Uniphore Boosts Deep Learning AI for Agent Assistance
Source – https://www.nojitter.com/
Deep learning AI models will provide more accurate call summaries and AI-based after-call work guidance.
Conversational service automation platform provider Uniphore today announced the addition of deep learning AI models and other updates for U-Assist, its agent tool for automating after-call work and call dispositions. Available to all Uniphore customers in the fall release, the U-Assist update provides:
- Interaction sectioning — By applying deep learning AI models to customer engagements, U-Assist will provide agents assistance in real-time, then during the wrap-up phase of a call automatically deliver a call summary and follow-ups. Using the AI to create the call summaries will improve accuracy compared to summaries prepared by agents based on their recollections of the conversation, Uniphore said.
- Intent detection —With the update, Uniphore is transitioning from the use of natural language processing to deep learning AI algorithms for its intention detection feature, with the aim of improving sentiment analysis over time, a Uniphore spokesperson said. To detect intent, the models will analyze what stage calls are at, customer sentiment, how agents are resolving the issues, whether coaching is being followed, and other factors.
- AI-based supervisor alerts — With this feature, supervisors will automatically receive alerts when agents may need support for their customer engagements. The alerts will detail the call type, customer sentiment, escalations, and agent verification, Uniphore said.
- Self-optimizing after-call work — Deep learning AI models will learn from edits, additions, and insights that agents make to the auto-generated summaries, refining what’s captured in future calls.
This latter point is particularly important, industry analyst Zeus Kerravala, of ZK Research, noted in an email exchange with No Jitter. “AI isn’t a one-time deployment; it’s an ongoing journey where the more data that is created helps improve the accuracy of the models,” and therefore the ability to streamline and improve the customer experience, he said.