Build evals that hold up in the real world.

The evaluation gap

Your policies can act. But evaluating whether a physical task was completed still comes down to brittle hand-coded checks, simulators that don't match reality, or zero-shot prompts to general models that have never seen the real world.

That gap slows every training loop you run.

Use our data to

• Detect success

  Train success detectors on real-world outcome labels.

• Shape rewards

  Turn real-world success and failure into reward signals for RL and post-training.

• Benchmark policies

  Measure against real-world task completion, not lab proxies.

Why the evaluations hold up

Most physical-AI data comes from labs and simulators, where conditions are narrow and controlled. Our data comes from real work: real tasks, real conditions, and real failures. It spans the trades and workplaces no dataset covers. Every task is a structured record of what was done and whether it worked.

Our data is grounded in the task, not one robot's hardware. So it works across embodiments: your stack, your tasks, your robots. That is what makes the evaluations hold up in the real world.

Over a million real-world tasks, and growing. Built for robotics, humanoid, and embodied-AI teams.