Robotics & Physical AI
Raw teleop in. VLA-ready data out.
Teleop demos with operator hesitation. Egocentric video with desynced cameras. Auto-generated VLA instructions that drift from the action. We catch this before it hits your training set.
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.
From raw teleop to VLA-ready dataset
Three stages. Same time axis. Independently verifiable at each step.
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.
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
Multimodal video and CV — what we ship every day
AI teams across video, sports analytics, telecom, and surveillance. The same multimodal QA rigor — now for robot data.
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.