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Booking.com AI Trip Planner - Case Study Implementation

Overview

This repository contains the implementation and analysis from our case study on Booking.com's AI-Powered Travel Platform. This initiative focuses on understanding the AI-driven features that Booking.com built using OpenAI's models, including conversational trip planning, smart filters, property Q&A, and review summarization.

How Booking.com is using AI: (Watch on YouTube)

Booking.com AI Case Study

Resources

Original Booking.com Resources

Key Concepts from Booking.com's AI Implementation

Booking.com partnered with OpenAI to transform their travel platform with AI-powered features that enhance discovery, personalization, and user experience:

  • AI Trip Planner: Conversational destination discovery and itinerary building using natural language prompts
  • Smart Filters: Natural language understanding to map user requests to property filters beyond predefined options
  • Property Q&A: AI-powered question answering about property details using fine-tuned models on user-generated content
  • AI Review Summaries: Automated summarization of property reviews into key themes for faster decision-making
  • Help Me Reply: Automated response generation for guest communications

In our implementation, we will only tackle Smart Filters and PropertyQA. Bonus: I am using an AI Memory layer, so using this, I have also implemented: Itinerary Planner (not just focused on property listings)

Architecture

Screenshot 2026-03-11 at 20 07 01

The implementation includes:

  • Backend: FastAPI server with semantic search, composite scoring, property Q&A, and itinerary planning
  • Frontend: React application for the user interface
  • Data Layer: Qdrant vector database for property embeddings and search
  • AI Integration: OpenAI models for intent parsing, question answering, and itinerary generation

App Screenshots

Welcome Screen

Welcome Screen

Home Page

Home Page

Search Results

Search Results

Onboarding Questions

Onboarding Questions

Setting Up Memory

Setting Up Memory

Getting Started

Prerequisites

# Backend dependencies
pip install -r requirements.txt

# Frontend dependencies
cd frontend
npm install

Get the API Keys

Running the Application

# Index the data (run once)
python -m scripts.index_properties

# Start the backend
cd backend
uvicorn main:app --reload

# In another terminal, start the frontend
cd frontend
npm run dev

Implementation Notes

This implementation is inspired by Booking.com's approach but adapted to our specific tech stack and use case. The focus is on understanding the core AI integration patterns and conversational travel assistance rather than replicating the exact infrastructure.

License

This is a case study project for educational purposes only.

Acknowledgments

  • Booking.com and OpenAI for sharing insights on their AI collaboration
  • Community contributors to the case study series