Glossary · AI and automation
What is intent detection?
Intent detection is identifying what a customer is trying to achieve from their message, such as tracking an order, requesting a refund or asking about pricing, and labelling it as a category. Chatbots use it to choose a response or route a conversation, and support teams use intent reports to see what customers ask about most.
Also called: intent recognition, intent classification
Intent detection, explained
Early chatbots were built around intents. Designers listed the things customers might want, wrote example phrases for each and trained a classifier. A message was matched to the closest intent, and each intent had a scripted reply or flow.
Language models have loosened that dependency. An AI agent can answer an open question without a predefined intent. Intent detection is still valuable, though, for two reasons: routing and reporting. Knowing a conversation is about a billing dispute lets you escalate it; knowing that a large share of this week's chats are about delivery times tells you where to focus.
A useful intent list is short and maps to decisions. If two intents are always handled the same way, merge them. If a single intent hides very different problems, split it.
Always keep an “other” bucket and review it regularly. New intents appear when products, policies or problems change, and a growing “other” share is often the first sign that customers are asking about something your content and routing do not yet cover.
How to measure it
Check classification accuracy on a labelled sample of real messages, and watch the share of conversations falling into “other”.
Common mistake
How SuperBot relates
SuperBot's analytics show conversation intents together with missed answers, leads and knowledge gaps.
Test it on your own questions.
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