Turn raw sensor logs into training-ready ground truth.
3D and sensor-fusion annotation for robotics and autonomy — including annotation of physical/sensor data you (or your field partners) already collected.
A spatial-annotation service for embodied AI: 3D bounding boxes, point-cloud segmentation, 6-DoF pose, frame-accurate event labeling and sensor-fusion review — on calibrated tooling, held to a measured quality bar. Send us your own captures, your field partners' data, or data we collected for you.
- Modalities
- Point cloud (LIDAR), Stereo & RGB-D Video, Radar, Event-camera streams, IMU, Multi-sensor fusion, Teleoperation logs
- Languages
- Specialist 3D/spatial annotators · India-resident review option
- Quality bar
- IAA ≥ 0.85 · two-pass QA
- Onboarding
- NDA → SOW in 14 days
Spatial annotation is where physical-AI programs stall. 3D bounding boxes, point-cloud segmentation, 6-DoF pose and frame-accurate event labeling are specialist tasks that demand trained annotators, calibrated multi-sensor tooling, and disciplined two-pass QA — not a generic crowd.
And the data rarely arrives clean from one place. You may have captures from your own rigs, sensor logs from a field-collection partner, and open or purchased datasets — all in different formats, with different calibration and consent metadata that has to be reconciled before a single label is trustworthy.
We annotate physical and sensor data regardless of who collected it — your own rigs, data captured by your field or collection partners, or data we collected for you — and return sensor-fusion ground truth with measured inter-annotator agreement, full audit logs and per-batch quality reports. One consistent labeling standard across every source you train on.
Where teams put this to work
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Any source
Annotate partner- or field-collected sensor data
Bring us data captured by your field-collection partners or third-party fleets — we reconcile calibration and consent metadata and label it to one consistent standard, without you having to name or expose the source.
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Autonomy
3D object detection & point-cloud segmentation
3D bounding boxes, semantic and instance segmentation, and object tracking across frames on LIDAR and fused camera-LIDAR scenes.
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Robotics
6-DoF pose & grasp labeling for manipulation
Object pose, grasp points and contact events for manipulation and imitation-learning pipelines, with sensor-fusion review.
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Safety
Frame-accurate event & safety-incident labeling
Time-aligned event, action and safety-incident labels across the long tail — the rare moments where perception and policy models actually fail.
A pipeline built for SLAs
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1
Guidelines
Co-write annotation guidelines with the client team and pilot reviewers.
Calibrated rubric
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2
Pilot
Run a 500-unit pilot to validate guidelines and tooling.
IAA ≥ 0.80
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3
Production
Scale to full volume with daily QA sampling and weekly calibration.
IAA ≥ 0.85
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4
Delivery
Versioned drops with audit logs, kappa reports, and dataset cards.
Audit-ready
Quality gates
- Inter-annotator agreement (IAA) ≥ 0.85 on spatial tasks
- Two-pass QA with adjudication on calibrated, sensor-aware tooling
- Cross-source calibration & consent metadata reconciled before labeling
- Per-batch quality reports and audit logs on every delivery
- India-resident annotation & review option for sensitive captures
At a glance
What does Physical AI Data Annotation cover?
The technical spec — sensor modalities, coverage, deliverable formats and the QA stages every batch passes through.
| Specification | Detail |
|---|---|
| Modalities / sensors | Point cloud (LIDAR), Stereo & RGB-D Video, Radar, Event-camera streams, IMU, Multi-sensor fusion, Teleoperation logs |
| Coverage | Specialist 3D/spatial annotators · India-resident review option |
| Deliverable formats | 3D boxes / segmentation / tracking labels (JSON), fused-scene annotations, per-batch QA report |
| QA stages | Guideline calibration → two-pass spatial labelling → adjudication → IAA reporting per batch |
Representative spec. Exact sensors, formats and acceptance thresholds are scoped per project in the SOW.
What's included
Spatial annotation across every sensor source
Specialist 3D and sensor-fusion labeling on calibrated tooling — for data you collected, your field partners collected, or we collected.
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3D boxes & segmentation
3D bounding boxes, semantic and instance segmentation, and object tracking across frames on point-cloud and fused scenes.
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6-DoF pose & grasp
Object pose, grasp points and contact events for manipulation and imitation-learning pipelines.
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Frame-accurate events
Time-aligned action, event and safety-incident labels across the long tail where models actually fail.
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Sensor-fusion review
Cross-sensor consistency checks on camera-LIDAR-IMU data so labels hold up under fusion, not just per-frame.
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Bring your own data
We label physical and sensor data regardless of who collected it — your rigs, your field partners' captures, or open/purchased sets.
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India-resident review
Optional in-country annotation and review for sensitive captures and regulated buyers.
/sovereign-data/
How it works
From mixed-source logs to one consistent ground truth
We reconcile sources, calibrate the rubric, label with specialists, and QA every batch.
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1
Ingest & reconcile sources
Normalize formats and reconcile calibration and consent metadata across your data and any partner-collected data.
Sources reconciled
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2
Guidelines & calibration
Co-write the labeling rubric and run a pilot with specialist 3D annotators on calibrated tooling.
Pilot IAA ≥ 0.80
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3
Production labeling
Scale to volume with two-pass QA, adjudication and daily quality sampling across all sources.
IAA ≥ 0.85
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4
Delivery & audit logs
Versioned sensor-fusion ground truth with per-batch quality reports and full audit trails.
Audit-ready
Trust & compliance
One labeling standard you can stand behind
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Measured quality
Inter-annotator agreement, QA pass rates and adjudication reported per batch — not asserted, measured.
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Partner-agnostic intake
We annotate data collected by your field partners without you having to name or expose the source — your confidentiality stays intact.
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DPDP-aligned handling
De-identification and India-resident review options for personal data in captures, with documented chain-of-custody.
FAQ
Physical AI data annotation — common questions
- Can you annotate data we (or our partners) already collected?
Yes. We annotate physical and sensor data regardless of who collected it — your own rigs, data captured by your field or collection partners, or open and purchased datasets. We reconcile calibration and consent metadata and label everything to one consistent standard, and you never have to name or expose the source.
- Which annotation types do you support?
3D bounding boxes, point-cloud semantic and instance segmentation, object tracking, 6-DoF pose and grasp labeling, frame-accurate event and safety-incident labeling, and sensor-fusion review across camera-LIDAR-IMU data.
- What quality bar do you hold?
Two-pass QA with adjudication and inter-annotator agreement (IAA) ≥ 0.85 on spatial tasks, on calibrated, sensor-aware tooling, with per-batch quality reports and audit logs.
- Can sensitive captures be handled in India?
Yes. We offer India-resident annotation and review for sensitive data, with de-identification SOPs and documented chain-of-custody. See our Sovereign Data page.
- We still need the data captured — can you do that too?
Yes — that's our Physical AI Data Collection service. We run multi-sensor field capture end to end and can then annotate it on this service.
Have sensor data that needs ground truth?
Send us a sample — your captures or your partners' — and we'll scope a labeling rubric, quality bar and delivery schedule.
Ready to scope a pilot?
A senior Language PM will scope your project and respond within one business day.
Talk to a Language PM