Intelligence isn’t built in a lecture hall. It’s built through practice, feedback, and failure. It’s built through experience.
Our products encode that full spectrum: expert demonstrations, preference signals, adversarial environments, and the reinforcement loops that turn a capable model into a reliable one.
As the frontier of what models can do expands, what they need to learn from expands with it.
Combines expert-crafted rubrics with automated verifiers and Groq-accelerated evaluators that grade model outputs the way a seasoned professional would, rewarding nuance and penalizing shortcuts across reasoning, code generation, and instruction-following tasks.
Provides custom RL environments built on top of real APIs, MCP servers, and developer tools, enabling models to learn how to call, chain, and recover from errors across complex service workflows with automated evaluation at every step.
Delivers high-quality prompt-response pairs, verified chain-of-thought traces, and expert demonstrations through supervised fine-tuning, giving models a rock-solid foundation of skills before reinforcement learning begins, teaching them to reason, follow instructions, and navigate professional tasks from the ground up.
Pairs high-fidelity browser and desktop operating environments with expert-demonstrated action trajectories, teaching autonomous agents to navigate web interfaces, execute complex multi-step workflows, and operate software tools exactly the way a domain expert would.
Captures the subtleties of what makes one response genuinely better than another through RL from human feedback (RLHF) and Direct Preference Optimization (DPO), training models to internalize the taste, judgment, and safety standards of domain experts across thousands of double-blind comparison pairs.
Spans expert-written code, comprehensive unit test suites, and debugging traces that teach models to write production-quality software, handle complex edge cases, and reason through architectural tradeoffs the way senior principal engineers do.
Draws on 100,000+ verified practitioners across medicine, law, quantitative finance, cybersecurity, and engineering, capturing the tacit knowledge, clinical intuition, and real-world judgment that textbooks leave out and synthetic data cannot replicate.
Covers long-horizon investigative research tasks, teaching models to gather evidence across heterogeneous sources, cross-examine conflicting data, synthesize structured findings, and produce thorough analyses that mirror how skilled research scientists build understanding over hours of investigation.
Identifies where and why models fail in professional contexts through systematic error analysis, pinpointing the precise failure modes, hallucination triggers, and distributional gaps that directly inform how your next dataset and RL environment should be engineered.
Teaches models to perceive, interpret, and reason across high-resolution image, video, audio, and structured document modalities together, closing the perceptual gap between how humans experience the physical world and how AI systems process it.
Offers ready-to-deploy, pre-validated datasets across high-demand capability areas, giving AI labs and frontier enterprises immediate access to rigorously validated training data without the lead time of custom engagements.
Tailors evaluation suites and training datasets to your specific capability targets, designing every prompt, verification rubric, and environment from scratch to address the exact reasoning and safety gaps your models need to close.