Quick overview This workflow triggers on incoming WhatsApp messages, uses Google Gemini to extract and score real estate lead details, stores each lead in Supabase, and sends a WhatsApp alert to the agent only for high-intent inquiries based on configurable score thresholds. How it works Triggers when a new WhatsApp message is received via the WhatsApp Business Cloud API. Pulls the sender name, phone number, and message text and applies your configured scoring thresholds and agent contact number. Sends the message to Google Gemini to extract structured lead details (budget range, property type, location preference, timeline, and intent score). Inserts the extracted lead data into a Supabase leads table for tracking. Routes the lead based on the intent score and sends a formatted WhatsApp message to the agent for high-intent leads. Leaves medium- and low-intent leads logged in Supabase without sending an agent alert. Setup Connect WhatsApp Business Cloud API credentials for both the incoming-message trigger and the WhatsApp send action, and activate the workflow to generate the webhook for your WhatsApp app. Add a Google Gemini (PaLM) API credential for the Gemini chat model.
Tags
Structured Output Parser
Supabase
WhatsApp Business CloudRelated workflows
See all AI Automation→Generate weekly student progress reports with Gemini, Supabase, and WhatsApp
Quick overview This workflow runs weekly to pull student attendance and score data from Supabase, uses Google Gemini to generate per-student performance summaries and attention flags, sends immediate WhatsApp alerts for flagged students, then compiles all summaries into a PDF report, uploads it, and shares it with the coach via WhatsApp. How it works Runs weekly on a schedule. Fetches all student weekly metrics (attendance and scores) from Supabase. Uses Google Gemini to write a short performance summary per student and determine whether the student should be flagged for attention based on attendance and score-drop rules. If a student is flagged, sends a WhatsApp message to the coach and inserts a flag record into Supabase. Aggregates all students’ summaries into a single HTML weekly report. Converts the HTML report to a PDF via an HTTP-based HTML-to-PDF API, uploads the PDF to a file-hosting API, and sends the PDF link/document to the coach via WhatsApp. Logs the report URL to Supabase for recordkeeping. Setup Add Supabase credentials and create the required tables/columns (weekly_student_data, attention_flags, and weekly_reports) referenced by the workflow. Add a Google Gemini (P
Draft personalized cold emails from Google Sheets using Gemini and HubSpot
Quick overview This workflow batch-processes leads from Google Sheets, researches each person and company using Tavily plus Google Gemini, drafts a personalized cold email, then creates/updates the contact in HubSpot with a research note and a saved email draft before marking the lead as Done in the sheet. How it works Runs manually and sets a batch size that controls how many leads to process per run. Reads leads from Google Sheets, skips rows already marked as Done, and normalizes the selected batch into clean lead fields. Processes the batch one lead at a time and uses Tavily search (last 60 days) with Google Gemini to extract the most recent public activity, pain points, and a personalization hook. Cleans and parses the research JSON so it can be reused reliably downstream. Uses Google Gemini to draft a 90–120 word cold outreach email (subject and body) that incorporates the research and offers a free “Automated LinkedIn Post Creator” n8n workflow. Creates or reuses the HubSpot contact, logs a HubSpot note with the research and drafted email, and saves the email as a HubSpot engagement in DRAFT status. Updates the matching row in Google Sheets (by Email) to set Status to “Done
B
Force AI to use a specific output format
This workflow is for anyone looking to automatically fetch, validate, and parse complex language-based queries into a structured format. Its unique capability lies in not only processing language but also fixing invalid outputs before structuring them. Note that to use this template, you need to be on n8n version 1.19.4 or later.