LinearB
AI engineering intelligence platform for tracking DORA metrics and PR cycle time - integrates with GitHub, GitLab, and Jira and raised $25M Series B.
LinearB is an AI-powered engineering intelligence platform that measures DORA metrics, PR cycle time, deployment frequency, and team health by connecting to GitHub, GitLab, Jira, Linear, and PagerDuty without requiring engineers to change their existing workflows. Founded in 2019 by Ori Keren and Dan Lines in Tel Aviv, the company raised a $25 million Series B in 2022 and serves engineering teams at fast-growing companies across SaaS, fintech, and enterprise software. The platform's WorkerB Slack bot delivers automated notifications about stale PRs, blocked reviews, and overdue tickets directly in Slack so teams maintain flow without requiring engineers to check a separate dashboard. LinearB's AI-generated improvement goals suggest data-driven targets for reducing PR review wait time, decreasing deployment risk, or improving planning accuracy based on each team's specific historical performance trend.
Key Features
- DORA metrics dashboard calculates deployment frequency, lead time for change, change failure rate, and mean time to recovery from connected Git and ticketing data
- PR cycle time breakdown shows time spent in each stage - in development, waiting for review, under review, and awaiting merge - per repo, team, and time period
- AI-generated improvement goals suggest specific, achievable targets for each team based on current performance trend and industry benchmark quartile comparison
- WorkerB Slack bot notifies engineers and managers about stale PRs, blocked reviews, and overdue tickets in real time without requiring dashboard login
- Initiative tracking links Jira epics or Linear projects to the pull requests that shipped them so product and engineering share visibility into delivery progress
- GitHub, GitLab, Jira, Linear, and PagerDuty integrations pull all data from existing tools - no new processes or time-tracking required from individual engineers
- Team health reports surface leading indicators of burnout, context switching, and unplanned work increases before they affect retention or delivery quality
Use Cases
- Engineering managers presenting DORA metrics to leadership to demonstrate velocity improvements or justify headcount and tooling investment requests
- CTOs benchmarking their engineering organization against industry quartiles to identify which pipeline stages create the most delay in shipping to production
- Platform teams using cycle time analysis to identify review bottlenecks and justify asynchronous review policies or automated reviewer assignment rules
- Remote engineering teams using WorkerB Slack alerts to maintain PR throughput without synchronous standup meetings or manual status check processes
Pros
- DORA metrics and cycle time data are calculated automatically from existing GitHub and Jira activity - no new processes or manual data entry required from engineers
- WorkerB Slack bot creates improvement feedback directly in the tool engineers already use, increasing adoption compared to requiring dashboard login habits
- Initiative tracking connects business roadmap items to shipped PRs - gives product managers and engineering leads shared visibility into actual feature delivery
Cons
- DORA metrics are lagging indicators that show what happened last sprint - LinearB identifies patterns but requires separate retrospective processes to fix root causes
- Accurate cycle time analysis requires consistent PR and ticket hygiene - teams that merge directly to main or skip Jira ticket linking get incomplete data gaps
- Free tier limits team size and historical data range - growing engineering organizations will need the paid plan as headcount grows beyond the free tier threshold
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