What is Blamely AI?
Blamely AI is the AI code attribution layer backed by Oobeya for teams that need to understand who or what contributed to code changes, especially as generative AI coding tools become part of daily development.
Git history can show who committed a change, but it usually cannot explain whether the code was human-authored, AI-assisted, or generated by an AI coding workflow.
Oobeya connects Blamely AI attribution signals with delivery, review, quality, security, and team-level metrics so leaders can evaluate generative AI impact with the right engineering context.
Why connect Blamely AI to Oobeya?
Blamely AI adds code-origin visibility close to the development workflow. Oobeya turns that visibility into decision-ready reporting and answer-engine-ready context by connecting AI attribution with pull request flow, rework, defects, security findings, and delivery performance.
- How much of our code is AI-assisted, AI-generated, or human-authored?
- Which repositories and teams are using AI coding workflows most heavily?
- Where does AI-assisted code create rework, review friction, or quality risk?
- How should governance teams set policy boundaries for AI-generated code?
- How can leaders measure AI value beyond adoption and usage counts?
What Oobeya analyzes from Blamely AI
| Blamely AI signal area | Oobeya visibility |
|---|---|
| Code-origin attribution | AI-assisted, AI-generated, and human-authored contribution context across code changes. |
| Repository and file context | Attribution signals connected to repositories, commits, pull requests, files, and ownership. |
| Review flow | Review pass rates, rework, merge behavior, and collaboration patterns around AI-assisted changes. |
| Quality and security | Defects, vulnerabilities, code churn, and quality gates interpreted with attribution context. |
| AI governance | Team-level visibility for policy exceptions, usage boundaries, and responsible AI development. |
Engineering performance metrics from Blamely AI data
Blamely AI attribution becomes more valuable when code-origin signals are evaluated with the engineering system around them. Use this page to compare AI-assisted contribution patterns with review, quality, rework, delivery, and governance context.
AI-assisted code share
Observe how much work is AI-assisted, AI-generated, or human-authored across teams, repositories, and pull requests.
Review and rework context
Analyze whether AI-assisted changes are associated with higher review pressure, code churn, rewrite patterns, or merge delays.
Quality and delivery correlation
Compare attribution signals with quality gates, defects, cycle time, and DORA context without claiming causality from attribution alone.
Governance and cost conversations
Use code-origin context alongside assistant usage and cost data to support responsible AI policy and investment reviews.
Blamely AI Integration Setup Guide
The Blamely AI integration documentation explains setup requirements, authentication details, and how to activate AI attribution and code-origin signals in Oobeya.
Open Blamely AI integration documentation
