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At Math Labs, underneath our Concept Engine is the continuous exploration of superior approaches to solving a range of strategic ‘hard’ computational mathematical and AI problems that gives our platform a significant advantage.



Un-aided Anomalous Signals

Understanding ‘anomalous points, patterns and regimes’ represents a significant area of interest to Math Labs, given the range of use-cases it represents.

At our lab, we look at anomalies such as 'financial fraud', 'transaction anomaly' as well as at anomalous entities through a human understandable 'causal' lens , away from the more prevalent  'black box' approach  - requiring us to unify singular approaches from across mathematical fields, rather than limiting ourselves to traditional AI.


Quantum-Classical hybrid mathematics

A core mathematical domain at Math Labs, given its focus on operating on world data - is 'massive parameter space' optimization. Our research has successfully brought together Quantum and Classical approaches together to solve such problems. We are increasingly moving ahead on solving a range of problems, not naturally suited to quantum approaches - thereby finally - democratising Quantum mathematics.

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Human Understanding

Building an understanding of the world, mimicking humans but at scale, forms a central element of our Concept Engine. At any point in time our 'Human Understanding' research area has several work streams engaged in creating or optimizing valuable mathematical components that - when put together - enhances this understanding.


Human-driven 'Discovery'

The Discovery research area at Math Labs continually explores ways to search for entities, most relevant to a concept. We closely follow how a human starting with an idea, arranges and sorts information to find the most useful company, product or event related to that idea.

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