A surgeon knows things no textbook contains. A quant sees patterns no paper describes. That knowledge is the highest-value training signal on earth — and almost none of it has ever been written down. We get it out, and we make models learn it.
Most data companies collect. They find what already exists in a written form, clean it up, and hand it over.
But the knowledge that actually separates a good model from a great one was never written down. It lives in the judgment of people who've spent fifteen years getting good at something — and it doesn't arrive in a format any model can learn from.
So we don't collect. We translate.
We find the practitioners, sit with them, and build the operational machinery that turns what they know into what your model needs. That's a harder problem than annotation. It's also the only one worth solving.
Not by our internal org chart. AI teams think in training objectives, so that's how we've built the menu.
We source from practicing professionals — people with real credentials and real hours — and run structured elicitation to surface the reasoning they can't easily articulate. Then we translate it into training data your researchers can actually use.
Models that act in the world need to know how the world behaves — objects, physics, space, causality. We build multi-view, temporally-grounded datasets for teams training embodied and world-model architectures.
Built by film and TV professionals — directors, cinematographers, editors — who understand what quality means before a model ever sees the frame. Generic vendors can't replicate this, because they don't have the people.
From scoping to first delivery in weeks, not months.