Event forecast
Using AI with complex mathematical models, it is possible to predict with high probability the occurrence of an event.
Predictive maintenance
Machine learning models build patterns of anomalies that warn of failures over time.
Predictive customer support
Using the supporting data, patterns are found that allow developing solutions to problems and predicting future complaints/claims.
Predictive manufacturing design
Helps designers determine which combinations of variables are most likely to produce a positive result on their projects.
MoA Prediction
Predict the molecular mechanism underlying phenotypic screens and follow-up assays.
Adverse effects predictor (medicines)
Based on clinical data, it helps determine the adverse effects that someone may have from a medication or treatment.
Predict out-of-stock risk
Stockouts of business-critical inventory can be avoided using data captured at the site where the inventory moves.
Disease predictor (public health)
Machine learning models trained on health information exchange data in a geographic area can predict the probability that a person or groups of people will acquire a specific disease.
Customer churn prediction
Based on metadata and behavioral patterns, with AI it is possible predict if a customer is going to churn.
Prediction of the factors that contribute to a topic under study
By identifying the behavior of agents through data analysis, potential risks can be determined.
Predicting readmission (hospital)
It can find reasons that lead to a patient’s readmission to a hospital and provide recommendations on the types of treatment that are most likely to be successful.
Failure prediction in production processes
Based on the data of the production processes, it is possible to significantly reduce production failures.
Demand forecast
Through forecast models and using data from inventory movement, it is possible to forecast demand.
