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.
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, calibrated against each other. The first establishes what is true at the level of the individual signal. The second learns the latent dynamics that govern how those signals evolve, across modalities and through time. Together, they turn fragmented physiological data into a coherent record of how the body changes.
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, with world models at the centre. Trained to predict the future state of physiology in a learned latent space, so the representation captures what persists in the body and discards sensor artifacts. This work is part of an ongoing collaboration 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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