Workflow Templates

What Does This Flow Do? This workflow demonstrates how to dynamically generate a line chart using the QuickChart node based on data provided in a JSON object and then upload the resulting chart image to Google Drive. Use Cases You can use it in presentations or requesting for chart generation from a software with HTTP requests. Automated report generation (e.g., daily sales charts). Visualizing data fetched from APIs or databases. Simple monitoring dashboards. Adding charts to internal tools or notifications. How it Works Trigger: The workflow starts manually when you click 'Test workflow'. Set Sample Data: A Set node (Edit Fields: Set JSON data to test) defines a sample JSON object named jsonData. This object contains: reportTitle: A title (not used in the chart generation in this example, but useful for context). labels: An array of strings representing the labels for the chart's X-axis (e.g., ["Q1", "Q2", "Q3", "Q4"]). salesData: An array of numbers representing the data points for the chart's Y-axis (e.g., [1250, 1800, 1550, 2100]). Generate Chart: The QuickChart node is configured to: Create a line chart. Dynamically read labels from the jsonData.labels array (Labels Mode: From Array). Use the jsonData.salesData array as the input data (Note: This configuration places data in the top-level 'Data' field. For more complex charts with multiple datasets or specific dataset options, configure datasets under 'Dataset Options' instead). The node outputs the generated chart image as binary data in a field named data. Upload to Google Drive: The Google Drive node (Google Drive: Upload File): Takes the binary data (data) from the QuickChart node. Uploads the image to your specified Google Drive folder. Dynamically names the file based on its extension (e.g., chart.png). Setup Steps Import: Import this template into your n8n instance. Configure Google Drive Credentials: Select the Google Drive: Upload File node. You MUST configure your own Google Drive credentials. Click on the 'Credentials' dropdown and either select existing credentials or create new ones by following the authentication prompts. (Optional) Customize Google Drive Folder: In the Google Drive: Upload File node, you can change the Drive ID and Folder ID to specify exactly where the chart should be uploaded. Activate: Activate the workflow if you want it to run automatically based on a different trigger. How to Use & Customize Change Input Data:** Modify the labels and salesData arrays within the Edit Fields: Set JSON data to test node to use your own data. Ensure the number of labels matches the number of data points. Use Real Data Sources:** Replace the Edit Fields: Set JSON data to test node with nodes that fetch data from real sources like: HTTP Request (APIs) Postgres / MongoDB nodes (Databases) Google Sheets node Ensure the output data from your source node is formatted similarly (providing labels and salesData arrays). You might need another Set node to structure the data correctly before the QuickChart node. Change Chart Type:** In the QuickChart node, modify the Chart Type parameter (e.g., change from line to bar, pie, doughnut, etc.). Customize Chart Appearance:** Explore the Chart Options parameter within the QuickChart node to add titles, change colors, modify axes, etc., using QuickChart's standard JSON configuration options. Use Datasets (Recommended for Complex Charts):** For multiple lines/bars or more control, configure datasets explicitly in the QuickChart node: Remove the expression from the top-level Data field. Go to Dataset Options -> Add option -> Add dataset. Set the Data field within the dataset using an expression like {{ $json.jsonData.salesData }}. You can add multiple datasets this way. Change Output Destination:** Replace the Google Drive: Upload File node with other nodes to handle the chart image differently: Write Binary File: Save the chart to the local filesystem where n8n is running. Slack / Discord / Telegram: Send the chart to messaging platforms. Move Binary Data: Convert the image to Base64 to embed in HTML or return via webhook response. Nodes Used Manual Trigger Set QuickChart Google Drive Tags: (Suggestions for tags field) QuickChart, Chart, Visualization, Line Chart, Google Drive, Reporting, Automation

