FeatureFactory vs LinearB

For PR workflow automation, LinearB is genuinely strong. gitStream lets you codify review policies - auto-approving low-risk changes, routing risky ones, enforcing checks - and it is a mature, well-executed part of their platform. If PR policy automation is your primary need, LinearB does it well. FeatureFactory focuses instead on line-level AI-vs-human measurement and closing the loop from product plan to shipped outcome; workflow automation is on our roadmap, not our wedge today.

LinearB measures AI adoption by correlating it at the pull-request level - a useful signal for tracking rollout. FeatureFactory goes finer: it attributes authorship line by line, distinguishing AI-written code from human-written code, and scores each on quality. That lets you answer what the AI actually wrote and whether it survived review, not just whether AI was involved in a PR.

LinearB meters certain usage in credits, at roughly 100 credits per PR. That model works for some teams but often confuses buyers trying to forecast spend as PR volume scales. FeatureFactory prices flat per seat, published openly on our site, so cost is predictable regardless of how many PRs you ship.

Yes. Teams that rely on LinearB's gitStream automation and SEI dashboards can add FeatureFactory for line-level AI-vs-human quality scoring and product plan-to-outcome traceability. You do not have to replace an existing SEI platform to get line-level AI attribution.