Quick overview This workflow runs daily to fetch pending student fee records from Supabase, filters fees due in three days, and sends WhatsApp Business template reminders via the Meta WhatsApp Cloud API, then updates Supabase to mark each reminder as sent. How it works Runs every day at 9:00 AM on a schedule. Pulls all pending fee records from Supabase where reminder_sent is false and status is pending. Filters the results to only students whose due_date matches exactly three days from today. Sends a pre-approved WhatsApp template message (Meta WhatsApp Cloud API) to each student’s phone number. Updates the corresponding Supabase row to set reminder_sent to true so the reminder is not sent again. Setup Create a Supabase project, add a student_fees table (including student_phone, due_date, amount_due, status, and reminder_sent fields), and ensure your fee records are stored there. Add Supabase credentials in n8n and set the correct table name in the Supabase nodes. Set up Meta WhatsApp Business Cloud API access in n8n (system user token/credentials) and fill in your WhatsApp phone number ID.
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Supabase
WhatsApp Business CloudRelated workflows
See all Supabase→Recover missed clinic appointments with Supabase and WhatsApp
Quick overview This workflow runs every 15 minutes to find unprocessed clinic appointments in Supabase that are more than 30 minutes past their scheduled time and sends a WhatsApp Business template message to the patient to prompt rebooking, then flags the appointment so it is not messaged again. How it works Runs every 15 minutes on a schedule. Reads appointments from Supabase where the patient is not checked in and the no-show reminder has not been processed. Filters the results to appointments that are at least 30 minutes past the scheduled appointment time. Loops through each qualifying appointment and sends a pre-approved WhatsApp Business template to the patient’s phone number. Updates the appointment record in Supabase to mark it as processed so it is not nudged again. Setup Create a Supabase project with an appointments table (including appointment_time, patient_phone, checked_in, and no_show_processed) and ensure your booking system writes appointments into it. Add a Supabase credential in n8n with access to the project and table used by the workflow. Set up Meta WhatsApp Business Cloud API access in n8n (token/connection), then enter your WhatsApp phone number ID and an a
Telegram Bot with Supabase memory and OpenAI assistant integration
Video Guide I prepared a detailed guide that showed the whole process of building an AI bot, from the simplest version to the most complex in a template. .png) Who is this for? This workflow is ideal for developers, chatbot enthusiasts, and businesses looking to build a dynamic Telegram bot with memory capabilities. The bot leverages OpenAI's assistant to interact with users and stores user data in Supabase for personalized conversations. What problem does this workflow solve? Many simple chatbots lack context awareness and user memory. This workflow solves that by integrating Supabase to keep track of user sessions (via What this workflow does This Telegram bot template connects with OpenAI to answer user queries while storing and retrieving user information from a Supabase database. The memory component ensures that the bot can reference past interactions, making it suitable for use cases such as customer support, virtual assistants, or any application where context retention is crucial. 1.Receive New Message: The bot listens for incoming messages from users in Telegram. Check User in Database: The workflow checks if the user is already in the Supabase database using the Create N
Call analyzer with AssemblyAI transcription and OpenAI assistant integration
Video Guide I prepared a detailed guide that showed the whole process of building a call analyzer. .png) Who is this for? This workflow is ideal for sales teams, customer support managers, and online education services that conduct follow-up calls with clients. It’s designed for those who want to leverage AI to gain deeper insights into client needs and upsell opportunities from recorded calls. What problem does this workflow solve? Many follow-up sales calls lack structured analysis, making it challenging to identify client needs, gauge interest levels, or uncover upsell opportunities. This workflow enables automated call transcription and AI-driven analysis to generate actionable insights, helping teams improve sales performance, refine client communication, and streamline upselling strategies. What this workflow does This workflow transcribes and analyzes sales calls using AssemblyAI, OpenAI, and Supabase to store structured data. The workflow processes recorded calls as follows: Transcribe Call with AssemblyAI: Converts audio into text with speaker labels for clarity. Analyze Transcription with OpenAI: Using a predefined JSON schema, OpenAI analyzes the transcription to extract