Adoption Visibility
Understand where Claude-centered workflows are being used, by which teams, and with what level of ongoing engagement.
CLAUDELive Integration
Measure Claude-centered engineering impact
Oobeya helps organizations measure Claude-centered AI coding workflows with the same engineering intelligence framework used across delivery, quality, efficiency, and productivity.
Adoption across teams Efficiency and cycle time SonarQube quality context Leadership-ready reporting
What teams can measure
Bring Claude workflows into your engineering KPI model
Use Oobeya to connect assistant usage patterns to the flow, quality, and delivery outcomes your organization already measures.
Efficiency Signals
Compare AI-assisted development activity with engineering throughput, review cycles, and team efficiency trends.
Quality & Risk
Relate usage patterns to SonarQube quality scores, technical debt, bug signals, and maintainability so speed does not hide risk.
Executive Reporting
Use one model to communicate adoption, workflow change, and engineering outcomes in terms leadership can trust.
Inside Oobeya
Evaluate Claude with context, not assumptions
Keep AI-assisted development in the same operating view as engineering efficiency, code quality, and SDLC performance.
AI Impact Overview
Use one executive-ready view for Claude-centered programs
Keep adoption, efficiency, code quality, and engineering delivery in the same frame when evaluating AI-assisted development.
Adoption
81%
Delivery Flow
Stable
Maintainability
A-
Drill-down Views
Drill from organization-level signals to team and user patterns
Identify where Claude usage is concentrated, where value is emerging, and where rollout support is still needed.
Teams Measured
31
Quality Delta
+12%
Lead Time
5.1d
Claude use cases
Built for organizations standardizing AI-assisted development
Support experimentation, governance, and executive reporting with a more complete picture of how AI is affecting engineering work.
Evaluate assistant programs consistently
Measure Claude-centered workflows with the same framework used for GitHub Copilot, Cursor, and broader engineering KPIs.
Keep delivery and quality in scope
Avoid reporting adoption in isolation by connecting usage patterns to cycle time, SonarQube metrics, and DORA outcomes.
Support governance in fast-moving teams
Give engineering leaders a structured way to monitor AI-assisted development as new workflows and tools spread quickly.
AI Coding Assistant Impact
Discuss Claude measurement with Oobeya
See how Oobeya can help your organization evaluate Claude-centered engineering workflows with quality, cycle time, efficiency, and DORA context.
