Most physical AI models are trained on synthetic, staged, or narrow lab data. Cravt captures real human workflows directly from manufacturing environments — including real tools, materials, constraints, and edge cases — and delivers training-ready datasets, not raw footage.
Capture data from live manufacturing workflows, not synthetic or staged setups.
Go beyond isolated actions with multi-step tasks, transitions, and decision points.
Deliver labeled, formatted, model-ready datasets rather than unusable raw video.
Cravt.ai combines broad manufacturing reach with a curated network of factories that allow direct workflow capture. This creates access to a high-diversity stream of real-world tasks across sectors including garments, cosmetics, packaging, electronics, and industrial tooling.
Use broad workflow data to train models on real-world industrial sequences and context.
Improve policy learning and task understanding with structured egocentric + exocentric workflow datasets.
Commission tailored dataset capture for specific workflows, tasks, or environments.
Cravt.ai starts with training-ready workflow data. It is our mission to become the infrastructure layer that helps AI systems learn not only isolated skills, but craft: the nuanced, contextual, end-to-end way real work gets done in the physical world.
See sample workflows, explore available datasets, or discuss a tailored data collection program.