Our mission.

We build RL environments for superhuman reasoning over time, for the frontier labs that train agents. Each comes from real, de-identified records (health data, industrial sensors, compute-cluster logs), in three families: personal agents, industry, and science and compute. The world keeps moving while the agent works, so the agent must keep learning: data arrive on a clock, and every answer is checked against what actually happened. In each family, the best frontier model averages below half, and models that train on our environments improve.

We perceive the real world as numbers that change in time. We specialise in time series because almost everything that matters is recorded that way: a heartbeat, a power grid, a factory line, a GPU cluster, an experiment.

No person can turn thousands of moving signals at once into the state of a system and what happens next. AI can pass the bar exam, but it still can't watch a system for weeks and act at the right moment. It learned from what people wrote down, in environments that freeze the world and abstract it to our understanding. That gave it human knowledge, and a human ceiling.

The work beyond that ceiling has no human examples to copy and no human grader to please. A model can learn it only from experience: it acts while the world changes, and the world tells it what actually happened.

Time series let us ride the exponential. The world records new data every second and time keeps grading, so each generation of models gets harder, fresher tasks. Time is a grader that never tires and cannot be persuaded, and it sets no ceiling at human level.

Our mission is to build the infrastructure that keeps AI climbing the frontier, long past human level in the real world.