DryDock vs LinearB
LinearB is the closest of the engineering-intelligence platforms to what DryDock does, because it acts as well as measures — its gitStream automation applies policy-as-code to PR workflows. The difference is scope and purpose.
LinearB is built for the delivery manager: find bottlenecks in cycle time, then automate PR workflows to fix them. Its automation is genuinely useful — but it optimizes for flow. DryDock optimizes for control in an AI-era pipeline: distinguishing AI-authored changes, applying release-level policy across the whole pipeline (not just PRs), routing judgment calls to named humans, and producing compliance-grade evidence. Flow and control are different products.
Side by side
| LinearB | DryDock | |
|---|---|---|
| Primary job | Improve delivery speed: metrics + PR workflow automation | Govern delivery: policy, human routing, audit evidence |
| Automation scope | PR workflows (gitStream rules) | Whole pipeline: PRs, releases, deploys, freezes, incidents |
| Optimizes for | Flow — shorter cycle time | Control — provably governed releases at speed |
| AI-authored code | Limited distinction at metadata level | First-class: tagged, separately scored, stricter gates |
| Audit evidence | Metrics history | Decision-level evidence log, exportable per framework |
| Human-in-the-loop | Auto-approve rules to reduce human touches | Policy-routed approvals where judgment is required |
| Best buyer | Delivery managers fixing bottlenecks | CTOs / VPs / compliance accountable for AI-era releases |
The verdict
Choose LinearB if your pain is slow delivery and PR friction. Choose DryDock if your pain is trust: proving AI-heavy releases meet policy, keeping humans in the right loop, and walking into audits with evidence instead of archaeology.
See governance in action
DryDock's crew of specialised AI agents enforces policy, routes human decisions, and builds your audit evidence continuously — live in your pipeline in a week.
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