Comparison
FeatureFactory vs GitClear
GitClear pioneered line-level AI-vs-human attribution and the rework research our thesis is built on. FeatureFactory extends that signal into a plan→build→measure loop.
The short version
GitClear is the reference point for line-level AI-vs-human authorship and rework research, and it does that deeply and rigorously. FeatureFactory is bottoms-up on the same attribution but adds product plan generation, the closed loop, and manager/exec ROI packaging on top.
Why teams pick FeatureFactory
Line-level attribution, then act on it
GitClear pioneered line-level AI-vs-human authorship and made it credible. We score the same signal - who wrote each line and whether it held up - but treat it as the input to a workflow, not the destination.
The closed loop, not just the dashboard
GitClear is analytics- and research-oriented: it measures. FeatureFactory closes the loop - product signal informs a product plan, the work gets built, and we measure again - so attribution feeds the next decision instead of sitting in a report.
From metric to product plan to shipped feature
GitClear stops at rich measurement. We package attribution into manager and exec ROI views and generate product plans with acceptance criteria, connecting what the AI wrote to what the team plans next.
Developer-led and agent-aware
Engineers adopt FeatureFactory directly and bring their own coding agent - Claude Code, Cursor, Devin. Attribution and quality scoring run on the code those agents produce, inside the same loop your team already works in.