Real-world data.
Real-world intelligence.
Real-world demonstrations, multimodal datasets, annotation and data verification for Physical AI companies building systems that perceive, learn and act in the physical world.
Data built around how Physical AI systems learn.
From human demonstrations and video to robot-action records and structured metadata, we tailor datasets to your Physical AI use case. Optional annotation is available when required.
Egocentric video
First-person human demonstrations captured around physical tasks and real operating conditions.
Explore capability ↗02 / CAPABILITYUMI demonstrations
Handheld-gripper manipulation data, scoped for camera views, pose and gripper signals.
Explore capability ↗03 / CAPABILITYRobot teleoperation
Hardware-specific demonstration programs with observations, actions and trajectory records.
Explore capability ↗04 / CAPABILITYAnnotation & labelling
Action segmentation, object labelling, task ontology, metadata and human QA.
Explore capability ↗05 / CAPABILITYData verification & engineering
Source provenance, authenticity screening, recording integrity, annotation QC and structured delivery.
Explore capability ↗06 / CAPABILITYDataset sourcing
Explore licensed existing material or commission task-specific collections around your requirements.
Explore capability ↗Real-world data you can trace, inspect and trust.
A Physical AI dataset is only useful when its origins, content and annotations stand up to scrutiny. We scope provenance and authenticity checks alongside technical quality control, helping buyers understand where data came from, whether the recording is consistent with its claimed source, and whether the examples are fit for their training objective.
From supplier-origin evidence and usage rights to duplicate detection, timestamp checks and human sample review, we document what has been assessed, what remains uncertain and what meets the agreed acceptance criteria.
Explore data verification ↗Verification is evidence-based, not an absolute guarantee against synthetic, manipulated or incorrectly represented data. Specific checks and reporting are agreed for each engagement.
Get the right data. Not just more data.
Choose an existing dataset subject to licensing and availability, or define a custom program for the tasks, environments and sensor setup your model needs.
Off-the-shelf datasets
Explore available footage and demonstration datasets from suitable sources, with rights, specifications and sampling confirmed before order.
- Faster starting point where a suitable dataset exists
- Review representative samples and metadata
- Confirm permitted training uses and delivery format
Custom task collection
Commission focused demonstrations for a defined physical task, target environment, embodiment or model-training objective through a scoped project.
- Define capture protocol and acceptance criteria
- Specify mono, stereo and additional sensors
- Agree pilot, volumes, QC and packaging
Every modality has a purpose.
The right observation and action signals depend on your embodiment, training target and deployment environment. Additional streams are specified and verified per project.
Mono RGB
Single-view video for activity, environment and manipulation demonstrations.
Stereo RGB
Paired views for additional spatial context, subject to synchronization and calibration requirements.
IMU + motion
Inertial, pose and motion signals where the collection rig records them.
Gripper + actions
Gripper pose, width, robot joint states and commanded actions where supported by the capture setup.
Temporal metadata
Task IDs, steps, timestamps, outcomes, episode structure and scene context.
Training delivery
Schema mapping and packaging for formats and tooling agreed before project commencement.
Learn more about our world-class annotation technology.
Choose the data format that fits your team. We supply real-world recordings with or without annotation. When you need labels, our annotation technology and human review workflows can be added to your dataset.
Annotation when you need it.
Add task-specific labels, hand–object interactions, action boundaries and outcome tags when required. Keep your data unannotated when your team prefers to label it internally.
- Custom taxonomies and export formats
- Manual and AI-assisted approaches, as validated
- Reviewer corrections and acceptance checks
Defined at the brief. Verified at delivery.
A practical workflow for turning a Physical AI learning objective into usable, verified training examples.
Define the data
Target task, embodiment, modalities, schema, geography, volume and allowed uses.
Secure demonstrations
Evaluate existing material or develop a feasible, approved collection plan.
Annotate and QC
Validate technical integrity, label data and apply agreed human review.
Package for use
Provide accepted datasets with metadata and documentation in the agreed format.
From factory floors to everyday manipulation.
We focus on repeatable physical workflows that translate into meaningful learning examples.
Warehousing & logistics
Pick, pack, sort, replenish, inventory and material handling.
Manufacturing
Assembly, wiring, inspection, tool use and repetitive machine-adjacent tasks.
Retail & fulfilment
Shelving, stock handling and order preparation.
Commercial operations
Cleaning, facilities and routine service workflows.
Home & domestic tasks
Folding, tidying, object handling and structured household demonstrations.
Custom environments
Customer-defined tasks where safety, access, permissions and feasibility can be established.
Tell us what your model needs to learn.
Share your data type, target task, required modalities and indicative volume. We will confirm scope, supply options and next steps.