Physical AI starts with
physical data.

ChallengeHigh-diversity first-person video for model pretraining. Strict per-contributor quotas, iPhone-only capture via the client mobile app, two-month window.

DeploymentSourced 30 commercial environments across retail, industrial, agriculture, and automotive. Onboarded 200+ households. 125 hours per commercial contributor, 50 per household, set to hit diversity targets. Field supervisors managed contributors in person to maintain 95% data quality accuracy.

Outcome20,000 hours delivered inside the window. Diversity targets met across environment, demographic, and task.

ChallengeA robotics customer building autonomous warehouse systems needed dense, multimodal capture inside live warehouse environments: synchronized RGB-D, depth, motion, and operator action data for downstream simulation, world-model training, and policy evaluation.

DeploymentDeployed a specialized field team trained on client-provided capture hardware. Multi-rig synchronized recording (RGB-D, depth, IMU, action telemetry) operated across one warehouse site. On-site quality reviewers validated every session for synchronization, completeness, and protocol compliance. Custom format conversion and metadata schemas matched the client simulation stack directly.

Outcome5,000 hours of production-ready capture, fully synchronized, structured to the client sim format, delivered as a continuously growing dataset feeding their world-model training and real-to-sim evaluation loops in 45 days.

ChallengeAn AI tax platform's extraction pipeline produced inconsistent accuracy across software formats, document categories, and form densities. Errors were reaching engineering review faster than they could be corrected.

Deployment20-person dedicated on-site team operating 24/7. Every field reviewed, formats normalized across Lacerte, ProSeries, UltraTax, Drake, errors flagged, and structured datasets delivered back to the engineering team.

OutcomeFull operations and onboarding handled by Human Loops. Engineering team fully freed from manual review. Error rate dropped to near zero across the deployment window.

ChallengeAn AI tax platform needed a production-grade synthetic dataset of US tax returns for model training. Real returns, fully de-identified, with every supporting document a real filing would include.

DeploymentStarted from 400 real 1040s. PII removed across every form and supporting document to produce 400 masked 1040s. For each return, the team generated the full supporting set: W-2s, 1099 INT, 1099 DIV, and all other source documents, plus tax summaries and tax questionnaires. Mixed team of associates and CPAs, with CPA review on every return for format correctness and tax-logic consistency.

OutcomeComplete synthetic dataset delivered in nine weeks. Format-correct, statistically faithful, CPA-verified, ready as direct input to the client's training pipeline.