Turn real world failures into repeatable evals and learn what to fix next.
Every failure should explain itself
We ingest real world rollouts alongside the data, annotation, eval, and policy versions that produced them.
We group recurring failures, turn them into repeatable evals in simulation, and identify the pipeline changes most likely to explain an improvement or regression.
Grapes into grey boxLego into Lego bagBrown bar into top pocketToy jeep into grey boxTeacup into Lego bagQuick, grab the red blockClose the laptopGrab the bottleSort grapes with polar bear
We build for teams that need to learn from robot failures faster.