Business problem: Managers have no way to tell if a customer was happy, neutral, or frustrated in a conversation, or whether that mood improved or worsened. This applies to both AI Agent and human-handled conversations. Because of this: Negative conversations often go unnoticed until a customer complains, leaves a bad review, or churns If an AI Agent or human agent handled a frustrated customer poorly, there's no way to catch it Managers have no structured way to know which conversations need attention, so they either check randomly or not at all Desired outcome: Automatically detect the customer's sentiment (positive / neutral / negative) in a conversation, whether it's handled by AI or a human agent Understand how a customer's mood shifted over the course of a conversation through sentiment reasons Flag conversations with negative sentiment so managers know which ones need a closer look Show sentiment in reports so managers can spot patterns over time instead of one-off incidents Let managers set up actions in Workflows based on sentiment (e.g. notifications, follow-ups) Use Case: Right now, managers find out a conversation went badly only after the damage is done — a complaint, a bad review, or a lost customer. With sentiment detected automatically, they'd be able to identify which conversations were negative and take action — coaching an agent, fixing an AI Agent's behavior, or following up with the customer — instead of relying on manual spot-checks or waiting for something to go wrong externally.
