Every label gets checked.
Models learn whatever their data teaches them, mistakes included. So we check each label more than once, and we measure every pilot against a spec you approve before we start.
Three levels of review on every label.
An annotator labels it, a reviewer checks it, and an expert does a final QA pass before it ships.
The right people for the task
Engineers, clinicians, linguists and field contributors, from people filming everyday tasks at home to experts annotating medical records.
Our own labeling tool
We built it ourselves, so each project can have its own workflow and quality checks.
Personal data removed
We strip personal information from data recorded in homes, hospitals and other sensitive settings.
Datasets we own and license
Alongside custom projects, we build datasets we can license across clients for pre-training, post-training and RLHF.
Start small, then scale.
Agree on the spec
We define the task, data types, volume and the quality bar the data has to meet.
Deliver a first batch
A small, fast delivery you can test in your own pipeline, measured against that spec.
Ramp to production
Once the pilot meets the bar, we scale collection and labeling with the same process.
From data to benchmarks.
Data and expert evaluation
Real-world data collection and labeling, checked by experts.
Benchmarks and quality standards
Shared evaluation sets that measure how models perform on real tasks.
Model development support
Working alongside teams as they train, test and deploy.