Machine Learning
Turn your data into predictions you can act on
We build practical machine learning models — forecasting, scoring and anomaly detection — that plug into your existing tools and decisions.
The problem
Most businesses sit on data they can't use
Years of sales, operations and customer data hold patterns that could guide decisions — but the data is scattered, messy or only looked at in hindsight.
- Forecasts rely on gut feel and last year's numbers
- Problems are spotted after they cost money
- Data is spread across systems and inconsistent
What we deliver
Everything you need, from one team
Demand & sales forecasting
Predict demand, revenue or workload to plan stock and staffing.
Scoring & prioritisation
Lead, churn and risk scores that rank what needs attention first.
Anomaly detection
Spot unusual transactions, readings or behaviour as it happens.
Data preparation
Clean, joined and structured data that models — and reports — can trust.
Deployment & integration
Models served via APIs and surfaced in your CRM, ERP or dashboards.
Monitoring & retraining
Accuracy tracked over time, with models retrained as your data changes.
How it works
A clear path from idea to results
- 1
Step 1: Data assessment
We audit your data and the decision you want to improve.
- 2
Step 2: Proof of value
A quick model on historical data tests accuracy before you invest further.
- 3
Step 3: Build & integrate
A production pipeline, connected to where decisions are made.
- 4
Step 4: Monitor & improve
Ongoing accuracy tracking, retraining and new use cases.
What this looks like in practice
Predictive analytics and ML models that surface the insights hidden in your data.
Stock planned on forecasts, not guesses
Demand forecasts by product and location help you order the right amount at the right time.
At-risk customers flagged early
A churn model highlights accounts that need attention, so your team can act before they leave.
Unusual activity caught as it happens
Anomaly detection alerts you to errors or fraud the same day — not at month-end.
Illustrative examples of the kind of results this service is designed to deliver.
Technologies we use
- Python
- pandas
- scikit-learn
- TensorFlow
- PyTorch
- Microsoft Azure
- AWS
- PostgreSQL
FAQ
Common questions
How much data do we need?
Often less than you might think — for many forecasting and scoring problems, one to two years of history is a good start. We assess your data first and tell you honestly whether machine learning is the right tool.
How accurate will the model be?
It depends on your data and the problem. We agree a success measure up front and test it on historical data in a proof of value before you commit to a full build.
Can we see why the model makes a prediction?
Yes. We favour explainable approaches and show which factors drive each prediction, so your team can trust — and challenge — the results.
Where does our data go?
It stays in your own cloud environment wherever possible, with access limited to the people who need it.
Related services
Where is your team losing time?
Tell us what you want to build or fix. We'll come back with practical next steps and an honest view of cost and timeline.