Somebody has to go out and record it. We do that: egocentric and sensor capture, built for diversity, consent-managed from the first frame, then annotated at the volume your training run actually needs.
Language models had it easy. The world had already written itself down, and somebody scraped it.
Physical AI gets no such gift. Every hour of manipulation footage, every egocentric sequence, every sensor frame your robot learns from had to be recorded on purpose, by a person, in a real place, under conditions you will still have to account for four years from now.
Most physical-AI programs stall right here.
LiDAR frames a month
LiDAR annotations a day
Frames annotated a year
Images annotated
Accuracy standards
Consent-managed & compliant
QA on every pipeline
Operations base across India, the USA and Kenya
Bad labels can be fixed. A shoot you never did cannot. If the lighting, the demographics, the environments and the awkward edge cases weren't in front of the camera, no amount of annotation will put them into your model.
A robot that has only ever seen a kitchen at noon will struggle at seven in the evening. Demographic, environmental and edge-case coverage gets specified before anyone starts recording, because you cannot retrofit a gap.
Somebody stands in a warehouse with a head-mounted rig and performs the same task four hundred times. Getting that consistent takes written instructions, trained contributors, and a reviewer who kills a bad take on the spot, while it can still be redone.
Calibration and timestamp sync get checked before annotation begins. A rig that drifted by two frames produces beautifully labelled data that teaches your model something untrue.
A face recorded this month has to stay accountable for as long as the dataset lives. We keep the record, keep it revocable, and keep it mapped to the exact frames it covers.
Anyone can put an accuracy figure on a website. What a training lead actually wants to know is whether it still holds on batch four hundred, six months in, after three people on the team have changed.
Class definitions, occlusion rules and the awkward edge cases get argued out before labelling starts, while changing them is still cheap.
Three levels on every pipeline, with domain-trained reviewers at each.
Your benchmark runs the whole way through the engagement, so drift shows up in week three and not at delivery.
Ambiguous frames go to a named decider. The ruling goes back into the guideline, so the same argument only happens once.
2D boxes, 3D cuboids, keypoints and segmentation each get scored on their own instrument. One blended number would hide the class that's slipping.
Physical-AI data comes from real people in real places. Somebody agreed to be recorded, and somebody has to be able to prove it years later. We treat that as part of delivery.
Every contributor consents, the record is retained and auditable, and it stays revocable.
Age, gender, region, build and environment balanced to the spec, so the dataset doesn't quietly hand back the bias you hired us to remove.
Contributors and annotators are on our payroll and trained on your protocol. When one program scaled fast, we took 800 trained professionals live in two days.
ISO 42001 for AI management, ISO 27001 for information security, ISO 9001:2015 for quality. Research partners: CeRAI at IIT Madras, and IIT Mandi.
Embodied AI & robotics
A multi-thousand-hour egocentric video collection pilot for a company training vision-language-action models — fielding the collectors, capturing the footage, trimming it to the task and captioning it by hand, as one accountable pipeline.
So the model never learns the wrong lesson from a messy clip.
Autonomous vehicles & ADAS
Eight years labelling perception data for an autonomous-mobility program. Pedestrians, brake lights, curbs, and the strange one-off situations that decide whether the car stops. 97–99% accuracy, held at production volume the whole way.
So an autonomous vehicle recognises a pedestrian at dusk — because it was taught to.
Mapping & geospatial
Map annotation and enrichment across 70 countries, running continuously as roads get built, closed and redrawn underneath it.
So the route on the screen matches the road that's actually there.
Head-mounted and fixed-rig capture of people doing real tasks, run to a written protocol with diversity controls set before the first shoot.
LiDAR, camera, audio and multimodal recording, calibrated and time-synced, in the environments where the data you need has never been collected.
Frame-level annotation, 3D cuboids, keypoints, hand-manipulation and VLA labelling, across robotics, embodied agents, human demonstrations, simulation data and multimodal capture.
Teams trained against your spec and taken live in days, then held at that standard for as long as the program runs.
Human demonstration data, grasp and hand-pose annotation, task-level segmentation.
LiDAR, perception and edge-case labelling at production volume.
Defect, process and workspace data captured on working floors.
Egocentric and in-home capture with consent and demographic controls built in.
Send us a capture spec.
Tell us the environment, the sensor stack and the coverage you need. We'll come back with a capture protocol and a scoped pilot. You'll hear back within two business days.
