A better way to turn human expertise into the data that helps AI advance
We work with AI companies to create training and evaluation data for complex, real-world tasks: finding the right domain experts, designing tasks that reflect how decisions are actually made, and turning professional judgment into clear rubrics, annotations, and evaluation criteria that models can learn from.
We have seen this problem from both sides
Our team has built AI and data systems at Microsoft AI, Amazon, Boeing, and Shopify, and worked directly inside leading data annotation companies. That experience taught us that the hardest part of data annotation is no longer producing more labels. It is capturing the right expertise and translating it into useful signal for the model.
As AI takes on harder and more specialized work, this becomes even more important. Experts know what good looks like, which details matter, and where seemingly reasonable answers fall short. But that knowledge is often difficult to capture consistently and at scale.
That is the gap we want to close: turning expert judgment into structured, reusable data that helps AI systems reason, evaluate, and perform better in the real world.
We believe the future of data annotation is not simply more human feedback. It is better ways to capture what humans know.
Measured, not asserted
Every quality method finds one class of defect and misses another. We name both, run the layers a dataset needs, and report what each one found.
The six quality layersEvidence, not scores
Before an expert joins a project we collect evidence that they have the skills and have done the work, and that evidence stays with them.
How we vet expertsWritten down as we go
What we measured, what it does not establish, and what changed when we looked. One research note a week, from the work.
Read the research