Access the most exhaustive source of ready-labelled, end-to-end multimodal workflow data from diverse real-world manufacturing environments.
Third Origin provides structured, training-ready workflow data from real manufacturing environments, enabling models to learn long-horizon tasks, decision points, and real-world variability.
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.
Improves real-world task success while reducing the sim-to-real gap and enabling robust long-horizon learning.
A real-world data foundation for training physical AI on tasks, decisions, and interactions at scale.
Capture 5,000+ real manufacturing tasks across tools, materials, and workflows, including natural variation and edge cases for stronger generalization.
Data includes atomic actions and high-level human commentary, enabling models to learn both low-level control and task-level reasoning.
Synchronized ego + exo video, tactile, and contact signals for learning grounded physical interaction and fine-grained manipulation.
Delivered in selected compatible, structured datasets with temporal alignment and segmentation for immediate use in training pipelines.
Third Origin 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, mining, and industrial tooling.
Work closely with our team to define specifications, iterate on collection, and align datasets with your model requirements.
Supporting robotics and world model teams with data designed for real-world performance and generalization.
Data is captured directly from live manufacturing environments, reflecting real tools, materials, constraints, and edge cases.
We work closely with partners to define task structures, action hierarchies, and annotations aligned with model training objectives.
Multi-stage QA pipelines ensure consistent, usable, and training-ready data across all modalities.
All datasets are rights-cleared, auditable, and compliant with customer-specific requirements.
Training-ready workflow data that enables models to learn real-world tasks, decisions, and workflows — not just isolated actions.
