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 market intelligence analysis, 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.