Intent Classification

How it works

Intent classification runs before response generation in a well-designed AI support pipeline. The classifier reads the customer's first message and assigns one or more intent labels from a predefined taxonomy. These labels then control downstream routing: an 'order status' intent triggers an API call to fetch order data; a 'return request' intent applies the returns knowledge-base filter; a 'billing dispute' intent routes to the senior agent queue.

Accurate intent classification requires a well-designed taxonomy and sufficient training examples per intent category. Overly granular taxonomies (50+ intent types) produce classification errors on edge cases. Overly broad taxonomies (5 intent types) produce correct classifications but miss routing opportunities.

Intent classification is different from topic tagging — intent describes what the customer wants to do, while topic describes what the conversation is about. A single conversation can have multiple intent shifts.

When you'll encounter it

Intent classification appears in routing rule configuration, AI support analytics, and ticket tagging systems. You'll configure intent-based routing during onboarding and refine the taxonomy as you review misrouted tickets in the first weeks of operation.