Causality – Predictive Machine Learning & Quantum Optimisation

In today's volatile markets, organisations using advanced forecasting outperform competitors by 15-25%. Causality delivers enterprise-grade forecasting as a full-service engagement, harnessing Microsoft Azure Machine Learning to achieve remarkable precision. Whether modelling macroeconomic trends or predicting demand at the product level, Causality translates data into clear, actionable insights. Seamlessly integrating with your existing data sources via Microsoft Fabric, it automates model selection and optimisation, presenting results through intuitive, interactive Power BI dashboards.

Built on Trusted Enterprise Platforms: Seamlessly integrates with your existing Microsoft ecosystem—from Azure and Fabric to Power BI—whilst leveraging NVIDIA acceleration and IBM Quantum for next-generation optimisation.

Unleash the Power of Prediction

Causality transforms uncertainty into quantifiable and actionable insights with an evidence-based, five-step data science methodology. Explore interactive what-if scenarios, simulate outcomes, and optimise critical decisions, be it portfolio management, vehicle routing, or staff scheduling, using real-time quantum annealing.

Benefits That Drive Success

Our Proven Methodology

From discovery to deployment in just six weeks, Causality follows a rigorous five-step process:

1. Discovery & Problem Definition: Clarify the commercial question, agree the value of accurate forecasts, confirm executive sponsors, data owners and end-users, and set success metrics and the required forecasting horizon.

2. Data Preparation and Analysis: Consolidate all relevant internal and external data sources, validate quality, address gaps, ensure compliance, and verify that the variables can reliably predict the target outcome.

3. Feature Engineering and Model Development: Engineer meaningful features, including macro-economic signals, use Azure Machine Learning to benchmark multiple algorithms in parallel, and select the model (or ensemble) with the strongest business performance.

4. Model Evaluation and Refinement: Test accuracy against the agreed metrics and a naïve baseline, examine feature importance to provide transparent explanations, and refine data and model parameters until performance is robust.

5. Implementation and Ongoing Optimisation: Deploy the model via automated data pipelines, surface live forecasts in Power BI dashboards to inform decisions, and monitor, retrain and audit regularly to safeguard accuracy and trust.

Evidence-Based Approach: Our methodology is grounded in peer-reviewed research from leading institutions, ensuring scientific rigour meets practical application.

Why Choose Causality?