Intent

An intent taxonomy is the structured list of intents a support operation recognises and reports on. A good taxonomy is granular enough to drive different actions and stable enough to trend over time; taxonomies that grow uncontrolled become unusable for reporting well before routing.

Natural language understanding is the layer that maps free text to intents and entities so a system can act on what a customer wrote. It answers what the customer wants and which values are involved; generation is a separate step deciding what to say back.

Entity extraction is the process of pulling structured values — an order number, an IBAN, a product name, a date — out of a customer’s free-text message. Reliable extraction is what allows an automated agent to act inside a business system rather than only classify the request.

Routing is deciding which agent, team, or AI agent receives a contact, based on intent, language, skill, customer tier, and availability. Accurate routing is the cheapest available reduction in handle time, because a misrouted contact pays the cost of being read and reassigned twice.