01
Data & goal
We audit your data and define which decision we want to improve.
dataPredictive analytics
Predictive analytics
to decide ahead of time
Models that anticipate demand, detect anomalies and turn your historical data into business decisions weeks ahead.
What it is
Get ahead of your market,
don't chase the data.
Your business already generates the data it needs to predict. What's missing is turning it into decisions ahead of time: how much stock to order, which customers are about to leave, which machine will fail.
We build custom predictive models — from demand forecasting and customer churn to anomaly detection and predictive maintenance — and deliver them in actionable dashboards or integrated into your processes.
- ✓Demand forecastingPredicts sales by SKU, category or store with weeks of lead time.
- ✓Churn & LTVIdentifies which customers are about to leave and how much the ones who stay are worth.
- ✓Anomaly detectionFlags when something doesn't add up: fraud, failures, leaks.
- ✓Predictive maintenanceAnticipates breakdowns and plans interventions before the stoppage.
How we do it
From idea to deploy,
in 4 steps
A lightweight, iterative process with tangible deliverables from the very first week.
02
Modeling
We train several models and pick the one with the best accuracy and robustness.
03
Integration
We connect it to your dashboard, ERP or alerting system.
04
Continuous improvement
We retrain with new data and monitor performance.
Impact
Results you can measure
Reference figures from similar projects. We validate yours in the first phase.
+22%
Margin per decision
Better pricing, better stock, better investment.
−30%
Idle stock
You order exactly what will sell.
94%
Typical accuracy
Models validated before going to production.
Use cases
Real applications by industry
Retail
Stock forecasting
Predicts demand by store and SKU with weather and promo data.
SaaS
Churn prediction
Identifies at-risk accounts 60 days ahead.
Banking
Credit risk
Fine scoring on new applicants with alternative data.
Industry
Maintenance
Warns about the machine about to fail before the line stops.
Insurance
Fraud detection
Flags anomalous patterns in claims and underwriting.
Marketing
Propensity modeling
Identifies which customers will respond best to each campaign.
Stack
Technologies we use
We pick tools to fit the project, not the other way around. These are the ones we reach for most.
Python · scikit-learnPyTorch · XGBoostProphet · LightGBMSnowflake · BigQueryMetabase · Power BIMLflow · AirflowAWS SageMaker · GCP
FAQ
Frequently asked questions
How much data do I need?
It depends on the problem. For forecasting, 12–18 months of history is usually enough, but we analyze your case first.
What if my data is messy?
That's the norm. Part of the project is always ordering, cleaning and enriching the data before modeling.
Who interprets the predictions?
We deliver them in clear dashboards for non-technical users, and we train the team to act on them.
How do I know the model is reliable?
We validate it on past data before deploying and monitor its accuracy every month.
Can it integrate with my ERP?
Yes. Predictions can be pushed as alerts, columns or actions within your current system.
Next step
Decide sooner,
not later.
We propose a first predictive model on your real data and validate the ROI before scaling. Shall we talk?
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