, line counts, file types — never the code itself.