Customer Support Chatbot

Business Challenge

An e-commerce giant needs to automate 50,000+ daily inquiries while maintaining >90% CSAT. The system must handle multi-turn conversations, recognize intents accurately, and escalate when necessary.

System Design

The solution involves an input processor, an NLU engine for intent classification, a dialogue manager for state tracking, and a dynamic response generator.

Intent Classification

We use ChainOfThought to determine the user's intent and extract relevant entities like Order IDs.

class IntentClassifier(dspy.Module):
    def __init__(self):
        self.classify = dspy.ChainOfThought(IntentClassifierSignature)

    def forward(self, message, history):
        return self.classify(message=message, conversation_history=history)

Dialogue Management

Managing the state of the conversation is key. The Dialogue Manager decides the next action: respond, perform a task (like checking order status), or escalate.

class DialogueManager(dspy.Module):
    def forward(self, state, message, intent_result):
        # Determine next action based on state and intent
        # execute backend actions if needed
        pass

Knowledge Integration

The bot queries multiple sources (FAQ, Product DB, Vector Store) to provide accurate answers to general inquiries.