Durable environments that measure agentic capabilities across long horizons, realistic tools, domain-specific judgment, and partial progress.

Task-faithful, domain-sensitive benchmarks and evaluations designed to measure capability improvements that transfer to real work.

Expert demonstrations captured through bespoke tooling to establish strong behavioral priors while preserving the judgment behind each action.

Realistic tasks spanning hours or days that preserve ambiguity, recovery, and partial-progress signals for training and evaluation.

Prebuilt, quality-reviewed datasets structured to drop into model-training stacks without additional translation work.

Full traces of expert execution—including tool calls, checks, pivots, and recoveries—for teaching agents how real work unfolds.