Neural network
A neural network is a machine learning model made of layered, connected units that learn to map inputs to outputs by adjusting weighted connections.
Neural network is a type of machine learning model loosely inspired by how neurons connect in the brain. It is built from layers of simple units, where each connection carries a weight, and the network learns by adjusting those weights so its outputs match the examples it is trained on.
Networks with many layers are called deep neural networks, and they are the foundation of modern AI. They are good at finding patterns that are hard to describe with explicit rules, such as recognizing the meaning of a sentence or the content of an image.
In context
In customer support, neural networks sit beneath the tools that read and understand messages. They let a system pick up on the meaning of a request rather than just matching exact keywords, so a customer can phrase a question many different ways and still reach the right answer.
The same models help with tasks like detecting sentiment, grouping similar tickets, and predicting which conversations are likely to need a human. Because they learn from examples, they adapt to the specific language and issues a business sees.
How Auralis uses Neural network
The neural network models behind Auralis are what let its done-for-you service understand support requests by meaning rather than keywords, which is part of how it reaches 74% deflection in repetitive ticket categories.