Born in a small lab. Built for big data.
We source it, annotate it, and train the model that runs on it — at a price a small team can actually say yes to.
Good training data shouldn't cost more than the model it trains. With us, it doesn't.
Big vendors price for enterprise procurement, then send you a minimum order you can't justify. We're a small lab. Our overhead is small, so our quote is small — and the labels are held to the same standard either way.
Capabilities
Classification, detection, segmentation, keypoint, tracking — one frame, labeled five ways. The annotation you buy is the annotation your model is scored against.
Services
Take any one of these on its own, or hand us the whole chain.
Name the data and we'll get it. If it already exists, we find and license it. If it doesn't, we collect it to your spec — field capture, commissioned shoots, synthetic generation, or a targeted top-up of the exact edge cases your model keeps failing on. No domain is off the list.
License a dataset that already exists instead of building one from zero — usually the cheapest way to get moving. Splits are made, class distribution is documented, and the labeling guide ships with it, so you know what each class was supposed to mean.
Your images and video, labeled to a written guide we agree on before anyone draws a box. Ambiguous frames go to a review queue instead of a guess, and the answer gets written into the guide so it never comes up twice.
We train, tune and benchmark on the data we built, then hand over the weights, the export you asked for, and an evaluation report your own team can read without a translation layer.
Any data. Any domain. Any budget.
Shelves, crops, defects, documents, X-rays, dashcam footage, warehouse video. If it can be captured or licensed, we can get it, label it to your spec, and hand it over in the format your training script already reads.
We're not the biggest vendor you'll talk to. We're the one whose quote won't make you rethink the project.
How a project runs
Nobody should commit a hundred thousand images to a spec they haven't seen applied. So we label a small batch first, for free, and you decide from there.
You send sample data and describe the classes. We write the labeling guide, label 100 images against it, and send them back for you to tear apart.
Annotators work to the approved guide. Anything it doesn't cover goes to a queue and gets a written answer — never a judgement call made alone at 2am.
Every batch gets a second pass. We track disagreement rate per annotator and per class, so quality shows up as a number you can argue with.
Dataset in your format, train/val/test splits, class distribution report, and the guide it was labeled against. Retraining stays reproducible.
Start a project
Tell us the task, the volume, and the two classes you already know get confused. We'll come back with a quote — or the one question standing in the way of it.