The problem: the data exists, but nobody uses it to decide
Most companies already generate the data they'd need to predict demand, prioritize customers or anticipate a problem — sales history, maintenance logs, support tickets. The problem isn't a lack of data: it's that it lives in separate systems, with no model turning it into a useful prediction for whoever needs to decide today.
How we solve it
- Defining the predictive target: what we want to predict or prioritize, and what business decision depends on that prediction — without this, a model is an academic exercise.
- Auditing available data: what you have, what state it's in, and what's missing. Most ML projects fail here, not in the model.
- Training and validation: we test the model against data it hasn't seen, with clear error metrics — not just against the data it was trained on.
- Integration into the decision flow: the prediction reaches the person deciding where they already work (dashboard, CRM, spreadsheet), not in one more isolated tool.
Expected result
The result depends entirely on the quality of available data and how well-defined the problem is — we assess this honestly during diagnosis, including telling you if your data isn't ready yet for a reliable model. We don't quote a generic return for this service because, unlike Spray Analyzer, we don't yet have a documented predictive machine learning case with our own figures — and we'd rather tell you that than make one up.
Technology stack we use
Python + scikit-learn
Classic predictive models — regression, classification, time series — when they're sufficient and more explainable than a deep neural network.
PyTorch / TensorFlow
When the problem requires deeper neural networks: complex patterns, large volumes of unstructured data.
Pandas / SQL
The least glamorous, most decisive part: preparing and cleaning data before training anything.
AWS / Azure / Google Cloud
Deploying the trained model as a service, integrated with your existing systems.
Methodology
Data assessment is, for this service, the most critical of the five phases. See full methodology →
Related services
Custom machine learning is often combined with AI process automation (to act on the prediction) and backend development (to serve the model reliably in production).
Frequently asked questions
How much data do I need to train a model?
It depends on the problem. Some models work with a few hundred examples if the pattern is clear; others need thousands. During the assessment we evaluate what you have and, if it's insufficient, design a data-capture plan before committing to a final model.
What if my data is messy or incomplete?
That's the common situation, not the exception. Part of Phase 2 of our methodology is precisely assessing the real quality of the data and defining what cleanup or capture process is needed before training anything.
Does the model keep working well over time?
Models degrade when the process they describe changes — what we call data drift. That's why Phase 5, continuous monitoring, isn't optional: we review real-world performance and retrain when needed.