Reasoning
Verifiable problems, solution traces, proofs, and scored outcomes.
Off-the-shelf datasets for training, evaluation, and RLVR – expert-built, quality-controlled, and ready for your stack.
Each collection carries the task, context, response, outcome, and metadata your models need.
Verifiable problems, solution traces, proofs, and scored outcomes.
Repository tasks, patches, tests, execution traces, and reviewer signals.
Web tasks, page state, actions, trajectories, and verified outcomes.
Cross-application workflows with screens, files, actions, and end states.
Tool schemas, calls, multi-system workflows, and expected state changes.
Demonstrations, sensor streams, actions, failures, and recovery episodes.
Use a single stream – or combine synchronized signals into one training-ready collection.
Every delivery is structured to be inspected, compared, versioned, and used again.
provenance · rights
schema · metadata
experts · automation
coverage · agreement
splits · versions
Begin with the fastest path, then add the proprietary coverage that creates advantage.
Deploy an existing collection in your preferred schema and storage.
Extend a proven dataset with your domains, policies, and edge cases.
Turn failures and new requirements into recurring dataset releases.