Real-world task data
Repeatable workflows from logistics, industrial, commercial and other suitable operating environments.
- First- and third-person visual data
- Success, correction and edge-case examples
- Project-specific task protocols
Real-world data. Trusted results.
Data + labelling for Physical AI.
Fax Research provides high-quality real-world data and human-reviewed labelling to help robotics and embodied AI teams train, evaluate and improve systems that operate in the physical world.
AutoplayingPhysical AI in real operating environments
Real-world dataPurpose-built datasets shaped around practical tasks and operating conditions.
Labelling & annotationHuman-reviewed labels aligned to your ontology, objectives and acceptance criteria.
Training-ready deliveryStructured, quality-reviewed datasets delivered in the formats your workflow needs.
Our capabilities
Built around what your system needs to learn.
We keep the workflow straightforward: understand the task, source the right real-world examples, apply the right labels, review quality and deliver a usable dataset.
Labelling & annotation
Labels can be designed around actions, objects, task steps, states, safety moments, language and other project requirements.
- Custom schemas and ontologies
- Human quality review
- Clear acceptance criteria
Structured delivery
Datasets are organised around the agreed format, metadata structure and downstream training or evaluation workflow.
- Multimodal and contextual metadata
- Quality checks before delivery
- Secure transfer arrangements
Our process
From task brief to labelled dataset.
A defined process keeps projects easy to understand while leaving room to tailor the data, labelling and delivery requirements to each robotics team.
01 / DEFINE
Align
Agree the target task, environment, data requirements, schema and success criteria.
02 / CAPTURE
Source
Run repeatable real-world task sessions in approved environments using an agreed project brief.
03 / LABEL
Structure
Apply agreed labels, metadata and dataset structure with human review.
04 / QA + DELIVER
Verify
Review against acceptance criteria and deliver the final dataset securely.
Industries
Real tasks. Real operating environments.
Our priority environments are those with repeatable physical workflows that are relevant to robotics and Physical AI development.
Warehousing & logistics
Picking, packing, sorting, replenishment, inventory and material movement.
Manufacturing & industrial
Assembly, inspection, tool use, machine tending, packaging and maintenance.
Retail & fulfilment
Stock movement, shelving, back-of-house operations and fulfilment routines.
Commercial operations
Cleaning, restocking, inspection, facilities support and repetitive service tasks.
Hospitality & kitchens
Preparation, organisation, movement, cleaning and structured back-of-house tasks.
Custom environments
Project-specific environments where task relevance, safety and permissions can be established.
Trust & quality
Clear scope at every stage.
Projects are designed around agreed boundaries, controlled access, practical data minimisation and documented delivery requirements.
Agreed project scope
Human-reviewed labelling
Personal-data minimisation
Defined retention & deletion
Approved recipients & uses
Incident-response pathway
Have a suitable operating environment?
Factories, warehouses and commercial operators can explore a separate Data Partner pathway.
Talk to our team
Tell us what your system needs to learn.
Share the task, environment, target system and any labelling requirements. We’ll arrange a conversation to understand fit and discuss the next steps.