Forecasting, machine learning and statistical models that help you anticipate demand, understand customers and make evidence-based decisions.
Historical reporting is essential, but on its own it keeps decisions reactive. Prediction turns the same data into a view of what’s likely ahead.
Stock, staffing and budgets are set from last year’s numbers plus a guess.
Customers leave and demand shifts before anyone notices the warning signs.
Every customer gets the same offer, even though their needs and value differ widely.
Pricing and campaign changes go live without evidence of what actually works.
Models don’t replace judgement. They give your team a clearer, quantified view of what’s likely, so decisions rest on evidence.
Forecasts with a clear range of likely outcomes, not a single number to hope for.
Stock, staff and budgets set from forecasts rather than last year plus a guess.
Churn, demand shifts and risks flagged while there’s still time to act.
Offers and service matched to customer segments, not a single average customer.
Tested changes to pricing and campaigns, with results you can measure.
We pick the simplest model that solves the problem well, and explain how it works in plain language.
Predict future sales, demand and business requirements.
Develop models for classification, regression and pattern detection.
Group customers based on behaviour and business characteristics.
Build personalised product, content and service recommendations.
Apply statistical techniques for hypothesis testing and optimisation.
No black-box magic. You see how a model performs, why it makes its predictions and how to use them day to day.
Examples of the kind of work this vertical covers. Every project is shaped around your data and the decision it supports.
Better stock decisions, with fewer stock-outs and less excess inventory.
Identify customers likely to leave, so relationship teams can step in first.
Forecast orders to plan purchasing and production capacity with more confidence.
Rank incoming leads by likelihood to convert, so effort goes where it counts.
We prove value early with a simple baseline, then improve only where the extra accuracy is worth it.
Define the decision, the prediction that supports it and how success is measured.
Assess your data, find the patterns and set a simple baseline to beat.
Build and compare candidate models, keeping the simplest one that performs well.
Test on unseen periods and review results and drivers with your team.
Deliver predictions into your workflow and agree how the model is monitored.
Every engagement is fixed in scope and quoted after a short discovery, so you know what you’re getting before work begins.
Find out whether your data can support the prediction you need.
Best for: exploring whether prediction fitsOne model for one decision, tested on your real data.
Best for: a specific, high-value questionA model delivering predictions in your workflow, with monitoring.
Best for: scaling a proven use caseWe favour established, widely used libraries, so your models are easy to maintain and not tied to one vendor.
It depends on the problem. Forecasting usually needs enough history to show seasonal patterns, often a couple of years. Other models can work with less. A short readiness check gives you a clear answer.
We can’t promise a number before seeing your data. We always measure accuracy on data the model hasn’t seen and compare it with a simple baseline, so you can judge the real improvement.
Yes. We explain the main drivers behind the predictions in plain language and prefer simpler, explainable models wherever they perform well.
We’ll tell you early. Often the first step is a clean, connected dataset, which is what our Data Engineering work provides.
Tell us the decision you want to improve. We’ll tell you honestly whether prediction can help, and where to start.