Machine learning
Demand forecasts, churn and risk scores, price suggestions and anomaly alerts. Every model is tested against the rule your team uses today, and only ships if it clearly beats it.
Decisions still made on instinct.
- Ordering by gut feeling Stock is ordered from memory and last month's numbers, so some items run out while others sit for months.
- Customers leave without warning By the time someone notices a customer has stopped ordering, it is too late to win them back.
- Risky decisions made blind Credit, approvals and fraud checks rely on thresholds set years ago and never tested.
- Prices that guess the market Prices are set by habit, not by what similar items actually sell for this month.
Predictions people actually act on.
- Demand and stock forecasts Next weeks' sales per item and branch, including holidays and promotions, turned into a suggested order list.
- Churn and retention scores Customers ranked by how likely they are to stop buying, early enough for the sales team to call.
- Risk and fraud scoring Applications and transactions scored with the reasons shown, so a person can check the decision.
- Price suggestions Prices compared with similar items on the market, with a sensible range rather than a single magic number.
How a model earns its place.
- Write down today's rule How the decision is made now becomes the baseline every model has to beat.
- Collect and clean history Past sales, decisions and outcomes, checked for gaps, duplicates and columns that cannot be trusted.
- Test on unseen months Models are trained on the past and tested on later months, the way they will be used.
- Compare in money Results are reported as fewer stock-outs or fewer bad loans, not just accuracy percentages.
- Run side by side For a trial period the model's suggestions sit beside the current method, and the team sees which was closer before anything changes.
- Monitor and retrain Accuracy is tracked every week; when the business changes, the model is retrained.
What a model hand-over includes.
- Baseline and model compared in writing
- Predictions in a dashboard or your system
- Reasons shown for each score
- Weekly accuracy tracking
- Retraining script and schedule
- Plain-language report for managers
Questions
How quickly will we know if this is worth doing?
Within the first few weeks. A short feasibility phase measures today's rule and a first model on your data, and you decide whether to continue based on that result.
Where do the predictions show up?
Wherever your team already works: a dashboard, a daily email or WhatsApp summary, a column in your existing system, or an API another tool calls.
What happens when the business changes?
Accuracy is tracked every week. When it drifts, for example after a new product line or a price change, the model is retrained on recent data.
Can we keep the data on our own servers?
Yes. Training and predictions can run entirely on hardware you control, and nothing has to be sent to an outside service.