One partner for the label work, the data it is built on, the application on top of it, and the option of generating the data rather than collecting it.
They are separate services because they can be bought separately, and plenty of projects only ever need one. They sit together because the boundaries between them are where projects usually go wrong: a dataset handed from one vendor to another loses the guideline, and an application team that never read the raw data inherits its faults without knowing they are there.
Human-labelled training data across text, image, video, audio and 3D, and the feedback layer for language models: preference data, written demonstrations, rubric scoring, red-teaming.
Rendered training data built in 3D instead of collected and labelled: for classes that are rare, expensive to photograph, or cannot leave the building.
Photorealistic 3D rendering
Rare-class and edge-case generation
Dataset augmentation
Privacy-preserving substitutes
Which one do you need?
If the answer is not obvious, the project funnel asks six questions and gives you a written brief at the end whether or not you work with us.
You have data and it needs labels
Data annotation and model feedback. This is also the answer if you have a model and need humans to judge its output rather than label its input.
You have data and do not trust it
Machine-learning data preparation. An audit is usually the cheapest useful step, and it can happen before you commission any labelling at all.
You know what you want to build, not how
AI application development. Including the case where the honest first answer is that the data to build it does not exist yet.
The data you need does not exist to collect
Synthetic data generation. Rare defects, subjects you cannot photograph enough of, or anything that cannot legally leave your building.
You are not sure yet
Say so. The funnel has an explicit option for it, and a project described badly by someone honest is easier to scope than one described confidently in the wrong terms.
