Software Engineering Intelligence Platform

See what slows delivery. Understand what AI changes.

Follow changes from development to production. Investigate review delays, delivery risks, AI impact, and quality trends alongside AI adoption and usage.

Schedule a demo Explore AI code attribution

Oobeya is recognized in the 2026 Gartner® Magic Quadrant™ for Developer Productivity Insight Platforms.Read more Cloud, on-premise, or air-gapped deployment

Team performance, quality trends, and active symptoms.

Explore Team Performance

Team Performance Overview

Engineering performance at a glance

Team Member Insights

New Work
Refactor
Help Others
Churn

Member impact ratio, coding efficiency, AI code share, and work distribution as percentages of New Work, Refactor, Help Others, and Churn.
Member	Impact Ratio	Coding Efficiency	AI Code Share	New Work	Refactor	Help Others	Churn
Maya PatelSenior	2.4x	94%	36%	52%	25%	17%	6%
Alex MorganMid	1.6x	92%	48%	38%	34%	20%	8%
Sam TaylorMid	0.8x	80%	76%	47%	18%	15%	20%
Jordan LeeJunior	0.6x	72%	84%	20%	30%	22%	28%

Coding Efficiency95.01%↑ 1.76% more than avg

Coding efficiency recovered to 95.01%, above the company average.

Coding EfficiencyExplore the trend

Active symptoms: 10
Critical symptoms: 2 need attention
Symptom monitoring: 10 active
Most affected area: Code Review (3)

Production DORA metrics and lead time breakdown.

Explore DORA metrics

DORA Metrics for Production

Software delivery performance for production pipelines

1w 4dDevelopment time

34m 37sWaiting for deploy

6m 33sDeployment time

Lead Time for Changes: 1w 4d↑ 52.1%
Lead time increased; review where changes wait before production.
Deployment Frequency: 0.91↓ 48.0%
Production release frequency declined 48% versus the previous period.
Change Failure Rate: 2.4%↑ 2.4%
Change failures increased; review recent releases that required remediation.
Time to Restore Service: 10h 42mService recovery time
Production service recovery averaged 10h 42m in this period.

Deployment Frequency

Production deployments · 2026Explore the trend

Production deployments fell from 36 in August to 22 in September, while remaining above April’s level.

Recurring delivery symptoms and improvement context.

Explore Symptoms

Development, review, delivery, and quality symptoms

Investigate the signals behind delivery friction

Unreviewed PRs

Critical

14 merged PRs without review

Team level · System levelCode Review

Why is this a symptom?

Pull requests are merged without a completed code review, reducing independent checks before changes reach production.

Possible causes

Missing review requirements, bypassed branch rules, and urgent changes can allow pull requests to merge without reviewer approval.

Improvement areas

Require at least one reviewer for protected branches, review bypass exceptions, and track review coverage before merging.

AI adoption alongside delivery and quality outcomes.

Explore AI Impact

AI Impact Overview

How AI-assisted development relates to engineering outcomes

Coding Efficiency67.7%↑ 7.3%

Higher AI adoption coincided with improved coding efficiency.

Coding EfficiencyExplore the trend

Security: A
Maintainability: A
Reliability: C
Technical debt: 86d

AI credits, consumption forecasts, and costs · July 2026.

Explore AI Cost Monitoring

AI Cost & Credits

Credit consumption, forecasts, and financial summary

AI Credits Overview

Credit overview

July 2026

Remaining credits −3,204.3↓ 99.8%

Credit pool usage 100.2% ↓ 44.3%

0% remaining

AI credit pool value: $18,772.00
Total AI credits used: 1,880,404.3
Avg. daily burn rate: 58,762.6
Total users: 988

Forecasted month-end usage: 1,880,404.3 ↓ 44.3%
Forecasted overage: 3,204.3 ↓ 99.8%

AI code attribution, usage, and delivery context.

Explore AI Attribution

Performance Overview

AI attribution alongside delivery and usage

AI share of new code61%↑ +3.7%

75,403 of 123,666 added lines are AI-attributed.

