Fine Tuning datasets
Domain-grounded demonstrations for instruction tuning, with expert methods, complete context, and gold responses.
Build AI that understandsthe work
We build expert-grounded data, evaluation, and agentic systems for production workflows where context, judgment, and governed action matter.
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From training data to deployed systems.
Each engagement can stop at a validated artifact or continue into a managed system. Every handoff preserves sources, rubric decisions, and expert review.
Bespoke tasks and datasets
Ranked alternatives with explicit reasons for correctness, factuality, tone, and useful tool use.
RL environments
Versioned tasks, tools, states, rewards, and failure cases for agents that learn inside real workflows.
Evals and Benchmarks
Held-out cases and scoring rubrics that test reasoning, method, safety, and downstream outcomes.
Agentic AI systems
Production agents grounded in source systems, evaluation gates, permissions, and reviewable traces.
Built for production impact.
Backed by trajectories, expert review, and release evidence that makes progress visible from data collection through agent behavior.
- Coding dataset50k+Real engineering tasks with 833k trajectory steps, 29k reward signals, and 382k tool interactions.
- Healthcare dataset1M+Clinical records spanning prescriptions, diagnostics, radiology, and pathology for high-context healthcare models.
- Expert network2M+Domain specialists and engineers across healthcare, computer science, electrical, mechanical, civil, and chemical disciplines.
Agents that work through the workflow
Agents observe incoming work, reason across live context, and act through connected tools while every step stays visible, reviewable, and accountable.
Read the incoming work
Agents take in live tasks, traces, context, and constraints before deciding what needs to happen next.
Use tools and take action
They plan across connected tools, carry state forward, and turn each observation into a useful operational step.
Keep every run governed
Every action stays observable through reviewable traces, escalation paths, and human approval where risk demands it.