Scope your dataset
Validated on a public benchmark 99% of expert demonstrations passed. Read the report

Physical AI data infrastructure · South Korea

Verified data for physical AI.

Collection, teleoperation, annotation and evaluation for robotics and embodied AI.
Every modality, every embodiment, every episode verified before it reaches your pipeline.

Scope your dataset Watch a sample episode
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Our verification standard

  • Every episode reviewed twice.

    Independent reviewers, agreement reported per batch.

  • Every stream time-aligned.

    Measured offsets per episode, not assumed.

  • Every rejection reported.

    Reason codes per episode. On a 30,000-episode benchmark, 0 clean episodes were wrongly rejected.

  • Every episode rights-cleared.

    Consent and provenance attached to the file.

  • Every threshold published.

    20 thresholds in one config file. 100% recall on 700 injected defect types. Re-run our QC yourself.

Solutions

End to end, or any single stage.

Plug into one stage of your pipeline or hand us the whole loop. The verification standard is the same either way.

  • Field of blue 3D columns

    Data collection

    Egocentric, exocentric and multi-sensor capture in real environments, on-site or in our facilities.

    • Egocentric & handheld (UMI)
    • Multi-camera, depth, LiDAR, IMU
    • Scripted and in-the-wild protocols
  • 3D render of robotic joints wrapped in blue ribbons

    Teleoperation

    Certified operators for demonstration collection and live intervention, on your embodiment or ours.

    • Arms, bimanual, humanoid, mobile
    • Leader–follower, VR, custom interfaces
    • Latency and reset events logged
  • 3D relief map built from layered blocks

    Annotation & curation

    Language, temporal, 2D/3D and sensor-fusion labels on your corpus or ours, in your schema.

    • Stage-level language annotation
    • Segmentation, tracking, 3D cuboids
    • Deduplication and curation
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    Evaluation

    Human judgement of policy rollouts, success and failure criteria, and benchmark runs to your rubric.

    • Success / failure / retry verdicts
    • Failure-cause taxonomies
    • Blind A/B of policies

Coverage

Every modality. Every embodiment.
Every environment.

Modalities

RGB · stereo · depth
video
LiDAR · radar
point cloud
IMU · proprioception
time series
Force · torque · tactile
contact
Audio · language
multimodal

Embodiments

Single and bimanual arms
manipulation
Humanoids
whole-body
Mobile manipulators · AMRs
navigation
Quadrupeds · drones
locomotion
Human demonstrators
egocentric

Environments

Manufacturing · assembly
industrial
Warehouse · logistics
operations
Retail · hospitality · food
service
Healthcare · laboratory
regulated
Home · agriculture · construction
field

Environments

Collected where the work happens.

  • 3D render of a kitchen appliance on a turntable

    Food & hospitality

    Bimanual manipulation, tool use

  • Truck driving along a curved elevated road

    Logistics

    Pick, pack, palletize

  • Close-up of interlocking metal gears

    Manufacturing

    Assembly, inspection

  • Folds of blue fabric

    Textile

    Deformable objects

  • 3D retail scene with a phone, shopping cart, bags and a card terminal

    Retail

    Restocking, scanning

  • Abstract 3D shapes with spheres on a ramp

    Workshop

    Power tools, measurement

  • Stylized columns of green plants

    Agriculture

    Sorting, harvesting

  • Minimal 3D architectural space in soft light

    Home

    Laundry, tidying, cleaning

Volume is easy.
Verified is not.

Unlabelled footage is a commodity. Every hour we deliver carries a language label, two reviewers' verdicts, a measured sync report and a consent reference.

  • 1.9 s 3,000 episodes, 800,000 frames QC’d on one laptop
  • 100% recall on 700 injected defect types
  • 99% of expert demonstrations pass
  • 0 false rejections on clean data

robomimic public benchmark, Sept 2026 · Full report

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Delivery

Your schema. Your bucket.
Your pipeline.

Robot learning formats
LeRobot v3 · RLDS · HDF5 · Zarr
Robotics logs
ROS 2 bag · MCAP · Parquet
Annotation formats
COCO · JSONL · KITTI · custom
Synchronization
≤ 1 frame skew · IMU ±20 ms · reported
Per-episode metadata
Scene, task, embodiment, rig, consent ID, QC verdict
Transfer
S3 · GCS · Azure · Hugging Face · encrypted disk

Sample

One episode, end to end.

Raw capture to training file, including what the reviewer changed.

Floating blue cubes
raw_stereo_L/R.mp4
Synchronized capture
episode.parquet
State & action, LeRobot v3
language_annotations.jsonl
Stage-level, frame ranges
sync_report.txt
Measured offset per frame
qc_verdict.json
Two reviewers, reason codes
consent_reference.txt
Redacted consent ID
review_notes.md
What changed and why
Request a full sample

Security & governance

Built for a vendor security review.

  • Access-controlled production floors. No home devices, no open crowd.
  • Managed workstations, no removable media, per-client network segments.
  • NDA and background check for every annotator and operator.
  • Written consent per demonstrator and site; provenance attached per episode.
  • Client data is never used to train anything of ours.
  • ISO/IEC 27001 and SOC 2 programs in progress.
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Engagement

  1. 01 · SCOPE

    Task, embodiment, format

    We return a protocol draft and per-layer pricing.

  2. 02 · EVALUATE

    Evaluation batch

    A graded batch on your protocol, no obligation.

  3. 03 · PRODUCE

    Production

    Dedicated team, weekly batch reports.

  4. 04 · DELIVER

    Delivery & audit

    Your bucket, your schema, full QC trail.