Use cases

Advances in deep learning and machine vision allow for automated defect detection with improved speed and accuracy. This is an example of the type of problem that can be solved using the Peltarion Platform.

Deep learning models with Natural Language Processing (NLP) are great for gaining insight into the user experience in customer service.

In virtually every decision they make, executives today consider some kind of forecast. The use of deep learning to predict sales allows for more complicated input data to gain deeper insight and increase accuracy.

”Propensity to buy” means how likely a customer is to purchase a product, which is key to optimize how to design and distribute marketing material and allocate resources.

Detecting fraud can be very challenging in a dynamic global business environment and with an overwhelming amount of traffic and data to monitor. Fraud detection is an ideal use case for machine learning with plenty of past success in many industries like payments and insurance.

Using AI for dynamic pricing is an opportunity to turn a complex business environment into an advantage, while retaining appropriate profit margins on all products. This is especially true for products with complex pricing models and high levels of individual customization such as insurance.

A key part of smart manufacturing and a modern factory approach involves real-time monitoring of machinery operating conditions. This can be done using audio recordings of in-service machinery on models created using the Peltarion Platform.