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FIG.01 · PHYSICAL INTELLIGENCE UNIT

Dataset

The data infrastructure layer for Physical AI.

Hand-drawn technical sketch: a robotic manipulator and a human hand reaching toward each other, meeting at a glowing blue point
DrawingDS-001.PNG
UnitDataset
TitleNativitas Machinae
DWG no.DS-001 · REV A
Scale1:4 · ISO 30°
Sheet1 of 1
( 00 )Document index

Build better AI

Learn from how real work happens.

Evaluate what matters

Study success, failure, and recovery.

Understand industrial work

Connect operations to their evidence.

( 01 )The premise · DS-002

Physical intelligence cannot learn from pixels alone.

Real private industry. Closed doors. Alongside the operational experts that make the world turn.

Dataset is the data infrastructure layer for Physical AI, capturing authentic industrial work through exclusive agreements inside warehouses, plants, and service fleets. We deliver rights-cleared, operational-ready training data to robotics teams and AI labs.

( 02 )Why AI researchers come to us
3 gaps · observed

Industrial data exists.
Buyer-ready datasets do not.

We turn real-world operations into structured data and intelligence that AI developers and businesses can use.

GAP / 01

Access is the moat.

The highest value work happens behind badge readers and NDAs. We are already inside, under exclusive agreements.

GAP / 02

Reality is multimodal.

Vision, sensors, machine state and outcomes arrive together, time-synced, ready to train on.

GAP / 03

Trust is structural.

Consent, rights and provenance ship with every episode, so your legal team clears it in days, not quarters.

( 03 )The Dataset system

The data stack
for physical AI.

Capture hardware, data processing, model training, and APIs. One connected system.

Access structured, training-ready episodes or commission proprietary capture of the tasks, environments, and failure modes your model has yet to master.

01Dataset CaptureObserve the work
02Dataset OSStructure the episode
03DataFaceRelease the intelligence
( 04 )Signal vocabulary · QIE

Reality,
made learnable.

Go beyond footage. Structured, time-aligned episodes connect real-world actions, operational context, and observed outcomes, built to help your model learn what happened, where, and what came next.

01RGB · DEPTH

Egocentric vision

Live
02POSE · TRAJECTORY

Human action

Live
03PLC · TELEMETRY

Machine state

Live
04TASK · ENVIRONMENT

Workflow context

Live
05SUCCESS · FAILURE

Outcome

Live
06VERIFIED · TRACEABLE

Rights & provenance

Live
Technical blueprint of a worker and robotic arm collaborating in an instrumented industrial cell
( 05 )Research program
ACTIVE THESIS

World Action Models · WAM-01

Can machines
inherit experience?

We are exploring models grounded in the states, actions, failures, recoveries and outcomes of real work.

Controlled research program
( 06 ) Request accessDS-001 · LIMITED

Buy the data no one else can reach.

Sample episodes, pricing and site coverage for robotics and frontier AI teams. Exclusivity windows available per task category.

Request a sample →