We replace gut-feel decision-making with production-grade quantitative infrastructure — models, simulations, and inference systems that are mathematically rigorous, auditable, and built to operate at scale.
Systematic alpha research, signal generation, and execution algorithm design for equities, fixed income, derivatives, and digital assets. Full backtesting infrastructure with realistic transaction cost modelling.
Value-at-Risk, Expected Shortfall, stress testing frameworks, and credit risk models built to regulatory standards. Monte Carlo simulation engines and scenario analysis tools for portfolio risk management.
Linear, integer, and stochastic optimisation for portfolio construction, resource allocation, pricing, scheduling, and supply chain problems. Solvers integrated directly into your operational systems.
Bayesian inference pipelines, A/B testing frameworks, causal inference systems, and experimental design tooling. We replace ad hoc analysis with principled statistical decision infrastructure.
Agent-based models, discrete-event simulations, and Monte Carlo engines for scenario planning, capacity forecasting, and operational stress testing. Parallelised on cloud compute for sub-minute run times.
Dynamic pricing engines, demand elasticity models, and revenue management systems for subscription, marketplace, and transactional business models. Reinforcement learning approaches for continuous price optimisation.
PageTrace's growth team was drowning in disconnected metrics with no reliable way to attribute revenue to product decisions. We built a statistical inference and causal modelling engine over their event pipeline — giving every business unit a rigorous, auditable framework for measuring what actually drives growth.
Tell us about the decisions you want to make more precisely and we'll design the quantitative infrastructure to support them.