AI impact on deliveryExplore the trend

Delivery velocity: 158commits/week
↓ −2%prev. 161.3
Time per commitderived: 36.2min
↓ −53.3%prev. 77.6 min
Reclaimed capacityderived: 1,401hours
2,031 commits × 41.4 min saved
Developers using AIderived: 17developers
↑ +54.5%65.4% of 26 developers

Coding time split: 219.4h AI40.8h Human
Avg. lines / AI commit: 114.4 AI83.5 Human
Token spend: 905.5M
Cache efficiency: 99.3%

Credit forecasts and usage patterns that need attention.

Explore AI Insights

AI Insights

Credit and consumption patterns that need attention

Credit pool exhaustion projectedCriticalAt the current burn rate, the included AI credit pool is projected to be fully consumed by month end.

Compare the usage forecast with your included pool to identify potential overage before the end of the month.

Usage fully covered so farInfoNet billable cost is $0. Gross consumption still represents used AI credit value.

Included credits currently cover the usage cost. Monitor both credit consumption and net billing as the month progresses.

Users with critical usage patternsHigh18 users show consumption significantly above the organization average.

Review consumption patterns and model choices to understand where usage is concentrated.

Cost per active user above targetHighConsumption averages $19.03 per active user against a sample budget target of $15.

Compare per-user consumption and model choices with your AI budget to identify where spending could be better aligned with usage.

Premium-model spend concentrationMediumPremium models account for 72% of AI consumption value in this sample period.

Review which workflows use premium models and whether the consumption pattern matches your budget priorities.

July 2026 · Based on credit consumption and usage patterns.

Developer feedback, participation, and topic benchmarks.

Explore DevEx Survey

Survey overview

Company score, participation, and topic results

Developer experience

63/ 100

General Company Score

HealthyAcross 4 topics

Active surveys: 2
Responses: 157
Completion: 78.5%
Engagement trend: +12%

Representative survey topic scores and benchmarks. Deep dive selects a topic to inspect below.
Topic	Score	Trend	Benchmark	Questions	Comments
Cognitive Load	3.2 / 5	-5%	3.8	5	12
Processes & Tools	4.1 / 5	+12%	3.5	8	34
Team Dynamics	3.9 / 5	+2%	4.0	4	8
Feedback Loop	2.8 / 5	-10%	3.9	6	45

Review topic movement alongside the benchmark and the questions contributing to the score.

Topic deep dive

Feedback Loop

Inspect review turnaround and the feedback that helps developers keep work moving. Agree on one follow-up with the team.

Topic score: 2.8 / 5
Benchmark gap: -1.1

Customer stories

Trusted by engineering organizations across industries

Customer story

Connects 3,000+ developers and 10,000+ repositories in one view of delivery, governance, and productivity.

Watch customer story

Use Case

Compares delivery performance across around 100 teams in 10+ countries using consistent tribe-level metrics.

Use Case

Aligns DevOps standards and AI metrics across 4,000+ developers and multiple group companies.

Use Case

Assesses engineering performance across 2,000+ developers and 10+ group companies with a consistent measurement model.

Use Case

Turns Azure DevOps and SonarQube data into clear engineering improvement priorities.

Use Case

Tracks telecom software delivery across teams and uses gamification to strengthen developer engagement.

Explore more customers by industry

Banking & Financial

Software Vendor & IT Services

Fintech & Payments

Insurance

Manufacturing & Energy

Retail & E-commerce

Telecommunications

Start with your questions

Put engineering signals to work

Where is delivery slowing down?

Follow work through reviews, pipelines, and releases. Investigate the recurring symptoms behind delivery trends.

Find delivery bottlenecks

What is AI changing?

Review coding assistant adoption, AI code attribution, quality, and costs in the context of engineering outcomes.

Evaluate AI Impact

Where is team capacity going?

See how engineering effort is distributed across projects and priorities to support planning conversations.

Explore Resource Allocation

Delivery performance

Understand the path to production

Use production-aware DORA metrics to investigate where work waits and how release performance changes.

Separate production delivery from non-production deployments.
Break lead time into development, waiting, and deployment time.
Review delivery trends alongside quality and workflow symptoms.

