Someone sends a message to your system. It could be a question, a complaint, a request, or just feedback.
Your AI processes it. And routes it to... the wrong department. Again.
Support tickets meant for billing end up with engineering. Feature requests go to accounting. Urgent issues sit in the general inbox.
The AI reads the words but misses the point. It does not understand what the person actually wants.
Before you can route, prioritize, or respond, you need to know what someone is trying to accomplish. That starts with classification.
UNDERSTANDING LAYER - Intent classification happens early, before routing or response generation.
Intent classification analyzes incoming messages to determine the underlying goal. When someone writes "I cannot log in," the intent might be password_reset, account_locked, or technical_support depending on context. The exact words matter less than what the person is trying to accomplish.
This is different from keyword matching or topic detection. A keyword search for "login" catches the message, but does not tell you whether the person wants help, is reporting a bug, or is requesting a new feature. Intent classification bridges the gap between raw text and actionable understanding.
Intent classification answers: "What does this person want to happen as a result of sending this message?"
Intent classification solves a universal problem: when you receive a request, you need to understand its purpose before you can respond appropriately. Categorizing by goal (not just topic) enables intelligent action.
Incoming communication. Multiple possible meanings. Classify by what the sender wants to happen, not just what they mentioned. The goal determines the response path.
Type a message or click an example to see how intent classification works.
Explicit patterns and keywords
Define patterns that map to intents. If message contains "refund" or "money back," classify as refund_request. Simple, fast, and predictable but struggles with nuance.
AI determines intent from context
Send the message to an LLM with a list of possible intents and descriptions. The AI understands nuance, sarcasm, and implied meaning. Flexible but adds latency and cost.
Semantic similarity matching
Convert the message to an embedding vector and compare against pre-computed intent embeddings. The closest match wins. Fast inference after setup, handles variations naturally.
Intent classification sits at the beginning of intelligent request handling. It takes raw text input, determines what the sender wants, and feeds that understanding to routing and escalation logic downstream.
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Your intents include "billing_question" and "subscription_help" but a message like "Why was I charged twice?" matches both. The system picks randomly or returns the first match, leading to inconsistent routing.
Instead: Define clear hierarchies. Create a primary intent for ambiguous cases or use confidence scores to route uncertain classifications to human review.
You create 47 specific intents trying to capture every possible request. Classification accuracy drops because the distinctions are too fine. "password_reset" vs "password_change" vs "password_help" all blur together.
Instead: Start with broad categories (5-10 intents) that map to different actions. Split only when you need different handling. If two intents route to the same place, combine them.
Someone writes: "I need to update my address and also want to know about your refund policy." Your classifier picks one intent and ignores the other. Half the request goes unhandled.
Instead: Design for multi-intent detection. Return all detected intents with confidence scores. Let downstream routing handle messages that need multiple responses.
You have learned how to determine what someone is trying to accomplish from their message. The natural next step is learning how to route that request to the right handler.