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Egocentric data collection · Homes & everyday work

Real-world data from everyday tasks.

We collect egocentric video, hand interaction and motion data from real household work (cooking, washing and food preparation) for teams building Physical AI and robotics.

The ordinary manual tasks that fill a working home, captured as they're actually done.

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Proof of value

Real hours,
delivered.

Across the environments we work in (homes included), we don't argue data quality on a call. We ship footage, teams run it through their own pipelines, and the next order follows.

80,257

hours of first-person footage delivered

Shipped to teams and running in their pipelines today, and counting.

What we capture

Household tasks,
start to finish.

Rolling and cutting dough, washing vessels, cooking at a stove, rinsing utensils: the small manual sequences that make up daily domestic work.

Rolling and cutting dough on a board, both hands in frame.
Washing steel vessels in a basin of water.
Cooking at a stove, stirring a pan of vegetables.
Rinsing utensils at an outdoor washing area.

Each session is recorded from the worker's own point of view, capturing the sequencing and hand positioning behind ordinary tasks that are easy to overlook and hard to simulate.

Why household data

Households don't repeat themselves.
Everyday data has to reflect that.

  • 01

    Everyday, not staged

    These are real domestic tasks performed in real homes, not a demonstration kitchen.

  • 02

    Sequenced manual work

    Cooking and cleaning tasks involve multi-step sequences, not single isolated actions.

  • 03

    Object and material variety

    Different vessels, ingredients and surfaces create the variation household-facing models need.

How it works

From real-world task
to structured data.

  1. 01

    Define

    We agree the tasks, environments, modalities and scale with your team.

  2. 02

    Collect

    Trained participants record real work in real places, to a written protocol.

  3. 03

    Synchronize

    Visual and motion signals are aligned to one timeline for every session.

  4. 04

    Review

    Sessions are checked against the brief before they move any further.

  5. 05

    Deliver

    Structured, documented data lands in your storage in the format you train on.

Get in touch

Tell us what your
model needs to learn.

Tell us the task, environment, modalities and scale. We'll design the collection around your requirements.

Start a paid pilot
Talk to us