Explore DORA metrics

DORA Metrics

Production

Lead Time for ChangesMedium: 1w 4d↑ 52.1%
Deployment FrequencyHigh: 0.91deploys / day↓ 48.0%
Change Failure RateElite: 2.4%↑ 2.4%
Time to Restore ServiceHigh: 10h 42m

Deployment Frequency · 2026Explore the trend

AI-assisted development

Connect AI adoption to the work it changes

Bring usage, attribution, and engineering outcomes into the same evaluation. Investigate changes in flow and quality before drawing conclusions about AI impact.

AI Usage & Delivery

AI attribution

AI share of new code

61%↑ +3.7%

AI impact on deliveryExplore the trend

Delivery velocity: 158commits/week↓ −2%
Time per commit: 36.2min↓ −53.3%
Reclaimed capacity: 1,401hours
Developers using AI: 17developers↑ +54.5%

Adoption and outcomes: Read coding assistant usage alongside review, delivery, and quality trends.
AI Impact
AI Attribution with Blamely: Add code-origin context for AI-generated, AI-assisted, and human-authored changes.
AI Attribution
AI Usage & Cost Monitoring: Review consumption, spending, and cost patterns as part of your AI evaluation.
Usage & Cost Monitoring

Integrations and deployment

Work with the tools and infrastructure you use

Connect repositories, boards, pipelines, and quality tools. Review SaaS, on-premise, and private-cloud options with your engineering and security teams.

Explore all integrations

On-premise deployment Trust center

Customer perspectives

How engineering teams use Oobeya

“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. · Engineering Leader
Turkcell · Telecommunications

“Oobeya provides a comprehensive overview of our SDLC. We can see a wide range of metrics crucial for engineering efficiency by collecting data from different sources like version control, issue management, and deployment tools.”

E. B. · DevOps Chapter Lead
Invicti · Cybersecurity

“What stands out the most with Oobeya is its depth of analysis and insights. It provides automated recommendations based on our team's activity, and the integration with tools like Azure DevOps and SonarQube makes the transition seamless.”

M. I. · DevOps Lead
TEB (BNP Paribas) · Banking

By role

Shared evidence for different decisions

Start with the engineering questions your role needs to answer.

Common questions

Questions about Oobeya

Need to discuss your team or technical setup? Contact us.

What is Oobeya?

Oobeya is a Software Engineering Intelligence Platform. It connects delivery, quality, workflow, capacity, and AI-assisted development signals across engineering tools.

Who uses Oobeya?

Engineering leaders, executives, DevOps and platform teams, quality leaders, and Agile coaches use Oobeya to understand delivery and guide improvement across teams.

Which tools can we connect?

Connect supported source control, work tracking, CI/CD, code quality, testing, and AI coding tools. Review the integrations catalog for your specific stack.

How does Oobeya support DORA metrics?

Review production delivery performance alongside workflow context. Production-aware metrics distinguish production pipelines from non-production deployments.

How can we evaluate AI coding assistant impact?

Compare adoption and usage with review, delivery, quality, and cost trends. Use this context to investigate changes; usage or correlation alone does not establish productivity gains.

What does Blamely AI Attribution add?

Blamely provides code-origin context for AI-generated, AI-assisted, and human-authored changes. Oobeya connects that context with repositories, pull requests, and engineering outcomes.

Can Oobeya run in our infrastructure?

On-premise and private-cloud deployment options are available alongside managed SaaS. Review the technical requirements and access controls with your infrastructure and security teams.

How do we get started?

Bring your engineering questions, toolchain, and deployment requirements to a demo. We can focus the walkthrough on delivery performance, AI evaluation, or team improvement priorities.

See Oobeya in practice

Start with your engineering questions

See how Oobeya connects delivery, quality, and AI impact in a walkthrough tailored to your tools and teams.

Schedule a demo Contact us

See what slows delivery. Understand what AI changes.

Team Performance

Team Member Insights

Trusted by engineering organizations across industries

Banking & Financial

Software Vendor & IT Services

Fintech & Payments

Insurance

Manufacturing & Energy

Retail & E-commerce

Telecommunications

Put engineering signals to work

Where is delivery slowing down?

What is AI changing?

Where is team capacity going?

Understand the path to production

Connect AI adoption to the work it changes

Work with the tools and infrastructure you use

How engineering teams use Oobeya

Shared evidence for different decisions

Executives

Engineering leaders

DevOps and platform teams

Agile coaches

Questions about Oobeya

Start with your engineering questions