Machine learning that learns from your own data.
A custom machine learning model learns patterns from your past data and uses them to make a decision or a prediction, such as which leads will convert, how many orders to expect next week or which transactions look unusual. We prepare the data, train and test the model against your real cases, and run it in production with monitoring, so you know how well it performs over time.
What is a custom machine learning model?
A machine learning model is software that learns a decision from examples instead of hand-written rules. Given enough past cases, such as leads that did or did not convert, it learns which signals matter and applies them to new cases in milliseconds.
What you get
- A review of your data and a clear answer on whether machine learning fits
- Data cleaning and preparation, with the steps documented
- A trained model, tested on data it has never seen, against an accuracy target you approve
- Deployment as an API or inside your existing tools, such as your CRM or dashboard
- Monitoring for accuracy and data drift, with retraining when results slip
What it can do
- Classification. Score leads, sort tickets, flag risky transactions or detect spam.
- Forecasting. Predict demand, sales, call volume or stock needs.
- Recommendations. Suggest the next product, article or action for each customer.
- Anomaly detection. Spot unusual orders, payments or sensor readings early.
Who it is for
Teams with structured data and a decision they make again and again, where fixed rules miss too much and a wrong call has a real cost.
How it works
- We define the question and how success will be measured.
- We review and prepare your data, and confirm the problem is learnable.
- We train and compare models, then test the best one on held-out data.
- We deploy it with monitoring and retrain it as your data changes.
Not sure machine learning is the right tool? Read when a business problem needs machine learning, and when it does not.
Honest limits
A model is only as good as the data behind it. If your history is small, messy or does not contain the answer you want to predict, we will say so and suggest rules, process automation or collecting better data first.
Questions about Machine learning models
How much data do we need?
It depends on the problem. A simple classifier can work with a few thousand labelled examples, while forecasting needs enough history to cover your seasons. We check your data first and tell you plainly if there is not enough yet.
How is this different from a large language model?
Classic machine learning learns from your structured data, such as records, numbers and categories, and is small, fast and cheap to run. Language models suit free text, speech and open questions. We often combine them, and we recommend whichever is simpler for the job.
How do we know the model is right?
We hold back part of your data the model never sees, test it there, and agree the accuracy target with you before launch. In production we track its results and retrain when the data changes.
Who owns the model and the data?
You do. The data, the trained model and the code belong to you, and your data is never used to train models for anyone else.
See if it fits your business.
Bring one real lead or question your team handled this week. We will show you what this would do with it.