🚀 Transform LinkedIn Post Reactions into Content Ideas with Airtable 📝 Description This workflow helps you to turn your LinkedIn activity into a powerful content ideation engine. It captures your most recent post reactions on LinkedIn automatically, filters them based on recency, and structures the content into Airtable—ready for brainstorming, inspiration, or publication planning. ⚙️ What It Does Fetches* the latest liked posts from LinkedIn via a public API (rapidapi.com/Real-Time Linkedin Scraper*). Filters** posts to include only those marked as your decided reaction and posted in the last 7 days. Extracts** the post text, author, links and more. Formats** the data into a database-friendly structure. Saves** the output in Airtable for easy tracking, tagging, or team collaboration. 💡 Use Cases Build a content idea vault from posts you admire. Capture inspiration from thought leaders. Identify trends based on what you find insightful. Supercharge your personal brand or newsletter by turning likes into learning. 🛠 Prerequisites Before using this template, make sure you have: ✅ A RapidAPI account and access to the linkedin-api8 endpoint. ✅ Your RapidAPI key and the target LinkedIn username. ✅ An Airtable account with a base/table set up. 🧰 Setup Instructions Clone this template into your n8n instance. Open the Fetch LinkedIn Likes node and enter: Your LinkedIn username. Your RapidAPI key in the headers. Open the Save to Airtable node and: Connect your Airtable account. Link the correct base (Content Hub) and table (Ideas). Set your desired schedule in the Trigger node. Activate the workflow and you're done! 📋 Airtable Setup Create a base called Content Hub and a table named Ideas with the following columns: | Column Name | Type | Required | Notes | |-------------|------------|----------|----------------------------| | Title | Single line text | ✅ | Generated from author info | | Description | Long text | ✅ | Contains post content | | Source | URL | ✅ | Link to the original post | | Type | Single select | ✅ | Value: Linkedin

This n8n workflow demonstrates how to create an agent using LangChain and SQLite. The agent can understand natural language queries and interact with a SQLite database to provide accurate answers. 💪 🚀 Setup Run the top part of the workflow once. It downloads the example SQLite database, extracts from a ZIP file and saves locally (chinook.db). 🗣️ Chatting with Your Data Send a message in a chat window. Locally saved SQLite database loads automatically. User's chat input is combined with the binary data. The LangChain Agend node gets both data and begins to work. The AI Agent will process the user's message, perform necessary SQL queries, and generate a response based on the database information. 🗄️ 🌟 Example Queries Try these sample queries to see the AI Agent in action: "Please describe the database" - Get a high-level overview of the database structure, only one or two queries are needed. "What are the revenues by genre?" - Retrieve revenue information grouped by genre, LangChain agent iterates several time before producing the answer. The AI Agent will store the final answer in its memory, allowing for context-aware conversations. 💬 Read the full article: 👉 https://blog.n8n.io/ai-agents/

Description This workflow automates the process of scraping Google Events data using SerpApi and organizing it in Google Sheets for analysis and tracking. Who's it for Event organizers** who need to monitor competitor events in their area Marketing teams** tracking local events for partnership opportunities Researchers** collecting event data for analysis Business owners** monitoring industry events and conferences How it works The workflow searches Google Events using SerpApi's Google Events engine, processes the returned data, and saves it to a Google Sheets spreadsheet. It handles pagination automatically to collect multiple events and flattens the nested API response into a structured format. What it does Configures search parameters - Sets the search query, total events to fetch, and pagination settings Fetches events via SerpApi - Makes paginated requests to Google Events API with proper rate limiting Processes and flattens data - Transforms nested event data into a flat structure with all relevant fields Saves to Google Sheets - Appends the processed events to a Google Sheets document for easy analysis Requirements SerpApi account** with API key (Get one here) Google Sheets API access** (OAuth2 credentials) Google Sheets document** - Make a copy of this template sheet How to set up Configure SerpApi credentials in the HTTP Request node Set up Google Sheets OAuth2 authentication Update the Google Sheets document ID in the final node to point to your copy Modify search parameters in the "Set Search Parameters" node: Change query to your desired search terms Adjust total_events (10 events per page) Set start position for pagination Run the workflow using the manual trigger How to customize the workflow Search terms**: Modify the query in the Set node (e.g., "conferences in New York", "music events Los Angeles") Event count**: Adjust total_events to fetch more or fewer events Output format**: Modify the Google Sheets column mapping to include/exclude specific fields Rate limiting**: Adjust the requestInterval in the HTTP Request node if needed Scheduling**: Replace the Manual Trigger with a Schedule Trigger for automated runs Output data includes Event title, description, and direct link Start date and timing information Venue and address details Ticket information and pricing Event location map links Event images Original search query for tracking Note: This workflow respects SerpApi rate limits with built-in delays between requests and processes up to 10 events per API call efficiently.

