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.

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.

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

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

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.

Set Up Redis
Create Redis keys for each user:
llm-user:<whatsapp_id> = { "modelIndex": 0 } TTL: 3600 seconds (1 hour).

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

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

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

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

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).” |

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.

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

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.