We help teams building world models, VLA systems, robotic foundation models and autonomous systems collect, structure and evaluate real-world experience, without forcing them into a fixed dataset, ontology or annotation stack.
Teams rarely need everything at once. They need one stage covered properly, usually the one their internal capacity does not stretch to this quarter.
Broad, screened, well-described real-world video, so the base model sees enough of the world before anyone talks about task performance.
The supervision that turns general video into task competence: demonstrations, decomposition and action-language grounding.
Sets built to tell an improving policy from a lucky one, including the failures and recoveries training data rarely contains.
Steady-state throughput once the pipeline works: review capacity, taxonomy upkeep, failure mining and QA reporting.
The task families differ, the sensors differ, the failure modes differ. What robotics data annotation has to deliver for each of them does not: structured, physically grounded supervision.
Scene evolution, object persistence and action-conditioned sequences for models learning the dynamics of the world.
Egocentric and multi-view human demonstration data with language grounding, for manipulation and navigation policies that must generalize across tasks.
Hand pose, contact events and grasp state for fine motor tasks where the signal lives in the details.
Picking, packing, sorting and assembly systems needing object interaction data and task completion labeling.
Behaviour classification, scenario coverage and long-tail event curation at production volume.
Physics-plausibility review, domain-gap analysis and real-to-sim validation sets for teams generating data in simulation.
Structured, well-documented demonstration data with a defined ontology and reproducible acceptance criteria.
Perception architecture review, data pipeline design and vendor evaluation support, grounded in deployment experience.
Explore AI Consulting →We extend internal teams with targeted collection, specialist annotation, curation and QA capacity. You do not have to hand over the whole data operation to start.
You hold the footage, robot logs or teleoperation sessions. We add the supervision layer and the review pass.
A written collection protocol around a task family or environment your current corpus does not cover.
Success, failure, partial completion and recovery labels built to a rubric, so evaluation reflects deployment rather than training distribution.
Ongoing throughput with taxonomy maintenance, contributor management and QA reporting against agreed thresholds.
The stream your internal team keeps deprioritising because it is slow, ambiguous or needs specialist judgment.
Depending on where you are, training a model or running automation on a live floor, you want one of two Biz-Tech teams.
Looking for the technical breakdown, including keypoint schemas, the three-tier VLA structure and the annotation platform itself?
See Physical AI Data Services →We will scope a focused pilot or data workflow around that gap, with the output schema, quality thresholds and acceptance criteria agreed before production begins.