The Problem
of drugs that pass animal studies fail in clinical trials.
Behavioral endpoints inform billion-dollar decisions — and most are still scored by eye, or with software a human has to supervise. Observer variability corrupts the data, and the subtle signal that separates a working drug from a failed one never enters the dataset. The way forward isn't to abandon animal models. It's to measure them like billion-dollar decisions depend on it.
Powered by AI
Machine vision meets neuropharmacology.
Our platform uses continuous tracking, pose estimation, and deep behavioral classification to extract hundreds of phenotypic parameters — many invisible to the human eye.
We benchmark our models against published pharmacology as we build them — designed to capture dose-response relationships, temporal dynamics, and subtle side-effect profiles, and validated assay by assay before they ship.
metadata measurements
for stride-level kinematics
invisible to rodents
measured every second