Create leadership-ready reporting
Bring delivery, quality, workflow, and AI signals into consistent leadership reporting.
For executives
Connect engineering performance to business decisions
Give CIOs, CTOs, and business leaders a shared view of delivery risk, roadmap confidence, engineering investment, and AI impact.
Executive visibility
Answer the questions behind the metrics
Bring delivery, quality, and workflow signals into leadership discussions about performance, risk, and roadmap confidence.
Review engineering benchmarks- Where is delivery risk increasing?
- Connect workflow bottlenecks, quality signals, and release behavior to understand where execution confidence needs attention.
- What is affecting engineering investment outcomes?
- Review performance across teams and initiatives with context from planning, repositories, CI/CD, and quality tools.
- Is AI adoption improving delivery?
- Compare assistant usage with throughput, review burden, and quality. Use outcomes alongside adoption counts in leadership reporting.
AI investment
Evaluate AI with engineering evidence
Assess adoption together with delivery, quality, and attribution signals to inform rollout and governance decisions.
- Adoption and usage
- Understand which teams use coding assistants and how usage changes across the organization.
- Delivery and quality outcomes
- Review AI-assisted activity alongside throughput, review pressure, and code quality trends.
- AI code attribution
- Use Blamely AI to distinguish AI-generated and human-authored code, adding contribution context to your evaluation.
Leadership reporting
Make performance easier to review and explain
Track delivery confidence across teams
Identify recurring friction, review delivery predictability, and decide where follow-up is needed.
Evaluate AI impact in a more practical way
Review AI-assisted development alongside throughput, review burden, quality, and governance requirements.
Customer reviews
Feedback from engineering leaders
According to our POC evaluations around IT productivity measurement, Oobeya was the tool that best met our expectations. Its integration flexibility, Symptoms capability, and DORA metrics gave us visibility across nearly 4,000 developers and helped us clearly see inefficient points, including outsourced projects.
A. D. · Turkcell
Engineering Leader
Telecommunications
Oobeya's onboarding process was impressively smooth. The support team is highly responsive, and the automatic detection of issues with actionable suggestions has been invaluable for a busy development team.
M. F. A. · Experilabs
Chief Technology Officer
Software & Innovation
We use Oobeya effectively in measuring our software development processes. We can manage our processes better by evaluating the outputs it produces. You cannot manage data that you cannot measure.
A. C. · Etiya
Senior Manager, Solution Development
Telecom Software
Based on G2 and Gartner reviews.
Governance and rollout
Plan the review before the rollout
Bring security, platform, and procurement stakeholders into the evaluation with clear requirements.
- Security and access
- Review identity integration, role-based access, auditability, and security requirements.Trust Center
- Deployment fit
- Evaluate SaaS, private cloud, or on-premise options against infrastructure and data residency needs.Deployment options
- Commercial scope
- Align team size, reporting needs, onboarding, and support with your rollout plan.Plans and pricing
Executive FAQ
Before your evaluation
Reporting, AI impact, and enterprise rollout questions.
What does Oobeya show executive teams?
A connected view of engineering performance, delivery risk, roadmap confidence, quality, and AI-assisted development impact.
Who is this view for?
CIOs, CTOs, VPs of Engineering, transformation leaders, and business stakeholders reviewing engineering performance.
How can we evaluate delivery risk?
Review workflow bottlenecks, release behavior, quality signals, and recurring friction across teams and initiatives.
How can we evaluate AI investment?
Compare assistant adoption with throughput, review burden, and quality trends. Use engineering outcomes alongside usage when evaluating rollout decisions.
Can we distinguish AI and human code contributions?
Yes. Blamely AI provides code attribution context for AI-generated and human-authored code.
Does Oobeya work across different tools?
Yes. Connect planning, repositories, CI/CD, quality, and workflow systems to review performance across your engineering toolchain.
Can Oobeya fit enterprise deployment requirements?
SaaS, private cloud, and on-premise options are available. Include your security, access-control, and governance requirements in the evaluation.
What should we bring to a demo?
Your team structure, toolchain, deployment requirements, and the leadership questions you want reporting to answer.
Executive conversation
Review the engineering questions behind your decisions
Tailor a walkthrough to your delivery risks, AI investment, governance, and reporting needs.