Hotel receptionist with WhatsApp, Gemini model-switching, Redis & Google Sheets

Overview

This project is an AI-powered hotel receptionist built using n8n, designed to handle guest queries automatically through WhatsApp.
It integrates Google Gemini, Redis, MySQL, and Google Sheets via LangChain to create an intelligent conversational system that understands and answers booking-related questions in real time.

A standout feature of this workflow is its AI model-switching system — it dynamically assigns users to different Gemini models, balancing traffic, improving performance, and reducing API costs.

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How It Works

WhatsApp Trigger

The workflow starts when a hotel guest sends a message through WhatsApp.
The system captures the message text, contact details, and session information for further processing.

Redis-Based Model Management

• The workflow checks Redis for a saved record of the user’s previously assigned AI model.
• If no record exists, a Model Decider node assigns a new model (e.g., Gemini 1 or Gemini 2).
• Redis then stores this model assignment for an hour, ensuring consistent routing and controlled traffic distribution.

Model Selector

The Model Selector routes each user’s request to the correct Gemini instance, enabling parallel execution across multiple AI models for faster response times and cost optimization.

AI Agent Logic

The LangChain AI Agent serves as the system’s reasoning core. It:

• Interprets guest questions such as:
  • “Who checked in today?”
  • “Show me tomorrow’s bookings.”
  • “What’s the price for a deluxe suite for two nights?”
• Generates safe, read-only SQL SELECT queries.
• Fetches the requested data from the MySQL database.
• Combines this with dynamic pricing or promotions from Google Sheets, if available.

Response Delivery

Once the AI Agent formulates an answer, it sends a natural-sounding message back to the guest via WhatsApp, completing the interaction loop.

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Setup & Requirements

Prerequisites

Before deploying this workflow, ensure the following:

• n8n Instance (local or hosted)
• WhatsApp Cloud API with messaging permissions
• Google Gemini API Key (for both models)
• Redis Database for user session and model routing
• MySQL Database for hotel booking and guest data
• Google Sheets Account (optional, for pricing or offer data)

Step-by-Step Setup

1. Configure Credentials

   • Add all API credentials in n8n → Settings → Credentials (WhatsApp, Redis, MySQL, Google).

2. Prepare Databases

   • MySQL Tables Example:
     • bookings(id, guest_name, room_type, check_in, check_out)
     • rooms(id, type, rate, status)
   • Ensure the MySQL user has read-only permissions.

3. Set Up Redis

   • Create Redis keys for each user:

     llm-user:<whatsapp_id> = { "modelIndex": 0 }
     

   • TTL: 3600 seconds (1 hour).

4. Connect Google Sheets (Optional)

   • Add your sheet under Google Sheets OAuth2.
   • Use it to manage room rates, discounts, or seasonal offers dynamically.

5. WhatsApp Webhook Configuration

   • In Meta’s Developer Console, set the webhook URL to your n8n instance.
   • Select message updates to trigger the workflow.

6. Testing the Workflow

   • Send messages like “Who booked today?” or a voice message.
   • Confirm responses include real data from MySQL and contextual replies.

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Key Features

• Text & voice support for guest interactions
• Automatic AI model-switching using Redis
• Session memory for context-aware conversations
• Read-only SQL query generation for database safety
• Google Sheets integration for live pricing and availability
• Scalable design supporting multiple LLM instances

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Example Guest Queries

Guest Query	AI Response Example
“Who checked in today?”	“Two guests have checked in today: Mr. Ahmed (Room 203) and Ms. Priya (Room 410).”
“How much is a deluxe room for two nights?”	“A deluxe room costs $120 per night. The total for two nights is $240.”
“Do you have any discounts this week?”	“Yes! We’re offering a 10% weekend discount on all deluxe and suite rooms.”
“Show me tomorrow’s check-outs.”	“Three check-outs are scheduled tomorrow: Mr. Khan (101), Ms. Lee (207), and Mr. Singh (309).”

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Customization Options

🧩 Model Assignment Logic

You can modify the Model Decider node to:

• Assign models based on user load, region, or priority level.
• Increase or decrease TTL in Redis for longer model persistence.

🧠 AI Agent Prompt

Adjust the system prompt to control tone and response behavior — for example:

• Add multilingual support.
• Include upselling or booking confirmation messages.

🗂️ Database Expansion

Extend MySQL to include:

• Staff schedules
• Maintenance records
• Restaurant reservations
  Then link new queries in the AI Agent node for richer responses.

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Tech Stack

• n8n – Workflow automation & orchestration
• Google Gemini (PaLM) – LLM for reasoning & generation
• Redis – Model assignment & session management
• MySQL – Booking & guest data storage
• Google Sheets – Dynamic pricing reference
• WhatsApp Cloud API – Messaging interface

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Outcome

This workflow demonstrates how AI automation can transform hotel operations by combining WhatsApp communication, database intelligence, and multi-model AI reasoning.
It’s a production-ready foundation for scalable, cost-optimized, AI-driven hospitality solutions that deliver fast, accurate, and personalized guest interactions.