This n8n workflow creates an intelligent real estate assistant that helps users find properties based on specific filters like city, price, bedrooms, and bathrooms. It starts with a chat trigger where users initiate a conversation, acting as a virtual real estate agent. The workflow utilizes language models (like GPT-4) for natural language understanding and generates responses based on user input. It integrates Bright Data to filter real estate listings from a marketplace dataset according to user preferences, retrieves a snapshot of selected properties, and formats the information into a user-friendly presentation.
The process begins when a chat message is received, prompting the AI agent to ask for property requirements. These inputs are stored in a memory buffer, maintaining context for ongoing conversations. When the user submits criteria, the workflow filters the dataset using Bright Data based on provided filters (home status, city, price, bedrooms, bathrooms). It then triggers an external workflow to get detailed property content from the snapshot.
The agent then consolidates data and responses, potentially waiting for the snapshot to be prepared. Finally, it delivers a curated list of properties, including images and details, to the user. This workflow is highly useful for real estate agencies or property listing platforms seeking to automate and personalize property recommendations through conversational AI.
Reviews
There are no reviews yet.