Overview This workflow retrieves all blog and event collection items from a Squarespace site and saves them into a Google Sheets spreadsheet. It uses pagination to fetch 20 items per request, ensuring all content is collected efficiently. How It Works The workflow queries your Squarespace blog and event collections. It fetches data in paginated batches (20 items per page). The retrieved data is formatted and inserted into Google Sheets. The workflow runs on demand or on a schedule, ensuring your data stays up to date. Requirements Credentials To use this template, you need: Your Squarespace collection URL Google Sheets API credentials Google Sheets Setup Use this sample Google Sheets template to get started quickly. Who Is This For? This template is designed for: Bloggers looking to manage and analyze content externally. Businesses and marketers tracking content performance. Anyone who needs an automated way to extract Squarespace blog and event data. Explore More Templates Check out my other n8n templates: 👉 n8n.io/creators/bangank36

Purpose of workflow: The purpose of this workflow is to automate scraping of a website, transforming it into a structured format, and loading it directly into a Google Sheets spreadsheet. How it works: Web Scraping: Uses the Jina AI service to scrape website data and convert it into LLM-friendly text. Information Extraction: Employs an AI node to extract specific book details (title, price, availability, image URL, product URL) from the scraped data. Data Splitting: Splits the extracted information into individual book entries. Google Sheets Integration: Automatically populates a Google Sheets spreadsheet with the structured book data. Step by step setup: Set up Jina AI service: Sign up for a Jina AI account and obtain an API key. Configure the HTTP Request node: Enter the Jina AI URL with the target website. Add the API key to the request headers for authentication. Set up the Information Extractor node: Use Claude AI to generate a JSON schema for data extraction. Upload a screenshot of the target website to Claude AI. Ask Claude AI to suggest a JSON schema for extracting required information. Copy the generated schema into the Information Extractor node. Configure the Split node: Set it up to separate the extracted data into individual book entries. Set up the Google Sheets node: Create a Google Sheets spreadsheet with columns for title, price, availability, image URL, and product URL. Configure the node to map the extracted data to the appropriate columns.

Who is this for? This workflow is designed for: Database administrators and developers working with MongoDB Content managers handling movie databases Organizations looking to implement AI-powered search and recommendation systems Developers interested in combining LangChain, OpenAI, and MongoDB capabilities What problem does this workflow solve? Traditional database queries can be complex and require specific MongoDB syntax knowledge. This workflow addresses: The complexity of writing MongoDB aggregation pipelines The need for natural language interaction with movie databases The challenge of maintaining user preferences and favorites The gap between AI language models and database operations What this workflow does This workflow creates an intelligent agent that: Accepts natural language queries about movies Translates user requests into MongoDB aggregation pipelines Queries a movie database containing detailed information including: Plot summaries Genre classifications Cast and director information Runtime and release dates Ratings and awards Provides contextual responses using OpenAI's language model Allows users to save favorite movies to the database Maintains conversation context using a window buffer memory Setup Required Credentials: OpenAI API credentials MongoDB connection details Node Configuration: Configure the MongoDB connection in the MongoDBAggregate node Set up the OpenAI Chat Model with your API key Ensure the webhook trigger is properly configured for receiving chat messages Database Requirements: A MongoDB collection named "movies" with the specified document structure Proper indexes for efficient querying Appropriate user permissions for read/write operations How to customize this workflow Modify the Document Structure: Update the tool description in the MongoDBAggregate node to match your collection schema Adjust the aggregation pipeline templates for your specific use case Enhance the AI Agent: Customize the prompt in the "AI Agent - Movie Recommendation" node Modify the window buffer memory size based on your context needs Add additional tools for more functionality Extend Functionality: Add more MongoDB operations beyond aggregation Implement additional workflows for different types of queries Create custom error handling and validation Add user authentication and rate limiting Integration Options: Connect to external APIs for additional movie data Add webhook endpoints for different platforms Implement caching mechanisms for frequent queries Add data transformation nodes for specific output formats This workflow serves as a foundation that can be adapted to various use cases beyond movie recommendations, such as e-commerce product search, content management systems, or any scenario requiring intelligent database interaction.

How it Works: Capture Contact Requests:** This template efficiently handles contact requests coming through a WordPress website using the Contact Form 7 (CF7) plugin with a webhook extension. Contact Management:** It automatically creates or updates contacts in Pipedrive upon receiving a new request. Lead Management:** Each contact request is securely stored in the lead inbox of Pipedrive, ensuring no opportunity is missed. Task Creation:** For each new contact or update, the workflow triggers the creation of a related task, streamlining follow-up actions. Note Attachment:** A comprehensive note containing all details from the contact request is attached to the corresponding lead, ensuring that all information is readily accessible. Step-by-Step Guide: Estimated Setup Time: The setup process is straightforward and can be completed quickly. Specific time may vary depending on your familiarity with n8n and the systems involved. Detailed setup instructions are provided within the workflow via sticky notes. These notes offer in-depth guidance for configuring each component of the template to suit your specific needs.

