The bottleneck came before the robots
Before Vision Lab, we worked on computer vision at industrial automation companies and robotics startups. The recurring pattern was simple: a model iteration finished, then engineers waited 2-3 weeks for labeled data before starting the next training cycle.
This was a workflow failure, not a missing technology. Available annotation tools were designed for images, not video. Review stayed linear, giving every frame equal treatment instead of using confidence. Coverage gaps surfaced only after training runs took a week to evaluate.
We built Vision Lab around three fixes: video-native annotation, confidence-based review prioritization, and coverage reporting that exposes gaps before training rather than after.
Early access opened in early 2026. Feedback from the first cohort matched our experience: the bottleneck was real, and the fix became straightforward when built around robotics teams' actual workflow.
