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AI Analytics & Attribution

Measure AI-assisted development and understand how LinearB attributes AI activity across your organization.

Steven Silverstone
8 articles by 1 author

A simple icon of a bookmark with a star near the bottom to represent a featured article A simple ribbon icon. It has a slightly rounded border and a star at the bottom of it. ​ AI Insights – Overview

Understand how AI tools affect your engineering performance. AI Insights connects AI usage, acceptance, and trust signals to delivery, quality, and DORA metrics so you can measure real impact, not just adoption.

AI Analytics: Adoption, Workflow, and Impact

Measure AI adoption, workflow touchpoints, and impact across pull requests, repositories, and teams.

AI Cost and Token Consumption

Analyze estimated AI cost based on AI usage across pull requests, developers, teams, models, and vendors.

AI FAQs - Governance, Detection & Trust

Answers common questions about AI detection, metrics, security, MCP access, and AI Reviews. Use this FAQ to understand how LinearB identifies AI activity and how AI insights are calculated and protected.

Attributing Bot-Authored PRs in AI Analytics

Overview. The Bot-Authored PR Attribution Strategy determines how LinearB attributes pull requests created by bot accounts, including AI coding agents. Use this setting to decide whether bot-authored…

Configuring AI Classification Thresholds

Use Classification Thresholds to control how LinearB classifies commits and pull requests as AI-assisted. These settings affect how AI activity is labeled in the product and how AI Analytics metrics…

How LinearB Calculates AI Attribution

Learn how LinearB identifies AI-assisted development across your codebase. This article explains how AI attribution is calculated using commit signals and AI tool data, how confidence thresholds work, and how results are applied to commits and pull requests.

Improving AI Attribution Accuracy and Coverage

Best practices for improving AI attribution accuracy and coverage in LinearB using commit metadata, branch signals, contributor mapping, and frequent commits.

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