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.

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.

Automated customer service phone calls and chatbots are becoming increasingly easy to interact with. However, the frustration associated with bad experiences can have a significant impact on customer retention. Deep learning models such as natural language processing (NLP) are ideal for gaining insight into the user experience in customer service interactions.

”Propensity to buy” is the likelihood of a customer to purchase a particular product. Successful propensity to buy models are key to being able to fully optimize how to design and distribute marketing material as well as allocate sales staff time.

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.