How to Create A Complex AI Bot which detects user intent and routes user and gives relevant answers?

Step 1: Plan User Intents and Flows

• Define different user intents (e.g., sales, support, feedback, generic queries).

• Map out the desired conversation flows for each intent.

Step 2: Set Up Trigger and Initial Step

• In your bot builder, set the initial trigger (e.g., user message or keyword).

• Add logic or AI components that analyze the incoming message for intent.

Step 3: Configure Intent Detection

• Use the platform’s AI or intent detection feature (often NLP-based) to classify user queries.

• Train or configure sample phrases for each intent to enhance accuracy.

Step 4: Route User Based on Intent

• Based on detected intent, route the conversation to the appropriate bot block or agent (e.g., sales queries to the sales bot, support to the support workflow).

• For edge cases or unrecognized intents, use a fallback route with clarification prompts.

Step 5: Connect to Trained AI or Agents

• For intents needing data-driven answers, connect to relevant AI agents, ensuring they are trained with the correct documents or datasets.

• Configure response mapping and fallback answers in case the AI cannot answer.

Step 6: Implement Escalation Logic

• For unresolved queries or specific intents (like complaints), add logic to escalate the chat to a human agent.

• Provide users with appropriate transition messages.

Step 7: Test and Optimize

• Run multiple tests with different user queries to validate intent detection and answer routing.

• Refine intent definitions, add more training examples, and optimize escalation/fallback flows.

If you provide a transcript or detailed steps from the video, a more specific breakdown can be created. For now, these steps reflect the typical flow seen in the previous provided material and standard practices for AI chatbot intent detection.

Bot Flow Template Clone Link

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