World Data Scope a data program

Robotics · Science · Audio

A powerful 3-part data engine for the physical world.

World Data turns model gaps into instrumented collection programs, structured evidence, and held-out proof—for machines that act, systems that discover, and models that listen.

Scope a data program Explore the three domains

Founding design-partner program · pre-launch · no public dataset claims

World Record / evidence console live schema
01 / Robotics physical action
state
joint · pose · timing
contact
force · tactile · slip
outcome
failure · recovery
02 / Science physical experiment
protocol
steps · materials · lots
instrument
raw · method · calibration
outcome
positive · negative · failed
03 / Audio physical signal
voice
speaker · language · style
space
room · device · noise
interaction
overlap · turn · latency
One evidence contract provenancerightsqualityhidden evaluation

Illustrative mechanism—not a completed customer dataset or benchmark.

Start from a gapDefine the model behavior or scientific decision that is missing evidence.
Capture the causePreserve the environment, instrument, action, timing, and outcome.
Keep the rights clearCommissioned raw and derived data is customer-owned by default.
Prove the resultSeparate training data from a frozen, held-out evaluation.

The next data frontier

The internet taught models language. The physical world teaches consequence.

Robots need actions and contact. Scientific models need protocols and measured outcomes. Audio systems need speakers, rooms, devices, and interaction. In every case, the valuable record is more than a file.

World Data builds the causal evidence around it—what happened, under which conditions, who owns it, and whether the model improved.

Three collection systems

One company. Three ways into the physical world.

Each domain has its own instruments and failure modes. All three use the same operating discipline: evidence before volume.

01 Robotics machines that act

Turn deployment failures into learning-grade physical episodes.

Retrofit existing robots. Synchronize vision, state, action, force, intervention, and recovery. Connect real collection to calibrated simulation and a hidden task evaluation.

  • Action-conditioned trajectories
  • Contact, failure, and recovery
  • Calibration and clock integrity
  • Real↔sim discrepancy evidence
DeliverableTaskPack

Real episodes, calibration, twin, synthetic lineage, and held-out task result.

robot.cell / task_017illustrative
Y X
t+14.82scontact detectedrecovery / accepted
02 Science systems that discover

Turn a model prediction into reproducible experimental evidence.

Design and run targeted physical experiments for materials, chemistry, physics, and non-clinical biology. Preserve raw instrument files, protocols, process conditions, uncertainty, and failed attempts.

  • Materials and formulation data
  • Protocols, lots, and apparatus state
  • Raw measurements and uncertainty
  • Negative and failed experiments
DeliverableEvidencePack

Study design, protocol, raw observations, QC, outcomes, rights, and prospective evaluation.

experiment / formulation_096illustrative
HELD-OUT REGION 1.0 0.5 0.0
accepted / 71 negative / 18 quarantined / 7
method v1.3raw + parseduncertainty attached
03 Audio models that listen

Capture the voices and acoustic conditions clean benchmarks miss.

Collect consented speech and interaction across speakers, languages, rooms, devices, noise, overlap, and latency. Keep participant rights and physical capture context attached.

  • ASR and domain speech
  • Diarization and separation
  • TTS and voice variation
  • Speech-to-speech interaction
DeliverableAudioPack

Versioned audio, alignments, conditions, speaker metadata, consent, QA, and evaluation slices.

room_04 / speaker_pair_12illustrative
CH–A
CH–B
12.48

A “What happens when—” B “—the turns overlap?”

SNR 14 dBoverlap labeledconsent linked

The shared infrastructure

One evidence standard across every domain.

The sensors change. The contract does not. Every accepted record must explain why it exists, how it was produced, what happened, and where it may be used.

  1. Objective
    Start from the model gap.

    A failing task, missing physical condition, uncertain prediction, or uncovered cohort.

    why collect
  2. Acquisition
    Instrument the causal record.

    Environment, apparatus, action, timing, calibration, sample, speaker, or robot.

    what happened
  3. Outcome
    Keep success, failure, and uncertainty.

    Negative evidence stays distinct from corrupt, censored, or quarantined runs.

    what it means
  4. Governance
    Attach provenance and rights.

    Ownership, consent, lineage, allowed uses, transformations, and retention travel with the data.

    who controls it
  5. Evaluation
    Freeze proof before training.

    Separate the acquisition program from a held-out test that can reveal real lift.

    did it work

How World Data works

Collect less blindly. Learn more from every physical record.

We begin with a bounded behavior or decision. The first program is designed to expose whether the missing evidence can move it—not to manufacture an impressive volume number.

Bring us one model gap
  1. Define
    Freeze the question.

    Set the intended use, baseline, acceptance rule, and protected evaluation boundary.

  2. Design
    Specify the missing world.

    Select environments, instruments, subjects, samples, failure slices, and metadata.

  3. Capture
    Preserve the raw evidence.

    Collect physical context at the source and reject silent defects before delivery.

  4. Prove
    Measure what changed.

    Compare the baseline and updated system on the frozen, held-out evaluation.

Data boundaries

Your physical evidence should not become someone else’s training set.

Customer-commissioned raw and derived data is customer-owned by default. Any reusable methodology, consortium contribution, or public release must be explicit in the contract.

Working rights modeldesign-partner terms
Commissioned raw dataCustomer-owned
Commissioned derived dataCustomer-owned
Generic adapters + schemasWorld Data retained
Cross-customer model trainingNever by default
Consortium or public releaseSeparate opt-in

Final ownership, privacy, consent, residency, and permitted-use terms are engagement-specific.

Start with one gap

What does your model still misunderstand about the physical world?

Prepare a short scoping brief. It stays in your browser until you choose to copy or email it.

A useful first brief includes:
  • one model behavior or scientific decision;
  • the physical condition it currently misses;
  • the evidence you already have;
  • the result that would justify a larger program.

This page does not transmit or store your answers.