Machine Learning Development
Machine learning development
Prediction and classification models trained on your own historical data — sales, orders, payments, returns.
The problem
You have years of data sitting in your ERP or accounting system, and you know it should tell you something useful — which orders will be paid late, which products will move next month, which customers are about to stop buying. Right now that knowledge lives in the gut of whoever has been around long enough to remember the patterns. When they leave, it leaves with them. A machine learning model reads the same history and makes the same call systematically, for every row, at any scale.
Your historical data starts making forward-looking decisions.
Built for: Businesses with at least a year of clean transactional data and a specific prediction to make.
What we deliver
Data quality check first. Before any modelling, we look at your historical data for gaps, duplicates and inconsistencies. A model is only as reliable as the history it learned from. If your data needs cleaning first, we tell you that before the build budget starts.
The right kind of model. Prediction, classification, ranking — we use the approach that fits the problem, not the most complex one available. Simpler models are easier to explain to your team and easier to fix when they are wrong.
An honest accuracy baseline. We measure the model against your real data, show you where it is wrong, and agree a minimum acceptable accuracy before we call it ready to use. We do not quote accuracy figures before seeing your data.
Explainable outputs. Your team needs to trust the predictions enough to act on them. We build outputs your ops or finance team can understand: a score, a category, a flag — not a black box.
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Frequently asked questions
- How much data do we need?
- It varies by problem, but for most business predictions you need at least a year of consistent history with minimal gaps. Seasonal businesses need more. If you have less, we will tell you — a model trained on too little data gives you false confidence, which is worse than no model.
- How is this different from the analytics already in our ERP?
- Your ERP reports on what happened. A machine learning model estimates what will happen. These are different jobs. The model learns from patterns in your history — which orders came in late, which customers churned, which SKUs ran out — and applies those patterns to current data.
- What if the model's predictions are wrong?
- Every model is wrong sometimes. The question is how often and how badly. We set a threshold your business can tolerate before we go live, measure it on real data, and monitor it after launch so you know when accuracy drops before your team feels it.