Biology evolves continuously. Medicine measures it in snapshots. The meaningful signal lives in the gap.
We are a mathematics and artificial intelligence company. Building the measurement layer for healthcare. A team of mathematicians, physicists, quantitative researchers, ML engineers, and neuroscientists.
The early signatures of disease are continuous, subtle, and buried in biological noise long before they reach a clinic. Reading them is, before anything else, a problem of measurement under constraint.
The bottleneck in healthcare is the measurement itself: too coarse, too discrete, too averaged to resolve what biology is actually doing. Resolve it, and the coarse becomes precise, the discrete becomes continuous, the averaged becomes individual.
The field has scaled ever-larger models on top of these approximations, treating them as ground truth. We start one layer down.
We work in the lineage of the physical and quantitative sciences, where measuring under noise, scarcity, and irregularity is the default rather than the exception. The result is a new class of measurement, not a refinement of existing ones, and with it, disease detected earlier, drug effects resolved sooner, individual physiology read at a precision medicine has not had before.
Two layers, and the order matters. The first establishes what is true at the level of the individual signal, before any model sees it. The second learns the latent dynamics that govern how those signals evolve, across modalities and through time. Run in this order because it is the only order that works: frontier deep learning on broken measurement returns confident nonsense, not truth. Get the measurement right first, and the models finally have something real to learn from.
Stochastic processes, statistical inference, dynamical systems, and the geometry of measurement under constraint. Methods designed for noise, fragmentation, irregular sampling, missingness, and individual-level inference at low N. Outputs are traceable, calibrated, and interpretable by design.
Deep learning over multimodal physiological time-series, trained to predict how physiology evolves in a learned latent space, so the representation keeps what persists in the body and discards sensor artifacts. This is where our world models sit, built with NVIDIA.
Quantitative descriptors of physiological change, derived where the signal supports them. Calibrated, traceable, and bounded by their own uncertainty. The output of the lab: measurements that downstream applications can build on.
Applied across 7M+ individuals and 30+ clinical conditions, and counting.
Better resolution of human physiology sits upstream of a long list of seemingly different problems. Solve it once, and the same primitives serve biotech, pharma, devices, health systems, public health, and healthspan science.
Pharma, biotech, wearables, health systems, and CROs. Retrospective or prospective, single-modal or multimodal, individual or population level, disease-agnostic. We start from the data you already collect.
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