Data Infrastructure for Physical AI

Building the data layer
for embodied intelligence.

We capture and structure multi-modal real-world interaction data — synchronized video, depth, pose, language, and sensor streams — for robotics, embodied agents, and world models.

0 hrs Captured Data
0 M+ Interactions
0 x Modalities
0 TB+ Structured Data
PHYSICAL AI EMBODIED DATA WORLD MODELS ROBOTICS MULTI-MODAL 4D CAPTURE DEPTH SENSING HAND POSE SEMANTIC ANNOTATION FOUNDATION MODELS PHYSICAL AI EMBODIED DATA WORLD MODELS ROBOTICS MULTI-MODAL 4D CAPTURE DEPTH SENSING HAND POSE SEMANTIC ANNOTATION FOUNDATION MODELS

USED BY RESEARCHERS AND ENGINEERS FROM LEADING AI LABS AND UNIVERSITIES

[Institution A] [Research Lab B] [University C] [AI Lab D] [Robotics Co. E]

— Placeholder: replace with authorized partner logos or names —

§ 03 DATASET.CORE ─── scale · fidelity · coverage ┤ ◈ ├
Core Dataset — v2.1

ZILLIA WorldTrace

The most comprehensive synchronized multi-modal dataset for real-world embodied interaction. Built to train robots that understand how humans navigate, grasp, and manipulate the physical world.

CAPTURE PIPELINE
capture
sync
annotate
package
index
deploy
WorldTrace.db — LIVE INDEX
episodes 248,601
hours 8,240 hrs
frames 893M
modalities RGB · Depth · Pose · IMU · Lang · Seg
storage ~120 TB
annotations 12.4M labels
environments 340 unique
format MCAP · HDF5 · LeRobot
data coverage
87%
annotation quality
94%
sync accuracy
99%
§ 04 DATASET.METRICS ─── depth · scale · precision ┤ ◈ ├
Interaction Episodes
0
episodes
Unique task-completion sequences captured in-the-wild across diverse real environments
Capture Hours
0
hours
Continuous multi-modal recording at 30–120 fps with sub-ms sensor synchronization
Video Frames
0
M frames
RGB + depth frames with paired semantic segmentation and object pose ground truth
Annotation Labels
0
M+ labels
Human-verified bounding boxes, keypoints, action labels, and natural language descriptions
Sensor Streams
0
modalities
RGB, Depth, Hand Pose, IMU, Language, Semantic Segmentation — all synchronized
Environments
0
unique
Indoor, outdoor, industrial, domestic, lab — representing real deployment conditions
Structured Storage
0
TB+
Compressed, indexed, and packaged in MCAP and HDF5 for direct model ingestion
Sync Precision
0
ms
Hardware-level timestamp alignment across all sensor modalities in real capture sessions
§ 05 DATASET.QUERY ─── search · retrieve · inspect ┤ ◈ ├
zillia.query — terminal v1.0 LIVE
$>
try:
// ZILLIA Query Engine — ready
// Type a query or click a suggestion to search the dataset
§ 06 CAPTURE.SYSTEM ─── hardware · precision · coverage ┤ ◈ ├
DEVICE A — MOBILE

NP-Scout Mobile

Wearable egocentric capture unit for in-the-wild data collection. Lightweight, battery-powered, and optimised for unconstrained real-world environments.

Resolution4K RGB + 640×480 Depth
Frame Rate30 / 60 fps selectable
Battery8 hrs continuous
IMU9-DOF @ 200 Hz
Request Unit →
DEVICE B — LAB GRADE

NP-Precision Lab

High-fidelity multi-camera rig for controlled manipulation studies. Sub-millimeter pose accuracy with time-of-flight depth and structured light fusion.

Resolution6K multi-camera array
Depth ModeToF + Structured Light
SyncHardware PTP <0.1 ms
Pose Accuracy±0.5 mm / ±0.3°
Request Demo →
§ 07 WHY.ZILLIA ─── differentiation ┤ ◈ ├
01 //

In-the-Wild Scale

Data captured across hundreds of real-world environments — not synthetic scenes. Models trained on ZILLIA generalise to deployment conditions from day one.

02 //

Multi-Modal Synchronisation

Hardware-locked timestamps align RGB, depth, IMU, hand pose, and language streams to under 1 ms. No drift. No post-hoc alignment hacks.

03 //

Structured for Models

Data ships in MCAP and HDF5 with LeRobot-compatible episode indexing. Plug directly into your training loop with one import.

04 //

End-to-End Pipeline

From sensor capture to cloud-indexed, annotation-complete datasets. We own the full stack — no third-party annotation handoffs, no broken metadata.

The physical world
deserves better data.

Robots fail in the real world not because the models are wrong — but because the data they were trained on was never real. Simulations miss the chaos. Synthetic datasets miss the nuance. Lab recordings miss the scale.

ZILLIA was built to close this gap. We put sensors in the world, capture how humans actually move and interact with objects, and structure that signal into the cleanest possible training substrate for embodied intelligence.

We believe the next generation of physically capable AI will be trained on data that looks like ours.

2022Founded
38Team Members
12Research Partners
4Countries Active
Open for Collaboration — 2024

Build with us.

Whether you're training a foundation model, scaling a robotics product, or exploring embodied AI — we have the data infrastructure to accelerate your work.