Uniphore, an early leader in Conversational Service Automation (CSA), today announced innovative new Artificial Intelligence (AI) enhancements to its portfolio of products. With these additions, the company continues to lead in providing new and exciting options for organizations to deliver transformational experiences throughout the entire engagement cycle – before, during and after contact is made.
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“These latest enhancements help our customers drive transformational experiences by delivering greater intelligence and recommendations through the application of deep learning AI models. I am extremely proud of the work our team has done to deliver these technology innovations for our customers.”
Announced today are enhancements to Uniphore’s U-Assist family that now include new deep learning AI models specifically developed to augment and optimize both the agent performance and customer experience. Uniphore’s latest AI innovations are in the areas of enhanced intent discovery + next best action, enhanced agent promises model, proactive supervisor alerts and automatic feedback loop for optimization of our AI models.
Uniphore’s new enhancements further bolster the capabilities of its existing industry-leading products and those recently announced, which included innovations like integrated front-end customer interaction and backend fulfillment from engagements and new solutions for agent authentication and customer data protection.
“From the beginning, Uniphore has led the industry by focusing on delivering AI + Automation solutions that make a tangible difference in conversations between customers and agents,” said Umesh Sachdev, CEO and co-founder of Uniphore. “These latest enhancements help our customers drive transformational experiences by delivering greater intelligence and recommendations through the application of deep learning AI models. I am extremely proud of the work our team has done to deliver these technology innovations for our customers.”
“For AI to be effective and make a strategic difference in Contact Centers, AI models need to be developed, tested and refined constantly. This is not easy and requires deep levels of understanding an experience to get it right,” said Zeus Kerravala, President and Founder of ZK Research. “Uniphore is focused on the right things. Delivering models that drive better agent preparedness before the call, more active listening and accurate issue resolution during the call, and better post-call recordkeeping and follow-up.”
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Uniphore’s latest product enhancements include:
- Interaction Sectioning
During every engagement, critical information is exchanged during different times of a call (greeting, authentication, discovery, resolution, wrap up, etc.) With a better understanding of what is happening when, Uniphore’s AI models can better help agents in real-time, assist customers with their needs and deliver more accurate summaries and shorten the overall time it takes to handle a call. One example would be as an agent begins the wrap-up phase of the call, the new AI model triggers the auto-summary capability and provides the agent with the call summary for the customer along with RPA-driven follow-ups. No more agents searching through their own notes and best recollection of what was said or committed during the call.
- AI Modeling for Intent Detection
When identifying the intent of a customer, two primary factors must be considered by both agents and the AI models used to assist in this process: what is being said and how it is said. Uniphore’s latest AI models now take both into account with enhanced sentiment analysis for improved, real-time coaching and automated delivery of recommendations for resolution.
- AI Intelligence for Informed Supervisor Alerts
Uniphore’s AI models bring all the information together (what stage is the call at, what is the emotional state of the call, what is being offered as a resolution, is the agent following the coaching, etc.) and turns it into intelligence to decide when and how to proactively alert supervisors to better support the engagement. False supervisor alerts can be counterproductive so having real intelligence informed by accurate AI models is critical.
- After Call Work Self-optimizing AI Models
At the end of every call, millions of contact center agents around the world spend valuable time summarizing and categorizing each call so the organization can track the outcomes and improve the customer experience. The cost of those few minutes in after-call work (ACW) quickly adds up to millions of dollars spent by enterprises and the information often goes untapped for improvement. Uniphore’s latest self-optimizing AI models have been developed so the system can learn from any additions, corrections, or insights agents include to the auto-generated summaries. Uniphore’s AI learns from these edits to better support future calls. As the system learns, it is scaled across the organization, saving valuable time and increasing customer satisfaction.
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