Autonomous, Adaptive & Data-Efficient Decision Making.
Based on a mathematically principled and practical approach.
We call it Principled AI.

How works
Probabilistic Modelling Icon

Probabilistic Modelling

Probabilistic Modelling creates models of an uncertain, dynamic world with limited data.

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Reinforcement Learning

Reinforcement Learning agents use models of the environment to make and evolve decisions.

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Multi-agent Systems

Multiple agents compete or collaborate to optimise decisions using game theory.

At we believe for AI to make useful, efficient and effective decisions it has to be based on mathematical principles. So we have combined three different branches of mathematics to create the world’s first AI platform for general purpose decision-making.

Probability theory provides a solid foundation to model and learn uncertain environments, and make predictions. Probabilistic models are data-efficient and better suited to representing real-world environments than deterministic models.

Reinforcement Learning (RL) provides a framework for machine-based autonomous decision-making based on Bayesian decision theory and Bellman optimality. RL agents use probabilistic models to make decisions that maximise rewards in uncertain environments.

Since real-world scenarios are not just uncertain, but also complex and dynamic, we use the principles of Game theory to optimise decision-making across multiple agents, in collaborative or competitive settings.

Together, these three distinct domains of mathematics solve a broad range of problems, whether it is responding to the uncertainty of demand in a supply chain or ride sharing, maximising returns in a dynamic and uncertain stock market, or reacting to change in a robot's environment.

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Introducing VUKU™, our AI platform.
Ideally suited for decision-making across multiple industries.

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