Frontier AI was trained on the internet. But the internet has no record of how a machine gets serviced, an order gets picked, or a fibre line gets spliced. We do.
The physical operations enterprises run on — and that AI and robotics teams have almost no data for. Select one to see the trajectory, the captured signals, and why it’s hard to model.
The data isn’t collected after the fact — it’s a byproduct of operating. Click a stage.
Anyone can bundle tools; almost no one owns the loop. We own both the agents and the network they book, so every task runs inside our system — and stays there. That closed loop is the moat; the data engine beneath it is what Jobbit Labs is built on.
No public record exists of how real-world tasks are actually performed — step by step, with context, corrections and verified outcomes. We produce exactly that, first-party.
World models — AI that predicts the outcome of actions in the physical world — are widely seen as the next step after language models. The constraint isn’t compute. It’s data.
A Joint-Embedding Predictive Architecture (JEPA) doesn’t predict the next video frame pixel-by-pixel — that’s wasteful and unreliable. It predicts the next state in an abstract latent space: encode the situation, predict where the action leads, and learn by matching prediction to reality.
Our recorded trajectories are exactly that — physical states, and the actions between them. In the demo, you set how much real data the model has seen. Watch its prediction lock onto reality as the data grows. That data is the product.
A robot arm sets a part down.
Can the model predict where it lands, before it moves?
We’re early, deliberately so — but the engine isn’t a slide. The orchestration layer and capture loop run live on Jobbit today; the research builds directly on top.
Routes every Jobbit task to the right tool, agent or vetted human — and labels each completed task for training.
Instructions, photos, video, actions, uncertainty and outcomes — recorded by default, structured into clean task records.
World models that learn physical cause-and-effect from trajectories, tested against ground-truth outcomes no synthetic benchmark can match.
The bottleneck is data on how real work gets done — and the models trained on it. We produce both. AI labs, robotics teams and research groups: let’s talk.