Intent Matcher
Leverages LLM understanding to create natural, context-aware routing without needing exact keyword matches, making conversations more flexible and human-like.
The Intent Matcher node uses LLM capabilities to understand the meaning behind user inputs and route conversations accordingly. When a user message arrives, this node analyzes it against defined intents and directs the flow based on the best match.
Setting up intent matching:
Select the LLM model to use for intent analysis
Define intent name (e.g., "contact-team", "request-pricing")
Describe trigger situations that should activate this intent
Create different paths for each intent
Set up a default path for unmatched intents
Example: A virtual being needs to distinguish between product inquiries and support requests:

Intent: product-inquiry
Trigger: When user asks about products, features, or pricing
Intent: technical-support
Trigger: When user mentions problems, errors, or needs help
Default: General conversation handling
Common use cases:
Route to specific knowledge base sections
Direct to appropriate support flows
Identify user goals and intentions
Categorize queries by type
Handle multiple related intents
Last updated
Was this helpful?