Tidel builds frontier training data for AI labs — verified coding & agent tasks, real-robot embodied data, and engine-native world-model capture.
Not scraped. Not resold. Not passed through.
Sourced, constructed, and verified — task by task, frame by frame.
Difficulty measured on our own rollouts, before anything ships.
Everything we deliver is manufactured, not aggregated: we source the raw material, design the tasks and scenarios, put domain experts in the loop where automation falls short, and measure every batch against current frontier models before it leaves the floor.
Agentic coding data across the full difficulty spectrum — from repository-scale software engineering to long-horizon problems frontier agents still fail. Every task ships as a reproducible environment with hidden tests and a measured pass rate. Built on three principles: frontier benchmark alignment, expert in the loop, and scenario diversity.
Embodied data collected on real hardware — teleoperation on dual-arm and humanoid platforms, tactile-glove motion capture, and egocentric vision — synchronized across cameras, depth and pose, with skill labels down to the frame.
4D capture from AAA game engines with tick-aligned world state at 60 Hz, paired with human visual-reasoning chains — nothing estimated, everything read from the engine, counterfactual by construction.
Private, license-cleared codebases. Robot fleets we operate. Game engines we instrument. Provenance is the first quality gate.
Reproducible environments, teleoperation protocols and capture rigs — every scenario built to a spec, with experts in the loop where automation falls short.
Hidden test suites, reference solutions, multi-stage human and automated QC — success is defined programmatically before anything is labeled done.
We run frontier models against every batch on our own infrastructure, and ship measured pass rates — not asserted difficulty.
The same discipline across all three programs — in robotics and world models, ground truth is measured from hardware and engines, never estimated. See how we approach coding data →
Coding tasks ship as reproducible containerized environments with hidden tests and a verifier — you can re-run everything we claim.
Frontier-model rollouts on our own infrastructure put an empirical pass rate on every datasheet, so you know exactly where your model will struggle.
Tasks are screened against public benchmarks and repositories; for evaluation and RL we source private, never-published material with cleared licenses.
Where automated generation or grading is insufficient — research-grade problems, ambiguous specs, physical skills — domain experts author, review and arbitrate.
We track frontier benchmarks continuously and evolve our task designs as evaluation paradigms move — so the data trains toward where the frontier is going.
Representative task packages and datasheets come first, QC acceptance targets are contractual, and billing is on qualified data only.
Frontier lab, applied team, or data partner — tell us what you're training and we'll come back with relevant samples, datasheets, and scoping.