Process
From requirements to model-ready data.
Every program follows the same structured path, adapted to the modalities, geography and quality requirements of the project.
Process
From requirements to model-ready data.
- 01
Define
We work from your model objectives, required modalities and data specifications.
- 02
Design
A collection and annotation workflow is structured around the project requirements.
- 03
Collect
Human-generated data is captured through controlled collection workflows.
- 04
Validate
Data can pass through structured quality and consistency checks.
- 05
Deliver
Approved datasets are prepared for secure delivery and integration.
Quality
Quality before quantity.
Useful AI data depends on clear specifications, controlled workflows, validation and structured delivery — volume alone does not make a dataset usable.
Specification
Requirements are documented before collection begins, so every task has a clear definition of done.
Validation
Structured checks review captured data against the agreed specification before it moves forward.
Consistency
Shared instructions, rubrics and reviewer calibration keep outputs comparable across contributors.
Traceability
Datasets are organised with structured metadata so records can be traced through the workflow.
Building an AI system that needs better data?
Tell us what your model needs to learn. We'll explore how a purpose-built human data program could support it.