Description: This n8n workflow automates a Discord bot to fetch messages from a specified channel and send AI-generated responses in threads. It ensures smooth message processing and interaction, making it ideal for managing community discussions, customer support, or AI-based engagement. This workflow leverages Redis for memory persistence, ensuring that conversation history is maintained even if the workflow restarts, providing a seamless user experience. How It Works The bot listens for new messages in a specified Discord channel. It sends the messages to an AI model for response generation. The AI-generated reply is posted as a thread under the original message. The bot runs on an Ubuntu server and is managed using PM2 for uptime stability. The Discord bot (Python script) acts as the bridge, capturing messages from Discord and sending them to the n8n webhook. The n8n workflow then processes these messages, interacts with the AI model, and sends the AI's response back to Discord via the bot. Prerequisites to host Bot Sign up on Pella, which is a managed hosting service for Discord Bots. (Easy Setup) A Redis instance for memory persistence. Redis is an in-memory data structure store, used here to store and retrieve conversation history, ensuring that the AI can maintain context across multiple interactions. This is crucial for coherent and continuous conversations. Set Up Steps 1️⃣ Create a Discord Bot Go to the Discord Developer Portal. Click “New Application”, enter a name, and create it. Navigate to Bot > Reset Token, then copy the Bot Token. Enable Privileged Gateway Intents (Presence, Server Members, Message Content). Under OAuth2 > URL Generator, select bot scope and required permissions. Copy the generated URL, open it in a browser, select your server, and click Authorize. 2️⃣ Deploy the Bot on Pella Create a new folder discord-bot and navigate into it: Create and configure an .env file to store your bot token: Copy the code to .env: (You can copy the webhook URL from the n8n workflow) TOKEN=your-bot-token-here WEBHOOK_URL=https://your-domain.tld/webhook/getmessage Create file main.py copy the below code and save it: Copy this Bot script to main.py: import discord import requests import json import os from dotenv import load_dotenv Load environment variables from .env file load_dotenv() TOKEN = os.getenv("TOKEN") WEBHOOK_URL = os.getenv("WEBHOOK_URL") Bot Configuration LISTEN_CHANNELS = ["YOUR_CHANNEL_ID_1", "YOUR_CHANNEL_ID_2"] # Replace with your target channel IDs Intents setup intents = discord.Intents.default() intents.messages = True # Enable message event intents.guilds = True intents.message_content = True # Required to read messages client = discord.Client(intents=intents) @client.event async def on_ready(): print(f'Logged in as {client.user}') @client.event async def on_message(message): if message.author == client.user: return # Ignore bot's own messages if str(message.channel.id) in LISTEN_CHANNELS: try: fetched_message = await message.channel.fetch_message(message.id) # Ensure correct fetching payload = { "channel_id": str(fetched_message.channel.id), # Ensure it's string "chat_message": fetched_message.content, "timestamp": str(fetched_message.created_at), # Ensure proper formatting "message_id": str(fetched_message.id), # Ensure ID is a string "user_id": str(fetched_message.author.id) # Ensure user ID is also string } headers = {'Content-Type': 'application/json'} response = requests.post(WEBHOOK_URL, data=json.dumps(payload), headers=headers) if response.status_code == 200: print(f"Message sent successfully: {payload}") else: print(f"Failed to send message: {response.status_code}, Response: {response.text}") except Exception as e: print(f"Error fetching message: {e}") client.run(TOKEN) Create requirements.txt and copy: discord python-dotenv 3️⃣ Follow the video to set up the bot which will run 24/7 Tutorial - https://www.youtube.com/watch?v=rNnK3XlUtYU Note: Free Plan will expire after 24 hours, so please opt for the Paid Plan in Pella to keep your bot running. 4️⃣ n8n Workflow Configuration The n8n workflow consists of the following nodes: Get Discord Messages (Webhook):** This node acts as the entry point for messages from the Discord bot. It receives the channel_id, chat_message, timestamp, message_id, and user_id from Discord when a new message is posted in the configured channel. Its webhook path is /getmessage and it expects a POST request. Chat Agent (Langchain Agent):** This node processes the incoming Discord message (chat_message). It is configured as a conversational agent, integrating the language model and memory to generate an appropriate response. It also has a prompt to keep the reply concise, under 1800 characters. OpenAI -4o-mini (Langchain Language Model):** This node connects to the OpenAI API and uses the gpt-4o-mini-2024-07-18 model for generating AI responses. It is the core AI component of the workflow. Message History (Redis Chat Memory):** This node manages the conversation history using Redis. It stores and retrieves chat messages, ensuring the Chat Agent maintains context for each user based on their user_id. This is critical for coherent multi-turn conversations. Calculator (Langchain Tool):** This node provides a calculator tool that the AI agent can utilize if a mathematical calculation is required within the conversation. This expands the capabilities of the AI beyond just text generation. Response fromAI (Discord):** This node sends the AI-generated response back to the Discord channel. It uses the Discord Bot API credentials and replies in a thread under the original message (message_id) in the specified channel_id. Sticky Note1, Sticky Note2, Sticky Note3, Sticky Note4, Sticky Note5, Sticky Note:** These are informational nodes within the workflow providing instructions, code snippets for the Discord bot, and setup guidance for the user. These notes guide the user on setting up the .env file, requirements.txt, the Python bot code, and general recommendations for channel configuration and adding tools. 5️⃣ Setting up Redis Choose a Redis Hosting Provider: You can use a cloud provider like Redis Labs, Aiven, or set up your own Redis instance on a VPS. Obtain Redis Connection Details: Once your Redis instance is set up, you will need the host, port, and password (if applicable). Configure n8n Redis Nodes: In your n8n workflow, configure the "Message History" node with your Redis connection details. Ensure the Redis credential ✅ redis-for-n8n is properly set up with your Redis instance details (host, port, password). 6️⃣ Customizing the Template AI Model:** You can easily swap out the "OpenAI -4o-mini" node with any other AI service supported by n8n (e.g., Cohere, Hugging Face) to use a different language model. Ensure the new language model node is connected to the ai_languageModel input of the "Chat Agent" node. Agent Prompt:** Modify the text parameter in the "Chat Agent" node to change the AI's persona, provide specific instructions, or adjust the response length. Additional Tools:** The "Calculator" node is an example of an AI tool. You can add more Langchain tool nodes (e.g., search, data lookup) and connect them to the ai_tool input of the "Chat Agent" node to extend the AI's capabilities. Refer to the "Sticky Note5" in the workflow for a reminder. Channel Filtering:** Adjust the LISTEN_CHANNELS list in the main.py file of your Discord bot to include or exclude specific Discord channel IDs where the bot should listen for messages. Thread Management:** The "Response fromAI" node can be modified to change how threads are created or managed, or to send responses directly to the channel instead of a thread. The current setup links the response to the original message ID (message_reference). 7️⃣ Testing Instructions Start the Discord Bot: Ensure your main.py script is running on Pella. Activate the n8n Workflow: Make sure your n8n workflow is active and listening for webhooks. Send a Message in Discord: Go to one of the LISTEN_CHANNELS in your Discord server and send a message. Verify Response: The bot should capture the message, send it to n8n, receive an AI-generated response, and post it as a thread under your original message. Check Redis: Verify that the conversation history is being stored and updated correctly in your Redis instance. Look for keys related to user IDs. ✅ Now your bot is running in the background! 🚀

