Data & artificial intelligence / Machine learning
Models that learn
from your data.
Custom machine learning, trained on your data and shipped to production: classification, recommendation, anomaly detection and forecasting. Models that decide at scale, not lab experiments that never get used.
What it is (and what it isn’t)
Learning patterns,
not following fixed rules.
Machine learning lets a program learn patterns from data instead of following rules written by hand. Rather than programming every possible case, we show it examples and the model learns to predict, classify or decide on data it has never seen before.
It is not magic, nor an artificial brain. A model is a statistical system that finds relationships in your historical data and applies them to new cases. It is only as good as the data it is trained on: if that data is incomplete or biased, the model will be too. That is why we always start from the problem and the data, never from the technology.
Machine learning is one of the techniques withinartificial intelligence, and it is also the engine behind predictive analytics: whenever a prediction islearned from data rather than calculated with a fixed formula, there is machine learning behind it. We build custom models, trained on your data and taken to production, not tests that stay in a slide deck.
Types of model
A different model
for each question.
Classification
Assigns each case to a category: spam or not, customer who will leave or stay, urgent or routine ticket. It answers the question “what type is this?”.
Regression
Predicts a number: a sale price, next week’s demand, the time left before a breakdown. It answers “how much?”.
Clustering
Finds natural groups in your data without predefined labels: customer segments, product families or behaviour patterns you had not defined.
Recommendation
Suggests the next product, piece of content or action based on each user’s behaviour and that of similar users.
Anomaly detection
Learns what “normal” looks like and flags whatever falls outside it: fraud, machine failures, suspicious transactions or access.
Time series
Models data ordered over time to forecast how it will evolve —sales, consumption, stock or workload— taking trend and seasonality into account.
From data to a model in production
A useful model is
one that is operated.
Training a model is only part of the job. Making it work reliably, month after month, takes a full cycle —what is known as MLOps:
- Data: collecting, cleaning and labelling representative, high-quality information
- Features: building the variables the model actually needs in order to learn
- Training: fitting the model to your history and choosing the right algorithm
- Validation: an honest test on data the model has never seen, with clear metrics
- Deployment: shipping to production as an API or service, integrated into your systems
- Monitoring: continuous oversight of accuracy, latency and drift
- Retraining: updating the model when your data or your business changes
- Versioning: version control of data, models and code so you can reproduce and roll back
Cases by sector
Where ML already
changes the business.
| Sector | Use case | Result |
|---|---|---|
| Retail & e-commerce | Product recommendation and demand forecasting | More cross-selling and fewer stockouts. |
| Banking & insurance | Fraud detection and risk scoring | Fraud caught earlier and more consistent credit decisions. |
| Manufacturing | Predictive maintenance from sensor data | Fewer unplanned stoppages and better use of equipment. |
| SaaS & subscription | Customer churn prediction | At-risk customers identified in time to act. |
| Services & support | Ticket classification and prioritisation | Faster automatic routing with auditable logic and human oversight. |
In one line
A model isn’t magic: it learns from the data you give it.
Benefits and outcomes
What a well-built
model delivers.
Decisions at scale
The model handles thousands of cases with consistent logic, without fatigue or human bottlenecks, and at the speed your operation needs.
Genuine personalisation
Recommendations and experiences tailored to each customer from their behaviour, rather than the same generic message for everyone.
Early detection
Fraud, breakdowns or churn are seen coming before they turn into an expensive problem. The model warns you while there is still room to act.
Improves over time
Unlike a fixed rule, a model can be retrained on new data and sharpen its accuracy as it learns from more cases.
An edge from your data
A model trained on your history learns what is specific to your business: something no generic tool or competitor has.
Traceability
Every prediction is logged and reproducible, which lets you audit why a decision was made and correct course when needed.
How we train with rigour
Honest accuracy,
not pretty numbers.
We never measure a model on the same data it was trained on. We split the data intotraining, validation and test, and hold back that last set to check how it behaves on cases it has never seen. If a result looks too good, it almost always means the model is memorising rather than learning: that is overfitting, and we catch it with cross-validation and honest metrics.
We choose the metric to fit the problem —detecting fraud is not the same as recommending products— and explain it plainly. We make sure the data is representative, watch for drift in production and prioritise explainability when the decision affects people. The goal is not a pretty number on a slide, but a model that holds up in the real world.
The CPPA method
From data to a model
in production.
Problem and data
We start with the decision you want to improve and audit the available data: is there enough, is it good quality, is it labelled? If it falls short, we say so before building anything.
Proof of concept
We train a first model and validate it with honest metrics on unseen data. We only continue if it clears a clear value threshold. A measurable pilot before investing at scale.
Production and integration
We take the model to production as an API or service, integrated into your systems and fed with up-to-date data, with tests, error handling and explainability from day one.
Monitoring and retraining
We watch accuracy and drift over time, measure the real impact against the baseline and retrain when data or the business changes. The model is maintained, not abandoned.
Examples
What it looks like
in practice.
Product recommender
- Purchase and browsing history
- Training on patterns of similar users
- Recommendation model
- Suggestions on web and email
- Sales measurement and retraining
Fraud detection
- Real-time transactions
- Model that learns what is normal
- Risk score per operation
- Alert and blocking of suspicious cases
- Human review and feedback
Ticket classification
- Incoming email or ticket
- Trained language model
- Classification by topic and urgency
- Automatic routing to the team
- Learning from the corrections
Demand forecasting
- Sales or consumption history
- Model with trend and seasonality
- Forecast by product and period
- Stock and purchasing adjustment
- Comparison with actuals and retraining
Risks and mitigation
Where ML fails
and how we avoid it.
Machine learning is not infallible. Knowing its common failure modes is what separates a model that holds up from one that disappoints in the first month:
Overfitting
The model memorises the training data and fails on new cases. We avoid it by splitting train/validation/test and using cross-validation: we always measure on data it has not seen.
Drift
The world changes and a model trained on old data loses accuracy. We monitor performance in production and retrain on fresh data when it starts to slip.
Bias in the data
If the history carries a bias, the model learns and amplifies it. We audit the data and the results by segment, and adjust to avoid unfair decisions.
Black box
A model that decides without explaining why is hard to trust and to audit. We favour explainable models and add interpretability and human oversight where it matters.
Scarce or poor-quality data
Without representative data no model works: the classic “garbage in, garbage out”. Sometimes the best solution is not ML, but fixing the data source first.
Unrealistic expectations
Expecting magic leads to projects that never reach production. We validate with a measurable proof of concept and are honest about what the model can and cannot do.
Frequently asked questions
Are machine learning, AI and predictive analytics the same thing?
How much data do I need to train a model?
Does the model become obsolete over time?
Is it a black box? Can I know why it decides what it decides?
How is a model taken to production?
How long does it take to see results?
Related services
Keep exploring.
Artificial intelligence
Machine learning is one of the techniques within AI. See how we apply artificial intelligence to your business with judgement, not because it is fashionable.
View →
Predictive analytics
When machine learning is used to anticipate demand, churn or fraud, we call it predictive analytics. Models that predict rather than guess.
View →
Data engineering
No model works without reliable data. Data engineering builds the pipelines and quality a good model is trained on.
View →What decision would you like a model to make for you?
Request a proposal →Want to see how we think about a business’s data end to end? Read ourCPPA X-RAY on 100 Montaditos.