Real-world human data that robot foundation models and world models train on.

Look at the data.

Every clip below was captured in a real working environment. Nothing is staged and nothing is synthetic.

We customize the pipeline for every data challenge.

Each stage uses the best method available — open source where open source is better, our own where it is not. We publish research in this field, so we know the difference, and we swap components when something better comes out. You tell us what your model needs to learn, and we build the pipeline for it.

An exocentric view with real 3D geometry.

One shot from Alia is all of this. A single 360° stereo capture produces a 16K RGB panorama of the entire room and metric depth for every pixel — no scanning rig, no multi-camera stitching, no post-processed geometry. Below, both outputs from the same capture.

The same instant, two ways: 16K color across the full sphere and metric depth for every pixel. Only possible with Alia.

Capacity and access, already built.

Getting a capture team into a working pharmacy, an auto plant, or a hospital ward takes agreements, training, and compliance work. We have already done that work.

Examples of where we capture

We publish what we learn.

We test our capture and annotation methods on public models and publish the results. Four papers so far, alongside our granted patents and the earlier computer vision work our team brought to the company.

Measured on public foundation and robot models. Published, with reproducible results.

Common questions.