Automate Sports Betting Data with TheOddsAPI This workflow enables you to create and update a table using TheOddsAPI for sports betting data. It automatically pulls upcoming Ice Hockey games at the start of the day and updates the table with results at the end of the day. You can modify it to retrieve odds and game data for any sport. This setup is particularly useful for sports betting applications, such as tracking the results of a predictive model. It leverages scheduled triggers to activate HTTP requests, which then create or update fields in Airtable by matching on the game ID. Prerequisites Before implementing this workflow, ensure you have the following: TheOddsAPI Account & API Key Sign up at TheOddsAPI and obtain an API key. Ensure you have the correct API permissions to access sports odds and results. Airtable Account & API Key Create an account at Airtable and set up a database. Obtain an API key from the Account Settings page. API Access & Rate Limits Review TheOddsAPI’s rate limits and ensure your account tier allows for scheduled API calls. Confirm that Airtable API limits align with your expected data retrieval frequency. Step-by-Step Guide to Integrating TheOddsAPI 1. Schedule API Requests Set up a trigger to automatically pull upcoming Ice Hockey games at the start of each day. 2. Fetch Data from TheOddsAPI Retrieve the latest sports betting data, including game details and odds, using TheOddsAPI. 3. Store Data in Airtable Insert or update records in Airtable by matching game IDs, ensuring data accuracy. Sample Airtable Template Column Setup for Ice Hockey (Table can adjust depending on sport and data needs. Reference TheOddsAPI for more documentation.) Game ID** Sport** League** Game Date (UTC)** Home Team** Away Team** Completed** (Boolean: TRUE/FALSE for game completion status) Scores** (JSON or String for final scores) Last Update** (Timestamp of the latest update) 4. Schedule an End-of-Day Update Configure another trigger to fetch final game results at the end of the day. 5. Update Records in Airtable Modify existing Airtable records with final scores and game outcomes for complete tracking. 6. Customize for Other Sports Adjust API parameters to retrieve data for different sports and betting odds, making the system flexible for multiple use cases. This structured workflow automates sports betting data collection and updates, ensuring accurate and real-time tracking of odds and game results. By integrating TheOddsAPI with Airtable, you can build scalable applications for predictive sports analytics and betting insights.

