How we think about building high-quality data for frontier models, agents, and physical AI.
What alignment data actually is — SFT, RLHF, DPO, red teaming — how the formats differ, and how to specify quality so expert data improves your model instead of polluting it.
A framework for agent evaluation — executable environments, golden trajectories, and failure taxonomies — and the metrics that predict real-world reliability.
Agents
What it takes to produce training data for robots and embodied models — sensor synchronization, teleoperation protocols, episode validation, and delivery formats.
Physical AI
Quality is not a final inspection — it is a system. The framework we use to make data quality measurable, auditable, and steadily improving across every program.
Quality