Senebiclabs is the data layer under medical AI. We have licensed clinicians review the data your models learn from and get tested against, so it was checked by people who actually understand medicine.
A medical model is only as good as the data behind it. Most of that data gets labeled by crowd workers with no medical training, or graded by another model.
That is fine for a demo. It becomes a problem the moment a hospital, a regulator, or an investor asks how you know the model is right. We started Senebiclabs to close that gap. Real clinicians check the data, every decision is recorded, and you get back something you can actually defend.
Whatever stage your model is at, we do the clinical data work behind it.
We grade your model's answers against a clinician's read, and give you a report that shows every mistake, including the ones that matter clinically.
We turn raw medical data into labeled ground truth. Imaging, clinical text, and classification, reviewed one case at a time.
We write the gold answers, preference data, and test sets you fine-tune and check your model against.
Every case is reviewed by a licensed clinician. On the work that matters, several people review the same case and we show you how often they agreed.
Every case is reviewed by a credentialed clinician, and we keep our own record of what was decided and when. To you, reviewers stay anonymous; what you can show a safety board is the process: qualified specialists reviewed your data, with a full audit trail behind it.
We remove identifying details before anyone sees a case, and we keep each client walled off from the rest. The system enforces it, not a line in a contract.
We tell you how many cases a figure is based on, where reviewers disagreed, and what we couldn't assess. If a result is thin, we say so.
We start with the work only clinicians can do: judging, checking, and writing medical data. Over time we want to cover the whole data stack a medical model needs, across imaging, pathology, clinical text, genomics, and medical language models.
Start small, see if it holds up, then scale. Book a demo, or read the docs.