How intent recognition works
Older chatbots used intent classifiers: a designer defined a list of intents, wrote training phrases for each, and the model picked the closest match with a confidence score. Anything below the threshold fell to a fallback intent. Language models infer intent directly from the conversation, including when one message carries two intents at once.
For a business, the intents that matter most are the ones that change priority: ready to buy, urgent, upset, or about to cancel. Recognizing those quickly is what lets an assistant alert a person at the right moment instead of treating every message the same.
Example: two basement calls at a restoration company
Two callers mention a basement to a water damage restoration company. One says water is coming through the wall right now; the other wants a quote for a mold inspection next month. Intent recognition separates an emergency, which gets safety steps and an urgent alert, from a routine estimate request, which gets booked. This scenario is illustrative.
Intent signals in InsertChat
InsertChat doesn’t make you build an intent list. Its assistant reads each request in context and acts on the signals that matter: high-intent alerts flag customers who explicitly ask to buy, book, hire, or get a quote; urgent alerts cover a caller in distress, a safety risk, or a serious service failure; and cancellation alerts flag customers who want to cancel.
Requests for a person, upset customers, and topics you list as always needing a person are marked needs-you for your team. Learn more in AI lead capture, or see the restoration setup.
