From Frustrated Chats to Live Video Support With Jev and Vonage

A Vonage demonstration application combines TypeSafe AI’s Jev model with the Vonage Video API to identify frustrated customer-chat interactions and offer timely escalation to a human agent through live video. A Node.js and Express backend evaluates the full conversation history using typed Jev queries for sentiment, a graduated frustration score, and the probability that a user explicitly requests human help, avoiding reliance on unstructured language-model responses. Escalation occurs when configured thresholds indicate significant frustration, high-confidence negative sentiment, or a direct request for an agent, while evaluation failures allow the chat to continue normally. Once a customer accepts, Vonage Video Signaling sends the escalation context, including the customer’s latest message and emotional assessment, to a shared employee dispatch dashboard, where an agent can claim the request and join the customer’s dedicated video session. The project can be launched through GitHub Codespaces with Vonage and TypeSafe AI credentials, and it illustrates how structured AI evaluation and real-time communication can support applications such as proactive assistance, ticket prioritization, and agent-quality monitoring.

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