The best data available, for the models that move AI forward.
Frontier models are only as good as what they learn from. We work with leading specialists in every field to build environments, evaluations, and expert data that are careful, rigorous and honest, and we judge our work by how much it helps models improve.
Why we do this
Better models depend on better data
Models that can reason and work like experts will speed up progress in science, medicine, law, and business. Getting there depends on data that reflects how experts actually think and work. That is the part we have chosen to own.
How we hold ourselves
We would rather make less than make it wrong
One careless task can teach a model the wrong lesson at scale. So every task is written by a specialist, reviewed by multiple experts, and tested against frontier models before anyone trains on it.
What we build
Everything we make helps models learn expert work.
Three kinds of data, one standard: an expert in the field would sign off on every task.
Train
RL environments
Working software, expert-written tasks, and grading, built and tested together. Agents act in the environment and are scored on what actually changed.
Environments →Measure
Evaluations
The same environments run as private benchmarks, with held-out tasks, expert baselines, and analysis of where models fail.
Evaluations →Teach
Expert data
Demonstrations, reasoning traces, and preference data written by people who do the work professionally.
Expert data →Built in any professional domain, with specialists recruited for each one. Meet the experts.
Quality
Quality comes first. Volume follows.
A model learns exactly what its data rewards. We check every task, rubric, and grader until it rewards only the right work, because that is what helps a model actually get better.
Specialists only
Every expert passes an interview, a live work sample and paid trial tasks, and keeps being ranked on the quality of their work.
Multiple experts on every task
Every task is reviewed by more than one specialist before it ships. A flawed task wastes the compute spent training on it, so independent review is what makes every hour of training on our data count.
Rubrics refined against real outputs
Rubrics are tested on actual model answers and revised until independent experts grade the same way.
Graders we try to break
We attack every grader before release. A task ships only when doing nothing, guessing, or gaming it scores zero.
Help us move the frontier forward.
Tell us what your models need to learn. We will send a sample built to our standard, with its grading report and frontier model baselines.