Knowing where to start with using AI to solve real value-adding business problems can be a difficult challenge. Here is a collection of tangible business use cases which can be solved using the Peltarion Platform.
AI enabled document search for law practices
By leveraging modern NLP techniques such as text similarity, law practices can meet the need for quickly and efficiently searching through vast amounts of legal documentation.
Using AI for item valuation in the insurance industry
By leveraging deep learning in valuation processes, insurance companies can efficiently and instantly analyze images, details provided and combine those with historical data of past valuations.
Detecting Damage After Natural Disasters
Following a natural disaster, one of the most critical tasks is to determine the priority areas for emergency response teams. In this use case, we look at how deep learning could be leveraged for quickly identifying severely affected areas.
Predictive maintenance in manufacturing processes
A key part of smart manufacturing is monitoring machinery operating conditions. In this use case we discuss how AI models can automate monitoring tasks, resulting in reduced downtime and improved production quality.
Identify Upsell opportunities with AI
Businesses have limited resources and knowing where to place your bets is key to using resources efficiently. In this case, we will go into detail on how to build your own upselling model on the Peltarion platform.
Building intelligent recommendation engines
Better search functionality on your website is one way to help but unfortunately customers don’t have the time to dig through search results or create elaborate filters. But there is another way.
Automated defect detection
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.
Better customer experience with sentiment analysis
Deep learning models with Natural Language Processing (NLP) are great for gaining insight into the user experience in customer service.
Using AI for sales forecasting
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.
AI in marketing & sales: Propensity to buy
”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.
Using AI to detect fraud
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.
AI for customized dynamic pricing
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.