Define the capability
Align on target behaviors, task difficulty, and success criteria.
Products
High-quality data,
at scale.
RL environments, expert training data, and evaluations for AI labs. Built around the work you want your models to do.
Get DataRL environments
Give agents a world to work in. Stateful environments bring together real codebases, tools, and multi-step workflows, with repeatable starting conditions and outcomes you can verify.
- Isolated runtimes
- Long-horizon tasks
- State-based verification
Training datasets
Tasks, demonstrations, and agent trajectories for RL and SFT. We combine domain expertise with scalable production and quality checks, shaping each dataset around the capability you want to develop.
- Expert-designed tasks
- Reference solutions
- Agent trajectories
Rubrics & verifiers
Turn success into a useful learning signal. Programmatic checks test what can be measured directly; expert rubrics capture the judgment required for scientific and professional work.
- Hidden tests
- Expert rubrics
- Task-specific rewards
Benchmarks & evaluations
Measure how agents perform as tasks unfold. Build evaluation suites around realistic workflows, inspect failures, and use the findings to guide your next training run.
- Custom evaluation suites
- Reproducible tasks
- Failure analysis
Expertise where
it matters.
Work with domain experts to define realistic tasks, reference solutions, and quality standards across the domains your models need to master.
Long-horizon coding
Software engineering across evolving requirements.
AI4Science
Scientific tasks shaped by domain experts.
AI4AI
Model research and recursive self-improvement.
Knowledge work
Expert-led analysis and professional workflows.
Agentic tool use
Stateful work across APIs, SaaS tools, and software.
From a research question to a training run.
Build and verify
Combine expert review with reproducible checks as production scales.
Deliver and iterate
Fit your training workflow and refine the data around what your model learns.