Robotics & Physical AI
Raw robot data in. Training-ready data out.
Teleop demos, drone footage, multi-camera rigs — raw robot data is noisy, unaligned, and full of bad takes. We turn it into clean, structured datasets your models can actually learn from.
Where robot data breaks
Models don't fail because the architecture is wrong. They fail because the data is noisy, unaligned, and silently broken.
Teleoperation noise
Bad demos, operator hesitation, dropped objects, desynced cameras, unsafe trajectories. If they enter your training set, your policy degrades — and you can't tell why.
Unstructured failure data
Robots fail constantly. Without a structured failure taxonomy, you can't separate grasp slips from collision events from environment mismatch.
Language–action drift
Auto-labeled VLA instructions look fine in isolation. In practice they drift from the actual action — and nobody catches it until evaluation collapses.
Multimodal across every input
One pipeline across the modalities Physical AI teams actually use.
Robot-onboard video
Egocentric and third-person feeds.
Egocentric human video
Wearable-cam human demonstrations.
Teleoperation
Trajectories, force/torque, demo logs.
LiDAR & depth
Point clouds, RGB-D, multi-view.
Sensor fusion
Synced multi-camera, IMU, force.
Built for the people shipping robots
Different physics. Different failure modes. Different data.
Humanoid
Bimanual manipulation labels. VLA training pairs. Hindsight instruction verification. Operator-quality scoring across teleop sessions.
Warehouse
Grasp slip taxonomy. SKU packaging variation. Bin-picking failures that don't show up in sim.
Field
Off-road traversability. Operator intervention events. Where your sim-trained policy meets real terrain.
Home
Clutter classification. Long-tail object catalog. Safety events around humans.
Not a data factory. A sharp QA layer on your data.
Why teams pick us over building in-house, a big vendor, or cheap offshore labelers.
VS · BOOK-A-DEMO VENDORS
Start free, no contract
A real test batch on your own data before any commitment — no minimums, no procurement cycle. You see our quality and speed first, then decide.
VS · ACCOUNT MANAGERS
Founder in the loop
The person who built award-winning multimodal data systems (CES, EDF Pulse, Kyivstar R&D) is on your task — not a sales rep relaying to an ops team.
VS · COLLECTION ENGINES
We work with your data
No forcing you into our capture pipeline. Bring the teleop, drone, or multi-camera data you already have — we make it clean and training-ready.
VS · IN-HOUSE / OFFSHORE
Right-sized and accountable
A trained annotation team with real QA rigor — without the cost of a six-figure data factory or the churn of managing offshore labelers yourself.
Robot data is computer vision. We already label it.
Labeling robot data is the same craft we run on video and CV every day — multimodal frames, objects, events, trajectories. We're already doing it for robotics and drone teams. Active clients are under NDA, so here it is by domain, not by name.
DRONES · AERIAL
Aircraft and object detection across thermal and RGB aerial video for an autonomous-drone team.
ROBOTICS · MANIPULATION
Teleoperation episode segmentation, action labels and quality review for a robotics team.
SPORTS · BROADCAST CV
Ball and player tracking, polyline and event annotation across live sports video.
TELECOM · STREET SCENES
Semantic segmentation of street-level scenes and infrastructure for a telecom operator.
TEAMS WE'VE SHIPPED DATA FOR
See our annotation case studies →
Multimodal sensor data since 2013 (CES · EDF Pulse · Forbes 30u30). Who's behind WeLabelData →
Free test batch, then a real proposal.
No fixed package, and no blind quote. You run us on a real test batch first — so before you commit, you've already seen our annotation quality, our speed, and exactly what it's like to work with us.
Call → Test batch → Proposal
- We get on a call — your data, your model, and what "good" means to you.
- If it's a fit, you send a representative test batch and your labeling spec.
- Our people annotate it to your spec and surface the corner cases real data always hides.
- We come back with the open questions, align with you, and re-label until it's right.
- We measure real throughput — and where it settles once the team is up to speed — then send a proposal built on those numbers.
What you walk away with
- Annotated test batch (JSON + video overlay)
- Corner-case & failure-mode taxonomy
- Real throughput numbers — measured, not promised
- Project cost & schedule proposal
- Schema recommendation for production scale
Let's talk about your data.
Training VLAs? Running a teleop program? Scaling a data engine? Book a 30-min call — or drop a note and we'll get back to you.