Support tickets stream in from the inbox on the left and pass through a central AI classifier. The model reads each ticket's text to infer two things at once: its intent (which team should handle it — billing, technical, account, or general) and its sentiment (how upset the customer sounds, from calm green to furious red). Tickets are then routed to the matching queue pillar on the right.
Real deployments of ticket-routing and sentiment models commonly cut first-response time by automatically triaging the 60-80% of tickets that are routine, freeing human agents to focus on the small share of conversations where a customer is genuinely at risk of churning.
Support tickets stream out of the inbox, pass through a glowing AI classifier core, and are routed to color-coded queues by intent — while sentiment analysis catches angry customers and escalates them straight to a human agent.
Each ticket is colored by its sentiment score (green calm to red furious) and classified into a queue by intent. Tickets whose sentiment drops below the escalation threshold skip the normal queue entirely and jump to the red Escalation lane.
Adjust ticket rate, the share of negative-sentiment tickets, AI routing accuracy, and the escalation threshold. Toggle AI routing off to see every ticket dumped into a single slow manual queue instead.
Automated sentiment-triggered escalation lets support teams intervene with at-risk customers within seconds, instead of hoping an overworked agent happens to notice frustration buried lower in a first-come-first-served queue.