Here’s How Five9 Assists Agents with OpenAI Technology

Five9 has made Agent Assist 2.0 generally available to its customers. Compared to earlier versions, the new version of the solution does not use model training and manual categorization but Large Language Models (LLM) technology. Now, Large Language Models can analyze a call transcript and summarize it to produce high-quality results regardless of the volume of conversations. What's more, the models do not need any prior training on what the conversations are about.

AI Summary - the feature that will take on the summarization of customer conversations - is part of Five9's strategy to “democratize advanced AI applications for customer service," aiming to leverage technologies that reduce the time and expense of training previously used natural language models.

“Five9 is delivering innovative and practical AI that empowers contact center workers and enables a fluid customer experience. Incorporating generative AI technology into agent assistance is the natural next step for CX teams, and we are committed to helping businesses apply the technology to gain quick wins,” said Callan Schebella, EVP, Product Management, Five9.

Agent Assist 2.0 aims to replace the tedious process of agents summarizing each chat or call to point out key ideas or issues. As the context of conversations is crucial to their resolution, summarizing calls in bulk proves to be efficient for both agents and customers - less waiting and more money saved.

According to the company, the initial set of customers that used Agent Assist 2.0 reported impressive results. The latest release of the solution expands the use cases for Agent Assist, from onboarding of new agents to maximizing the agent’s efficiency throughout the course of their time with a company.

Not too long ago, Five9 teamed up with Invoca to offer a joint solution that provides more in-depth and real-time data analysis across the customer journey, bridging the gap between the contact center and marketing teams for a more seamless customer experience.