mnfldwrks — models of the real world
We build AI systems that learn mathematical models of biological systems from data. Not chatbots. Not pipelines. Models that simulate, predict, and learn from experiments.
Our models live on manifolds — the geometric surfaces that describe how cells move, differentiate, and respond. We learn the dynamics on these surfaces directly from high-dimensional biological data, then use those dynamics to do science.
Geomancer is an AI scientist. It takes biological data — single-cell transcriptomics, imaging, time series — and constructs a mathematical world model: a system of differential equations on a learned geometric space that captures how the biology actually works.
Then it uses that model. It runs simulations, generates testable hypotheses, designs experiments, and updates itself when the lab results come back. Geomancer learns ODEs from your data and tells you what happens next.
The core innovation isn't any single model. It's the loop: a system that learns, predicts, tests, and refines — continuously. Each pass through the cycle makes the model sharper and the hypotheses more precise.