Machine learning & predictive models
Models that forecast demand, risk or churn from your history, deployed to production and monitored.
A predictive model only pays off when it leaves the data scientist's notebook and feeds a real decision: how much to buy, which customer to call first, which order to review. We prepare the data, train and compare models, deploy the chosen one with an automated pipeline and track whether accuracy holds over time.
Benefits
- Demand, risk or churn forecasts based on your history
- A model tied to a concrete business decision
- Automated retraining when new data arrives
- Accuracy monitoring after the model goes live
- Explanations of which variables drive each prediction
- Cloud infrastructure that scales with volume
Process
Collection & preparation
Consolidating and cleaning data from several sources.
Modeling & training
Comparing models against metrics agreed before training.
Deployment & automation
A production model with automated data and retraining pipelines.
Pillars
Predictive analytics
Patterns in historical data that help anticipate demand and risk.
Data modeling
Features and datasets prepared so the model doesn't learn noise.
Automation
Predictions delivered straight into the system where decisions happen.
Continuous improvement
Retraining and monitoring so the model doesn't go stale.
Frequently asked questions
What can a machine learning model predict for my company?
- The models forecast demand, risk or churn from your history, always tied to a concrete decision: how much to buy, which customer to call first, which order to review. Each prediction comes with an explanation of which variables drove it and is delivered straight into the system where the decision happens.
How is a predictive model deployed to production?
- The chosen model goes into production with automated data and retraining pipelines, on cloud infrastructure that scales with volume. Before that, data is consolidated and cleaned and models are compared against metrics agreed before training; afterwards, accuracy is monitored so the model does not go stale.
What are the stages of a machine learning project?
- There are three: collection and preparation, consolidating and cleaning data from several sources; modeling and training, comparing models against metrics agreed before training; and deployment and automation, with the model in production and automated retraining. Follow-up continues afterwards, with accuracy monitoring as new data arrives.
Related content
- Data lake, data warehouse or lakehouse: how to decide in 2026With open table formats like Apache Iceberg, lake and warehouse are no longer an either-or choice. The decision now comes down to which answers the business needs every week, who will query the data, and how you'd rather pay for it.
- Why so many machine learning models never reach productionThe model works in the notebook and dies before it becomes part of a process. The research points to the same places: a poorly defined problem, production data that doesn't match training data, and nobody owning the model after launch.
- AI in retail: personalization and demand forecasting without overstockingIn retail, the AI that pays for itself first improves product data and forecasts demand per SKU. Personalization comes later, once there is enough purchase history. A 2,000-listing catalog rewritten with an LLM shows where to start.
- Predictive maintenance with AI in the automotive sector: where to startPredictive maintenance starts with the data a fleet, a shop or a line already generates, not with new sensors. Pick one component that is expensive when it stops, measure the history and test a model in a short cycle.
Why Cortex IQ
2009
the founder's first IT project
10
published cases, with the client's name
4
partner clouds: AWS, Google Cloud, Microsoft and Oracle
3
working languages: Portuguese, English and Spanish
Partner clouds
