Traditional business consulting tells you what might improve based on best practices. Causal Modelling tells you why things work in your specific conditions and what actions will deliver measurable results. It’s the difference between correlation and causation in service business improvement.

We have found that UK SMEs have plenty of dashboards, but are measuring the wrong thing. Classical analytics cannot tell the future and misses everything about causes – the WHY. Analytics software excels at finding patterns but struggles with complex service industry knowledge and causal reasoning. Causal modelling means understanding client behaviour, team dynamics, market conditions, and operational constraints that human expertise provides.

That’s the beauty of causal business analytics applications—we can work with limited data by incorporating domain knowledge and industry constraints. We also pull in meta data such as the wider economic trends that may have an impact on your bottom line. Traditional analytics needs massive datasets; we can provide insights even for emerging UK service models and market conditions.

Initial insights often emerge quickly. We start with a baseline which takes 6-8 weeks per project. However optimised outcomes and hypotheses start to emerge almost immediately. For our first B2B Service based SME action happened within 2 weeks which impacted profitability straight away. Full optimisation is typically realised over 3-12 months as we refine models based on real-world outcomes and feedback loops. In other words we plan and hypothesise, we action together, and we review results.

Our performance optimisation models explicitly incorporate wider market variables and can simulate different business scenarios, helping you prepare for multiple futures rather than extrapolating from past performance alone. For example what might happen with an economic downturn or if we increased marketing or training spend.

Our causal models environmental investments explicitly incorporate UK policy variables and can simulate different regulatory scenarios, helping you prepare for multiple futures including Nature Recovery Network developments rather than extrapolating from the past.

We focus on £1M+ revenue UK SMEs with substantial enough operations to generate meaningful ROI from optimisation—typically £200K+ annual operational costs across service delivery, client management, and business development.

We establish baseline measurements before intervention and track improvements through agreed KPIs relevant to UK service markets. ROI is achieved over the year through increased revenue, optimisation gains and improved operational efficiency.

Yes, we integrate with existing CRM, ERP, project management, and bespoke service delivery systems. Our approach enhances rather than replaces your current business intelligence capabilities.

Ai can help on many levels – most importantly when it comes to summarising huge data and speeding up repetitive tasks. But Ai will not be able to tell you the Why – the causes. Instead it is great at looking at and recognising patterns (such as what word I am likely to type next). We use AI for deep research, to clean up and interrogate data, and to speed up our process and delivery. The causal algorithms we use are actually just maths based on open-source libraries.

A great question. Causal Modelling has been around since the early 1990s and is a way to augment classical statistical models to answer the why. Many people who use dashboards and analytics every day are not aware that correlation does not equal causation. As of now causal modelling and DAGs has only surfaced in academic circles and in some huge, forward thinking companies such as Uber, Google or IBM. Causal methods are now coming to the fore in many sectors, but are critical to the improvements in robotics and Ai.