<aside> 🔎
*For more information on AI Tagging at EdgeTier, visit our existing documentation*
</aside>
AI Tagging (or AutoTagging) is a large-language-model approach to automatically applying labels to all interactions in EdgeTier. AI Tagging is designed to transform how customer interactions are classified, making tagging more precise and less time-consuming. Built using a Large Language Model (LLM), this feature ensures that every interaction is assigned a tag from a predefined list. This happens automatically and consistently.
In plain terms, EdgeTier’s AI tags every interaction’s Contact Reason for you, without needing to set-up any phrase tags or triggers.
AI Tagging has been in Beta. This feature was initially developed to provide a structured, automated way to categorise entire interactions without the need for manual selection or complex trigger setups. This has been implemented through collaboration with EdgeTier’s team, by defining your categorisation goals, and the team creating specialised tagging models. You can see these tags on your interactions across EdgeTier, but you cannot visualise them in any dedicated page.
We’ve now added an interface to allow you to visualise your AI tags within the EdgeTier platform. Here, you’ll be able to view how you’ve chosen to define your Contact Reason AI Tags with the EdgeTier team, as well as the tags performance. This page is a home for your Contact Reasons, and a first step to full self-service AI tagging in the interface.
Undefined Topics has it’s own tab within Contact Reasons, so you can quickly see if your existing AI tags are capturing your interactions, or if a large volume are undefined.
