Entity extraction

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.

An intent is what a customer is actually trying to do, inferred from what they wrote or said rather than from the specific words used. Two very different messages can share an intent, which is why routing and automation are built on intent rather than on keyword matching.

Workflow automation chains system actions — issue a refund, reissue a document, update a record — so a resolution completes itself without an agent performing each step. It separates an AI agent that resolves requests from a chatbot that can only describe how a request would be resolved.

An utterance is a single thing a customer said, exactly as the model receives it, before any cleaning or interpretation. Utterances are the raw material for intent classification and evaluation sets, and preserving them verbatim is what allows AI errors to be reproduced and diagnosed later.