We combine Bayesian inference, machine learning, large language models, and dynamical systems theory to produce robust probabilistic estimates — helping clients make better decisions under uncertainty.
Full posterior distributions over hidden states, not just point estimates.
Neural and statistical models trained on heterogeneous data streams.
LLM-driven extraction and reasoning over unstructured data sources.
State-space models that track how hidden variables evolve over time.
Scalable HPC infrastructure for real-time inference at production scale.
We license our platform to financial institutions and retail traders. Our probabilistic models support systematic investment research, portfolio construction, and risk quantification.
We integrate multi-physics simulations with Bayesian inference techniques to model cardiovascular disease progression and support predictive analysis, clinical decision-making, and long-term disease management.
We run proprietary systematic trading strategies powered by our own models — putting our platform's predictive capabilities to work in live markets.
Talk to our team about licensing the platform or exploring a research partnership.