🧠 How It Works This AI Agent automatically qualifies property buyer leads from form submissions and sends them directly to your CRM. 🔄 Workflow Steps Form Submission Trigger When a user submits their details via a property inquiry form, the workflow is activated. AI Lead Classification The buyer’s input (budget, location, timeline, etc.) is analyzed by OpenAI. Structured data is extracted, and a lead score (0–100) is generated. Lead Qualification Logic Leads with a score ≥ 70 are marked as qualified. Leads with a lower score can be ignored or stored separately for later review. Follow-Up Actions (for Qualified Leads) An email notification is sent to the real estate agent. A record is created in Airtable to act as a lightweight CRM. ⚙️ How to Set Up 1. Form Setup Replace the default trigger with your preferred source: Typeform, Google Forms, Webflow form, etc. Ensure your form collects the following fields: Name, Email, Budget, Location, Timeline, Property Type 2. Connect Your Credentials Add your OpenAI API key for the LLM node Connect your Gmail account for notifications Link your Airtable base & table to store qualified leads 3.
Tags
Airtable
Gmail
Information Extractor
OpenAI Chat ModelRelated workflows
See all AI Automation→AI: Summarize podcast episode and enhance using Wikipedia
The workflow automates the process of creating a summarized and enriched podcast digest, which is then sent via email. Note that to use this template, you need to be on n8n version 1.19.4 or later.
Suggest meeting slots using AI
The purpose of this n8n workflow is to automate the process of identifying incoming Gmail emails that are requesting an appointment, evaluating their content, checking calendar availability, and then composing and sending a response email. Note that to use this template, you need to be on n8n version 1.19.4 or later.
Custom LangChain agent written in JavaScript
This workflow has multiple functionalities. It starts with a manual trigger, "When clicking 'Execute Workflow'", that activates two separate paths. The first path takes a preset string "Tell me a joke" and processes it through a custom Language Learning Model (LLM) chain node. This node interacts with an OpenAI node for query processing. The second path takes another preset string "What year was Einstein born?" and passes it to an "Agent" node. This agent further interacts with a Chat OpenAI node and a custom Wikipedia node to produce the required information. The workflow uses both built-in and custom nodes, and integrates with OpenAI for both paths. It's built for experimenting with language models, specifically in the context of conversational agents and information retrieval. Note that to use this template, you need to be on n8n version 1.19.4 or later.