Implementing Real-Time Intent Classification in Conversational Agents

Why Raw LLMs Are Too Slow for Intent Routing

When building conversational bots or routing systems, passing every user input to a full-sized Large Language Model to figure out the user's goal is expensive and slow. Specialized intent classification models are much more efficient, offering latency figures under 150ms.

How RS FlowHub's Intent Classifier Works

By posting a user's message along with a list of candidate labels, RS FlowHub returns the top matching intents with a confidence score:

{
    "input": "I want to change my billing email address",
    "intents": [
        { "label": "update_profile", "score": 0.94 },
        { "label": "billing_question", "score": 0.05 },
        { "label": "technical_support", "score": 0.01 }
    ]
}

Optimizing Conversational UX

By routing user messages instantly based on intent confidence scores, you can bypass LLM latency completely for routine commands (like updating settings or requesting refunds) and reserve heavy generative tasks for complex support tickets. This optimizes your application's user experience and cuts your AI compute cost by up to 80%.