Data Science & Machine Learning
Know what's coming, before it happens
Find out which customers are about to leave, how much you will sell, and which payments look risky, using your own past data.
Most businesses already have the data to answer their hardest questions. We use it to build models: programs that learn from your past records and predict what comes next. The result is something your team can act on, such as a weekly list of customers likely to leave, a sales forecast your buyers trust, or a risk score on every application.
Sound familiar?
- You find out a customer has left only after they cancel.
- Stock-outs and overstock eat margin because forecasts live in spreadsheets.
- Marketing sends every customer the same message because nobody has sorted them into groups.
- Fraud or bad debt is caught by hand, late and inconsistently.
What we build
What this covers
Predictive analytics
Estimating an outcome before it happens, from whether a lead will buy to whether an invoice will be paid late.
Predicting which customers will leave
A ranked list of customers likely to leave (often called churn), with the reasons, updated on a schedule.
Customer segmentation
Groups built from real behaviour, so offers and messages fit each group.
Forecasting
Forecasts of demand, revenue and workload, with an honest range of how high or low it could go.
Fraud detection and risk scoring
Scores that flag unusual transactions or risky applications for review.
Recommendation systems
Product and content recommendations from purchase and browsing history.
Custom prediction models
For any yes/no or how-much question your business asks often, such as "will this order be returned?" or "how many staff do we need on Friday?"
Turning predictions into action
Rules, alerts and dashboards that put each prediction in front of the person who acts on it.
Try it
An example of what you'd get
Each morning your team opens one screen: who is likely to leave, what will sell, and what stock is running low.
Switch between the views and move along the chart. The numbers are made-up example data, not a client's.
What you're likely to sell in the coming weeks, with a range to plan around.
Hover or tap the chart to see each week. With the keyboard, focus the chart and use the arrow keys.
What you get
What we hand over
- A model trained and tested on your data
- A written evaluation in plain language
- Scores delivered where your team works (a dashboard, your CRM, a spreadsheet, or straight into your app)
- Code and documentation you own
Technology
Tools we use
You don't need to know these. We pick what fits the tools and budget you already have.
Pythonscikit-learnXGBoostPyTorchSQLMLflowAWS / Azure / GCP
How it works
Three steps, no tech knowledge needed
- 1
Talk to us
A free 30-minute call. Tell us the problem in your own words; we'll say honestly whether your data can help.
- 2
We build it
First a small trial on your real data, usually two to four weeks. If it works, we build the full version.
- 3
You use it
The result shows up in tools your team already uses, and everything we build is yours to keep.
Solutions
Specific problems we solve
Know what's likely to happen, and act first
Your historical data already shows patterns: which customers drift away, when demand peaks, which accounts pay late.
Customer segmentation built from what customers actually do
Age and location say little about what a customer will buy next.
Machine learning development, from first test to daily use
A model is only useful once it works reliably on new data.
Data science advice that ends in something that works
Many consulting projects end in a slide deck.
Have a problem your data could help with?
Tell us about it. The first 30-minute call is free, we'll tell you honestly what's possible, and there's no obligation.