Intent Detection

Intent detection is the process by which an AI system — powered by natural language understanding (NLU) and machine learning — determines what a caller is trying to accomplish based on their spoken words.

Intent Examples

• Speaker: "I want to know when my appointment is." Intent: retrieve_appointment
• Speaker: "Can I reschedule my 3pm visit?" Intent: reschedule_appointment
• Speaker: "I haven't received my insurance card yet." Intent: document_inquiry

How Intent Detection Works

An intent detector processes the caller's transcribed text and outputs a probability distribution over known intents. The system selects the highest-probability intent and routes the conversation to the appropriate handler (appointment bot, document lookup, escalation to human).

Intent Detection Accuracy

Accuracy depends on training data quality (diverse examples of each intent), domain specificity, and handling of multi-intent utterances. Modern systems achieve 95%+ accuracy on common intents but may struggle with rare or ambiguous requests.

Intent Detection in Voice Agents

Accurate intent detection is foundational to voice agent performance. If the AI misclassifies the intent, the entire conversation derails. High-quality voice agents combine intent detection with user feedback loops: if the caller says "that's not what I meant," the system re-detects and corrects course.