Match new property listings to buyer leads with Gemini, Gmail and Sheets

Quick overview When a new listing arrives, this workflow scans your buyer database, scores every lead against the listing using Gemini AI, sends personalized match emails to the top buyers, sends WhatsApp messages, logs results to Google Sheets, notifies the agent in Gmail and Slack, and posts to the Facebook Page. How it works 1. A new property listing arrives via webhook, from a competitor monitor, an MLS feed, a manual submission, or any system that can send a POST request. 2. A two-stage matching process begins. Stage 1 instantly filters your buyer lead database by hard criteria — budget range (with configurable flexibility), property type, and contact cooldown — using a Code node with no AI calls needed. 3. Filtered candidates are passed to Gemini AI in a single call. Gemini scores each candidate as Strong Match, Possible Match, or Poor Match based on budget fit, location alignment, property type, and timeline urgency — and drafts a personalized email for every Strong and Possible Match. 4. Personalized match emails are sent to each matched buyer referencing their specific preferences. Every email includes property details, a listing link, and a Calendly booking link so buyers can schedule a viewing instantly. 5. If the buyer has a phone number on file, an optional WhatsApp message is sent alongside the email. 6. Every matched buyer's Last Contacted date updates, enforcing a configurable re-contact cooldown so no lead receives too many messages. 7. Every match logs permanently to a Property Match History Google Sheet, building a record of which listings generated buyer interest over time. 8. The agent receives one summary email listing every buyer contacted, their match score, and the AI reasoning behind each match. 9. When two or more Strong Matches are found, an optional Facebook Page post publishes a property spotlight automatically. 10. An optional Slack alert fires alongside the agent email for immediate mobile notification. Setup 1. Configure the webhook URL in your listing source and ensure the payload includes at least listingAddress and listingPrice (plus any optional fields like beds, baths, neighborhood, and listingUrl). 2. Connect credentials for Google Sheets, Gmail, and Google Gemini (PaLM) and fill in the Google Sheets document IDs and sheet names for both the buyer database and the match history log. 3. Ensure your buyer database sheet includes the expected columns (at minimum Active, Email, Budget Min, Budget Max, Property Type, and Last Contacted) and that Active is set to TRUE for leads you want to include. 4. Update agent and routing defaults in the normalization step (AGENTEMAIL, AGENTNAME, CALENDLYUSERNAME, CALENDLYEVENTSLUG, MAXMATCHES, and DAYSSINCECONTACT). 5. (Optional) Connect a Slack credential and set the target channel (for example property-matches). 6. (Optional) Connect WhatsApp Business Cloud credentials and provide valid phone numbers for leads, and/or set your Facebook Page ID and Page Access Token in the Facebook Graph API request to enable posting. Requirements - Google Sheets setup: Two sheets are required. Sheet 1 — Buyer Lead Database — with these columns: leadId, Lead Name, Email, Phone, Budget Min, Budget Max, Preferred Location, Property Type, Bedrooms Min, Timeline, Lead Score, Last Contacted, Notes, Active, Facebook Lead ID. Sheet 2 — Property Match History — with these columns: Match Date, Listing Address, Listing Price, Lead Name, Lead Email, Match Score, Match Reason, Email Sent, WhatsApp Sent, Listing URL, Days on Market. - Buyer Lead Database population: The workflow reads buyer leads from the Buyer Lead Database sheet. Only leads marked Active = TRUE are scanned per run. Three ways to populate it — manual entry directly into the sheet using the template column structure; via the Facebook Lead Ads to AI-Qualified Follow-Up template (link in Additional Info), which can be configured to write qualified Facebook buyer leads into this sheet automatically; or via any webhook so