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This n8n workflow demonstrates how to automate customer interactions and appointment management via WhatsApp Business bot. After submitting a Google Form, the user receives a notification via WhatsApp. These notifications are sent via a template message. In case user sends a message to the bot, the text and user data is stored in Google Sheets. To reply back to the user, fill in the ReplyText column and change the Status to 'Ready'. In a few seconds n8n will fetch the unsent replies and deliver them one by one via WhatsApp Business node. Customize this workflow to fit your specific needs, connect different online services and enhance your customer communication! 🎉 Setup Instructions To get this workflow up and running, you'll need to: 👇 Create a WhatsApp template message on the Meta Business portal. Obtain an Access Token and WhatsApp Business Account ID from the Meta Developers Portal. This is needed for the WhatsApp Business Node to send messages. Set up a WhatsApp Trigger node with App ID and App Secret from the Meta Developers Portal. Right after that copy the WhatsApp Trigger URL and add it as a Callback URL in the Meta Developers Portal. This trigger is needed to receive incoming messages and their status updates. Connect your Google Sheets account for data storage and management. Check out the documentation page. ⚠️ Important Notes WhatsApp allows automatic custom text messages only within 24 hours of the last user message. Outside with time frame only approved template messages can be sent. The workflow uses a Google Sheet to manage form submissions, incoming messages and prepare responses. You can replace these nodes and connect the WhatsApp bot with other systems.

This n8n workflow demonstrates how you can summarise and automate post-meeting actions from video transcripts fed into an AI Agent. Save time between meetings by allowing AI handle the chores of organising follow-up meetings and invites. How it works This workflow scans for the calendar for client or team meetings which were held online. * Attempts will be made to fetch any recorded transcripts which are then sent to the AI agent. The AI agent summarises and identifies if any follow-on meetings are required. If found, the Agent will use its Calendar Tool to to create the event for the time, date and place for the next meeting as well as add known attendees. Requirements Google Calendar and the ability to fetch Meeting Transcripts (There is a special OAuth permission for this action!) OpenAI account for access to the LLM. Customising the workflow This example only books follow-on meetings but could be extended to generate reports or send emails.

This n8n workflow template lets teams easily generate a custom AI chat assistant based on the schema of any Notion database. Simply provide the Notion database URL, and the workflow downloads the schema and creates a tailored AI assistant designed to interact with that specific database structure. Set Up Watch this quick set up video 👇 Key Features Instant Assistant Generation**: Enter a Notion database URL, and the workflow produces an AI assistant configured to the database schema. Advanced Querying**: The assistant performs flexible queries, filtering records by multiple fields (e.g., tags, names). It can also search inside Notion pages to pull relevant content from specific blocks. Schema Awareness**: Understands and interacts with various Notion column types like text, dates, and tags for accurate responses. Reference Links**: Each query returns direct links to the exact Notion pages that inform the assistant’s response, promoting transparency and easy access. Self-Validation**: The workflow has logic to check the generated assistant, and if any errors are detected, it reruns the agent to fix them. Ideal for Product Managers**: Easily access and query product data across Notion databases. Support Teams**: Quickly search through knowledge bases for precise information to enhance support accuracy. Operations Teams**: Streamline access to HR, finance, or logistics data for fast, efficient retrieval. Data Teams**: Automate large dataset queries across multiple properties and records. How It Works This AI assistant leverages two HTTP request tools—one for querying the Notion database and another for retrieving data within individual pages. It’s powered by the Anthropic LLM (or can be swapped for GPT-4) and always provides reference links for added transparency.

What this workflow does: This flow uses an AI node to generate Seed Keywords to focus SEO efforts on based on your ideal customer profile. You can use these keywords to form part of your SEO strategy. Outputs: List of 20 Seed Keywords Setup Fill the Set Ideal Customer Profile (ICP) Connect with your credentials Replace the Connect to your own database with your own database Pre-requisites / Dependencies You know your ideal customer profile (ICP) An AI API account (either OpenAI or Anthropic recommended) Made by Simon @ automake.io

This n8n workflow shows how using multimodal LLMs with AI vision can tackle tricky image validation tasks which are near impossible to achieve with code and often impractical to be done by humans at scale. You may need image validation when users submitted photos or images are required to meet certain criteria before being accepted. A wine review website may require users only submit photos of wine with labels, a bank may require account holders to submit scanned documents for verification etc. In this demonstration, our scenario will be to analyse a set of portraits to verify if they meet the criteria for valid passport photos according to the UK government website (https://www.gov.uk/photos-for-passports). How it works Our set of portaits are jpg files downloaded from our Google Drive using the Google Drive node. Each image is resized using the Edit Image node to ensure a balance between resolution and processing speed. Using the Basic LLM node, we'll define a "user message" option with the type of binary (data). This will allow us to pass our portrait to the LLM as an input. With our prompt containing the criteria pulled off the passport photo requirements webpage, the LLM is able to validate the photo does or doesn't meet its criteria. A structured output parser is used to structure the LLM's response to a JSON object which has the "is_valid" boolean property. This can be useful to further extend the workflow. Requirements Google Gemini API key Google Drive account Customising this workflow Not using Gemini? n8n's LLM node works with any compatible multimodal LLM so feel free to swap Gemini out for OpenAI's GPT4o or Antrophic's Claude Sonnet. Don't need to validate portraits? Try other use cases such as document classification, security footage analysis, people tagging in photos and more.

Video Guide I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n. Youtube Link Who is this for? This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive. What problem does this workflow solve? Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files. What this workflow does The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include: Fetching and comparing files to avoid duplicate processing. Handling file downloads and extracting content based on the file type. Converting documents into vectorized data for contextual information retrieval. Storing and querying vectorized data from a Supabase vector store. File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content. Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions. Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot. Setup N8N Workflow Fetch File List from Supabase: Use Supabase to retrieve the stored file list from a specified bucket. Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing. Compare and Filter Files: Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table. Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled. Handle File Downloads: Download new files using detailed storage configurations for public/private access. Adjust the storage settings and GET requests to match your Supabase setup. File Type Processing: Use a Switch node to target specific file types (e.g., PDFs or text files). Employ relevant tools to process the content: For PDFs, extract embedded content. For text files, directly process the text data. Content Chunking: Break large text data into smaller chunks using the Text Splitter node. Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks. Vector Embedding Creation: Generate vectorized embeddings for the processed content using OpenAI's embedding tools. Ensure metadata, such as file ID, is included for easy data retrieval. Store Vectorized Data: Save the vectorized information into a dedicated Supabase vector store. Use the default schema and table provided by Supabase for seamless setup. AI Chatbot Integration: Add a chatbot node to handle user input and retrieve relevant document chunks. Use metadata like file ID for targeted queries, especially when multiple documents are involved. Testing Upload sample files to your Supabase bucket. Verify if files are processed and stored successfully in the vector store. Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?"). Test for accuracy and contextual relevance of retrieved results.