Intent recognition
Intent recognition is the process of classifying what a user wants from a natural-language input, the foundation of routing and resolution.
Intent recognition is the process of classifying what a user wants from a natural-language input. "I need to change my address" classifies as change_address. "Where's my refund?" as refund_status. The classification drives downstream routing, knowledge retrieval, and action execution.
In context
Intent recognition is foundational to customer service AI. It determines which knowledge to retrieve, which workflow to trigger, and whether the AI can resolve the request or needs to escalate. Modern intent recognition uses LLM-based classifiers; older systems used template matching or shallower ML.
The category's known weakness: intent drift. User language patterns evolve, new product features create new intent categories, and the classifier accuracy degrades over time if not retrained or re-tuned. This is one of the silent failure modes of AI customer service.
Modern AI agents reduce dependence on hard-coded intent categories by reasoning about open-ended requests directly, but intent classification still drives routing and analytics. A system that knows the intent can measure category-level performance and surface KB gaps.
How Auralis uses Intent recognition
Auralis instruments intent-level performance across the deployment: each category's deflection, AHT, FRT, and CSAT tracked weekly. Underperforming categories trigger threshold tuning and KB-gap closure as part of the Auralis-managed cadence.