What Intent classification means in practice
Intent classification is the natural-language-understanding step that turns a user's free-text or speech input into one of a curated set of business intents. "What's my balance" becomes intent=check_balance; "I lost my card" becomes intent=block_card. The classical implementation used dedicated NLU models (Rasa, Dialogflow); modern implementations use an LLM with a structured-output prompt, which is cheaper to maintain because the intent taxonomy is text rather than training data. In regulated deployments the intent classifier is the policy gate — only intents on the allow-list are routed to action; everything else is escalated or refused. This makes the taxonomy itself a compliance artefact.
Related terms
Conversational AI →
AI systems that interact with users through natural-language dialogue — chatbots, voice agents, virtual assistants — typically combining intent classification, retrieval, and generation.
AI voice agent →
A conversational AI system designed for voice channels — combining ASR, intent + dialogue logic, and natural-sounding TTS to handle calls end-to-end.
Guardrails →
Runtime checks that intercept LLM inputs and outputs to enforce policy — blocking PII leakage, prompt-injection attempts, off-topic queries, unsafe responses.
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Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.