Machines learn by watching skilled people work
Skilled people in homes, factories, and workplaces worldwide demonstrate how real work gets done. Poseidon runs the collection and refines it into qualified, model-ready datasets for the labs building physical AI. Built on direct collection relationships with skilled factories and workforces in Korea, across Asia, and beyond.
Machines learn physical skill the way apprentices always have: by watching experts. Poseidon runs that apprenticeship at industrial scale, with deep reach in Korea and across Asia.
The next generation of AI acts in the physical world. Its training data lives in people’s hands, in homes and factories, in the way work actually gets done. None of it can be scraped.
What little exists online rarely qualifies: footage without synchronized streams, distributions nobody controlled, activity staged for the camera. A model is only as good as the demonstrations it learns from.
Qualified footage is still not training data. It has to arrive model-ready: annotated for the reasoning behind each action, synchronized stream by stream, and packaged in the schema your pipeline expects. If your researchers spend weeks writing conversion scripts, the data was never model-ready.
Poseidon supplies qualified demonstration data in two forms: datasets you can evaluate today and custom collection to your spec.
The Datasets
Expert demonstration datasets across egocentric video, manipulation, and voice, collected in real homes, factories, and workplaces. Browse what exists, sample it on Hugging Face, or have us extend it to your spec: most engagements blend both.
Numo
Numo is the collection platform behind Poseidon’s datasets. Anyone can pick up open tasks, capture real-world work, and get paid. The same infrastructure runs private enterprise campaigns end to end: recruiting, device logistics, ingestion, and quality control, whether a task is open to the public or closed.
Poseidon’s Datasets
Real-world training data across voice, video, 3D, and paired text. Collected with consent, licensed for commercial use.
Share the spec: tasks, environments, hardware and streams, diversity distribution, acceptance criteria.
We pressure-test it against what moves model performance, and return a collection plan with QC gates mapped to your criteria.
Approve the plan.
We source demonstrators through our contributor network, workforce partners, and highly skilled factory relationships across Korea, Asia, and beyond, matched to your distribution targets across task, scene, and person.
quality control
Review sample tranches against the acceptance criteria.
Demonstrations captured on validated hardware with synchronized streams, then cleaned, annotated, and quality-controlled clip by clip. Non-qualifying data never reaches you.
Train, and measure the lift.
Qualified data only, rights cleared at the source, in the format your pipeline expects, with a data card documenting distribution, hardware, and rejection criteria.
Vouched for and shipped
Backed by a16z
Datasets delivered to frontier AI teams
Research output and advisor network
Open samples on Hugging Face