Advanced risk modelling, investment deployment analytics, and causal AI - applied to infrastructure, energy distribution, commodities, and financial markets.
Rigorous quantitative methods meeting real-world complexity across infrastructure, energy, and capital markets
Self-managing AI systems that continuously monitor, learn, and recalibrate models against live data - without human intervention.
Probabilistic risk frameworks across infrastructure investment, energy supply chains, and commodity flows.
High-fidelity virtual replicas of physical systems enabling investment scenario testing before capital is committed.
Modelling energy commodity flows, grid demand forecasting, and critical mineral supply chains for the energy transition.
Beyond correlation - causal AI that identifies root-cause drivers across complex, non-linear systems for defensible forecasts.
Data-driven frameworks for sequencing and deploying capital in infrastructure and energy transition projects.
Alumni of leading universities and Tier 1 consulting firms. Backed by UK Government innovation programmes and the Department for Business and Trade.
Live analytical platforms with 13,000+ active users, processing 145,000 queries daily across UK infrastructure networks.
Strategic partnerships with Google Cloud, NVIDIA, and AWS - enabling institutional-scale compute for complex modelling workloads.
Born at the University of Cambridge, with strong support from King’s College E-Lab and the Cambridge business ecosystem.
A quantitative analytics firm combining deep domain expertise in infrastructure, energy, and commodities with institutional-grade modelling capabilities. UK based with an expanding global presence, we have our roots at the University of Cambridge, with strong support from King’s College E-Lab and the Cambridge business ecosystem.
Founded in the United Kingdom and growing an international network of clients, partners and collaborators.
Agentic AI, causal inference and complex risk modelling - advanced mathematics, not a chatbot wrapper.
Network modelling, grid analytics and commodity supply-chain risk, backed by UK Government innovation funding.
Our quantitative methods are domain-agnostic - the same rigorous modelling infrastructure applied across sectors where complexity and risk intersect
Risk scoring, deployment sequencing, and readiness assessment for large-scale energy and infrastructure programmes. Funded by UK Government.
Demand forecasting, grid capacity modelling, and energy commodity analytics supporting the transition to low-carbon energy systems.
Supply chain risk and commodity flow modelling for the critical materials underpinning the energy transition - lithium, cobalt, rare earth elements.
Quantitative signal generation from infrastructure and energy data - providing differentiated, alternative data for systematic trading strategies.
Live deployed platforms - not prototypes
Production ML pipeline: four-model ensemble (XGBoost F1: 0.89, YOLOv8 mAP: 0.87), CI/CD automation, on-device inference at 12ms. 13K users, 145K daily queries.
Learn more →Live interactive platform modelling supply chain flows for the critical commodities - lithium, cobalt, copper - that underpin the energy transition.
Learn more →High-resolution network model of UK infrastructure - nodes, edges, and risk scores - used for investment deployment decisions. Government programme backed.
Learn more →Government-funded, client-proven, well partnered
active users on live platforms
queries processed daily
risk dimensions scored per network node
major technology partners: Google Cloud, NVIDIA, AWS
Institutional-grade tooling for quantitative modelling at scale
We work with investors, infrastructure operators, and energy organisations to build the quantitative foundations for better decisions.
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