We’re making health and life-science AI truly accurate and safe. Our team has delivered evaluation and data projects for OpenAI, Google, and Meta. Now we focus on one thing — making AI work in health — in partnership with foundational labs, fast-growing startups, and governments around the world.
SFT Pairs
Supervised Fine-Tuning
High-quality prompt-response examples that teach models how to behave. Your models learn correct patterns, structure, tone, and task-specific reasoning from expert-curated conversations.
RL/HF
User Queries and Feedback
Human-in-the-loop feedback that aligns models with real user expectations. Our medical experts rate, correct, and guide model outputs, helping your systems improve with every interaction.
Preference Ranking
Side-by-side comparisons of model responses that reveal which outputs humans truly prefer. These rankings train models to consistently choose the most helpful, accurate, and safe answer.
RLAIF
Reinforcement Learning from AI Feedback
Large-scale rubrics scores by AI evaluators. A scalable way to shape model behavior—combining human judgments with high-coverage automated feedback.
Health RL
Reinforcement Learning
Simulation-driven learning loops where models improve through trial and error. Ideal for agents, decision-making systems, and workflow automation across complex environments.
Traces
Detailed recordings of user actions—every click, step, and decision. These trajectories teach agents to navigate software, operate tools, and perform tasks the way real humans do.
See how we can help you train and evaluate your models.