Applied AI and Machine Learning
Models that earn their place: demand and revenue forecasting, basket and churn analysis, anomaly detection and document automation, built on data you already have. With a return you can measure.
Why it matters
Mid-market businesses already hold the data to predict, not only report
You do not need a new data science team or a new platform to get value from machine learning. Transactional, finance and BI data - the data you already collect and store - is enough to start predicting, not just reporting, on what matters most.
We find one use case where prediction changes a real decision, build it on your data, prove value first, and then put it into production on Fabric with results feeding into the tools your team already uses.
How we approach it
Find the right use case
Short discovery to identify the use case with the clearest payback from your data.
Estimate the return
We size the return before building anything. You decide whether to proceed.
Prove value at small scale
Model built and tested on your data. No commitment to scale until it works.
Deploy and integrate
Into production on Fabric, with results in Power BI or your existing tools.
Monitor and improve
Ongoing accuracy monitoring so the model stays useful as your data changes.
Where it applies
Five use cases we deliver
All built on data you already hold. All deployed on Microsoft Fabric with results in Power BI.
Demand and revenue forecasting
Predict what you will sell, when, and where - so you carry the right stock, staff the right shifts, and set realistic targets. Built on your historical transactional data.
Cuts stockouts, reduces overstock, improves planning accuracy.
Basket and affinity analysis
Understand what customers buy together, what they buy next, and what they are likely to buy in the coming weeks. Works on any transactional dataset.
Lifts average order value. Informs ranging and promotional decisions.
Customer churn, RFM and reactivation
Identify customers who are at risk of leaving before they go, segment your base by recency, frequency and value, and prioritise reactivation effort where it will have most impact.
Holds revenue. Focuses sales and marketing effort on the right accounts.
Anomaly detection
Automatically flag transactions, figures or patterns that fall outside expected bounds - catching errors, fraud signals and data quality issues before they reach a report.
Catches errors early. Reduces time spent on manual reconciliation.
Document and process automation
Extract structured data from unstructured documents - invoices, contracts, forms - and route it into your systems automatically, reducing manual handling.
Removes repetitive manual work from your team's day.
In practice
Manufacturing and Distribution
Migrated the existing sales analytics estate into Azure, then added basket analysis, RFM, churn, order frequency, and diagnostic purchase logic on top - all built on data the client already held.
The programme started as a 60-day engagement and moved into a 24-month ongoing rhythm. Machine learning capabilities were added incrementally as the data foundation matured, giving the client models they could trust because the data underneath them was sound.
See the full case studyAlso explore
AI-Ready Data Foundations
The data layer that makes machine learning trustworthy. Models are only as good as what they are built on.
ServiceMicrosoft Fabric
Build a governed, scalable data platform that gives your ML models reliable, clean data to work from.
ServiceAnalytics Acceleration Programme
The structured programme that governs how we deliver, including any applied AI work.
GuideGetting Your Data Ready for AI
The practical steps to get your data estate into a state where AI can do useful work.
ToolAI Data Readiness Checker
8 questions to find out whether your data is ready for AI - and what to do if it is not.
AIMicrosoft AI and Copilot
Plain-English querying and Copilot in Power BI - once the semantic model is ready to support it.
Common questions
Questions about applied AI
Do we need a data scientist or new tools?
No. We build on your data and the Microsoft tools you already have - Fabric, Azure, Power BI. You do not need to hire a data scientist or buy a new platform. The models run in your environment and the results come back into the tools your team already uses.
How do we know it will pay off?
We estimate the return before building anything. We identify the use case with the clearest payback, size the return based on your actual data, and prove value at small scale before committing to a full build. You decide whether to proceed with the full picture in front of you.
What data do we need?
Usually less than people think. Transactional data, finance data and your existing BI data is typically enough to get started. We look at what you have in the first call and tell you honestly whether it is sufficient.
How long does it take to see results?
A focused first use case - demand forecasting or basket analysis, for example - can be in production within a few weeks of starting. We are deliberate about starting small: one use case, proven value, then scale. Not everything at once.
Useful models, built on data you already hold
We estimate the return before building anything. Book a free analytics audit and we will identify the use case with the clearest payback from your data.
Book a free analytics audit