Physical AI
The data layer for AI that moves through the physical world — multi-sensor, spatial and teleoperation data for embodied models, rights-cleared and India-resident where it matters, so your robots generalise beyond the lab.
The bottleneck
Your models aren't data-starved — they're reality-starved
Embodied AI teams rarely lose on model architecture. They lose on data: not enough real-world, multi-sensor, correctly labelled examples of the messy situations a robot actually meets on a factory floor, a warehouse aisle, a farm or a home.
The result is a familiar set of blockers:
- The sim-to-real gap — policies that work in simulation stall on real sensors, real lighting and real physics.
- Long-tail edge cases — the rare failures that matter most are the hardest to capture at volume.
- Teleoperation and capture cost — building rigs, recruiting operators and running sessions burns scarce engineering time.
- Rights and provenance risk — data of unknown origin or consent is a liability you can't ship a product on.
What good looks like
The data foundation embodied AI teams actually want
Before you evaluate a vendor, get clear on the outcome. These are the four properties that separate data you can train a shipping product on from data that quietly poisons your model.
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Generalises beyond one lab
Captured in the real, varied environments your robots will operate in — not a single rig in a single room — so policies transfer instead of overfitting.
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Measurably correct
Every batch carries explicit acceptance gates — inter-annotator agreement, geometric tolerances and event timing — so quality is a number you can audit, not a promise.
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Rights-cleared and auditable
Documented consent and provenance for every clip, with an India-residency option for sensitive captures — the paper trail your legal and compliance teams require.
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Delivered to spec, on time
Modalities, formats, taxonomy and volume agreed up front and delivered against a schedule — so data stops being the thing that slips your roadmap.
How we resolve it
Two disciplines, delivered end to end
Physical AI data isn't a moonshot — it's two well-understood disciplines applied to sensor, teleoperation and environment data. We run either on its own, or both as one pipeline.
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Physical AI Data Collection
Synchronised multi-sensor field capture — RGB-D, LIDAR, IMU, force-torque and teleoperation — across the real environments where your systems will run, with targeted edge-case sessions.
Explore collection -
Physical AI Data Annotation
3D bounding boxes, point-cloud segmentation, 6-DoF pose and frame-accurate event labelling on data you've collected — to one consistent, documented standard.
Explore annotation
Built for scale and trust
What we bring to a physical-AI programme
- Global South markets for in-region capture
- 7
- Synchronised sensor modalities
- 5+
- Consented, rights-cleared capture
- 100%
- Quality measured, not asserted
- IAA-gated
Global South markets for in-region capture
India, Malaysia, Brazil, Mexico, Uruguay, South Africa, Moldova/Georgia
Synchronised sensor modalities
RGB-D, LIDAR, IMU, force-torque, teleoperation
Consented, rights-cleared capture
Documented provenance on every clip
Quality measured, not asserted
Acceptance thresholds agreed per project
Where it applies
Embodied AI across real-world domains
The same collection-and-annotation pipeline adapts to the domain, the sensors and the tasks that matter to your robots.
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Humanoids
Human-demonstration priors, egocentric video and teleoperation traces for humanoid manipulation and locomotion policies.
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Industrial & warehouse
Manipulation, navigation and safety-event data from real factory and warehouse tasks, including targeted edge cases.
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Autonomy
Sensor-fusion capture and long-tail edge cases for autonomous ground and aerial systems.
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Fleet triage & edge-case mining
Surface, curate and re-label the rare failures from your existing fleet logs so each iteration targets what's actually breaking.
How an engagement runs
From spec to shippable data in one accountable pipeline
Every engagement is scoped, gated and documented — so you always know what you're getting, at what quality bar, and when.
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1
Scope & spec
We agree environments, sensors, tasks, taxonomy, formats, volume and the quality bar — in writing, before any capture.
Signed data spec & acceptance criteria
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2
Consented capture
In-region contributors and teleoperation sessions collect synchronised multi-sensor data under documented consent.
Provenance & consent logged per clip
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3
Annotate to standard
3D boxes, point-cloud segmentation, 6-DoF pose and frame-accurate events, labelled to one consistent guideline.
IAA ≥ agreed threshold
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4
QA & deliver
Human-in-the-loop review and automated checks validate every batch before delivery in your target format.
Batch acceptance sign-off
Global South reach
A consented contributor network across the Global South
Physical AI needs data from the environments where machines will actually operate. We recruit and manage native-speaker, in-region contributors across seven Global South markets — every clip consented, rights-cleared and quality-gated — so your embodied models generalise beyond a single geography.
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South Asia
India-resident capture and annotation across 400 languages, with code-mixed and low-resource depth.
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Southeast Asia
Malaysia-anchored network for multilingual, multi-accent collection in real home and warehouse settings.
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Latin America
Brazil, Mexico and Uruguay contributor pools for Portuguese, Spanish and regional-accent data.
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Africa & Eastern Europe
South Africa, Moldova and Georgia reach for under-served languages and diverse operating environments.
Before you brief us
Physical AI data — the questions buyers ask
- Which sensor modalities can you capture and annotate?
Synchronised RGB-D, LIDAR, IMU, force-torque and teleoperation streams on the collection side, and 3D bounding boxes, point-cloud segmentation, 6-DoF pose and frame-accurate events on the annotation side. Tell us your stack and we map to it.
- Can you annotate data we've already collected?
Yes. Physical AI Data Annotation is offered on its own — send us your captures and we label them to one consistent, documented standard.
- Who owns the data and the IP?
You do. Collection is delivered rights-cleared with documented consent and provenance, and annotation output is your IP. We can work under NDA and exclusivity terms scoped to your programme.
- How do you guarantee quality?
Quality is measured, not asserted. Every batch carries acceptance gates — inter-annotator agreement, geometric tolerances and event-timing checks — agreed with you up front and validated by human-in-the-loop review before delivery.
- Can sensitive captures stay in India?
Yes. Both collection and annotation offer an India-residency option so sensitive data is captured, processed and stored within India. See Sovereign Data.
Building AI that acts in the physical world?
Tell us the environments, sensors and tasks — we'll scope a consented collection or annotation pilot, India-resident where it matters, with the quality bar agreed before we start.