Task-specific
Focus the dataset on a narrow operational behaviour, workflow or set of edge cases.
Our capabilities
Capabilities for Physical AI teams.
Real-world data, human-reviewed labelling, structured metadata and quality-controlled delivery shaped around robotics training and evaluation needs.
Data → labels → structureDesigned around your task.
Real-world task data
Task demonstrations and operational examples can be designed around the environments and behaviours your model needs to understand.
Labelling & annotation
Human-reviewed labels can cover actions, objects, states, task stages, errors, safety moments, language and custom project taxonomies.
Multimodal structuring
Visual data can be paired with contextual metadata and other approved modalities where they are relevant to the agreed project design.
Quality assurance
Review criteria can be defined before work starts, with quality checks applied against the agreed schema and acceptance thresholds.
Custom schemas
Dataset structure, naming conventions, metadata fields and label definitions can be tailored to your downstream training or evaluation workflow.
Training-ready delivery
Final datasets are organised, quality-reviewed and delivered using the formats and secure transfer arrangements agreed for the project.
Flexible by design
Not every robotics project needs the same dataset.
We define the work around the model, the task and the operating environment rather than forcing every project into a fixed package.
Environment-specific
Target the conditions, layouts and operating context that matter to real-world deployment.
Schema-specific
Align the labels and output structure to the ontology and pipeline your team already uses.
Have a specific robotics data requirement?
Tell us what the system needs to learn and we’ll discuss a suitable project design.