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Home / Services / Quantitative Intelligence

QUANT
INTEL
LIGENCE

Service Category
Decision Infrastructure
Domains
Finance · Risk · Optimisation
Typical Engagement
3 – 12 months

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.

Rigour Over Intuition Every model we deliver comes with formal assumptions, confidence intervals, and a clear statement of where it breaks down. We build systems you can defend to regulators, boards, and auditors.
Research-to-Production Pipeline Most quant research dies in Jupyter notebooks. We build the engineering layer — backtesting frameworks, live execution infrastructure, and monitoring systems — that takes models from prototype to production.
Domain Expertise Embedded Our quantitative engineers come from systematic trading, actuarial science, and operations research. We don't just build software — we understand the mathematics and business logic driving your decisions.
What's included

WHAT WE
DELIVER

Algorithmic Strategy Development

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.

Risk 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.

Optimisation Systems

Linear, integer, and stochastic optimisation for portfolio construction, resource allocation, pricing, scheduling, and supply chain problems. Solvers integrated directly into your operational systems.

Statistical Inference Engines

Bayesian inference pipelines, A/B testing frameworks, causal inference systems, and experimental design tooling. We replace ad hoc analysis with principled statistical decision infrastructure.

Simulation 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.

Pricing & Revenue Optimisation

Dynamic pricing engines, demand elasticity models, and revenue management systems for subscription, marketplace, and transactional business models. Reinforcement learning approaches for continuous price optimisation.

Tools & platforms

OUR
STACK

Compute
Python / NumPy
Compute
Julia
ML
PyTorch / JAX
Stats
Stan / PyMC
Optimisation
Gurobi / CVXPY
Simulation
SimPy / Mesa
Backtest
Backtrader / Zipline
Data
Pandas / Polars
Parallel
Ray / Dask
Serving
FastAPI
Experiment
MLflow / Weights & Biases
Viz
Plotly / Dash
Featured work

CASE
STUDY

Event Pipeline
Causal Model
Stats Engine
Attribution Layer
Insights API
Dashboard
Quant / SaaS Analytics
PageTrace Business Analytics Engine

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.

4×
Faster Decisions
98%
Model Accuracy
1B+
Events Modelled
Common questions

FAQ

Do you need access to our proprietary data? +
Yes, for model development we need access to relevant historical data. We work under strict NDAs, can operate within your own infrastructure (no data leaves your environment), and follow data minimisation principles — only the data actually required for the model is accessed.
How do you validate that models are actually working? +
Every model ships with an evaluation framework established before training begins — out-of-sample test sets, walk-forward validation, and predefined performance thresholds. We also build production monitoring that tracks live performance against backtest expectations and alerts on statistically significant deviation.
Can models be explained to regulators and non-technical stakeholders? +
We build explainability in from the start — SHAP values, LIME explanations, and feature importance reporting for complex models. For regulated domains, we prefer interpretable model families where possible and always document assumptions, limitations, and decision logic in language your compliance team can review.
What distinguishes your quant work from standard data science? +
Rigour and productionisation. Standard data science often ends at a Jupyter notebook. We bring formal statistical discipline — treatment of uncertainty, overfitting prevention, realistic cost modelling — and we build the engineering infrastructure to run models reliably at scale, in production, with monitoring and rollback capabilities.
Do you work with non-financial quantitative problems? +
Yes. While we have deep financial expertise, our quantitative methods apply equally to supply chain optimisation, clinical trial design, energy market modelling, insurance pricing, and operational research problems. The mathematics is domain-agnostic — we adapt to your context.
Ready to build

START YOUR
PROJECT

Tell us about the workflows you want to automate and we'll map a path from where you are to where you want to be.

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