🔥 AI Lead Scoring Agent: Smart Contact Form Triager Automatically score every contact form lead as Hot/Warm/Cold and alert your sales team instantly. This intelligent workflow captures contact form submissions, uses GPT-4 to analyze message content and score lead quality, then sends formatted alerts to Slack - ensuring your sales team always focuses on the hottest prospects first. 🚀 What It Does Instant Lead Capture: Automatically receives contact form submissions via webhook endpoint AI-Powered Scoring: GPT-4 analyzes message content and classifies leads as Hot 🔥, Warm 🌤, or Cold ❄️ Smart Data Extraction: Cleanly extracts name, email, and message from form submissions Real-Time Slack Alerts: Sends formatted notifications to your sales team with lead details and AI scoring 🎯 Key Benefits ✅ Never Miss Hot Prospects: AI identifies urgent leads automatically ✅ Save Sales Time: Focus effort on highest-probability leads first ✅ Instant Team Alerts: Real-time notifications in Slack channels ✅ Smart Prioritization: AI scoring eliminates guesswork in lead quality ✅ Zero Manual Work: Complete automation from form to sales alert ✅ Universal Integration: Works with any contact form or landing page 🏢 Perfect For Sales & Marketing Teams SaaS companies managing inbound leads Service businesses qualifying prospects E-commerce stores identifying serious buyers Agencies prioritizing client inquiries Business Applications Lead Qualification**: Identify purchase-ready prospects instantly Sales Efficiency**: Focus team effort on highest-value opportunities Response Prioritization**: Handle urgent inquiries first Team Coordination**: Keep entire sales team informed of new leads ⚙️ What's Included Complete Workflow: Ready-to-deploy lead scoring automation Webhook Endpoint: Receives submissions from any contact form AI Classification: GPT-4 powered lead interest analysis Slack Integration: Professional team notifications with emojis and formatting Data Processing: Clean extraction and formatting of lead information 🔧 Quick Setup Requirements n8n Platform**: Cloud or self-hosted instance OpenAI API**: GPT-4 access for lead scoring Slack Workspace**: Team channel for lead notifications Contact Form**: Any form that can POST to webhook endpoint 📱 Sample Slack Alert 🔥 New Lead: Sarah Johnson (sarah@techstartup.com) Message: "We're looking for a project management solution for our 50-person team. Need to implement ASAP as we're scaling fast. Can we schedule a demo this week?" Triage: 🔥 Hot ❄️ New Lead: John Smith (john@email.com) Message: "Just browsing your website. Might be interested in learning more someday." Triage: ❄️ Cold 🎨 Customization Options Scoring Criteria: Adjust AI prompts for industry-specific lead qualification Team Channels: Route different lead types to specific Slack channels Additional Fields: Capture company size, budget, timeline data CRM Integration: Connect to Salesforce, HubSpot, or Pipedrive Follow-up Automation: Trigger email sequences based on lead temperature Analytics Tracking: Monitor lead quality trends and conversion rates 🏷️ Tags & Categories #lead-scoring #sales-automation #contact-form-processing #ai-qualification #slack-integration #prospect-management #inbound-marketing #sales-productivity #lead-generation #openai-integration #webhook-automation #crm-automation #sales-alerts #lead-triage #ai-agent 💡 Use Case Examples SaaS Company: Score demo requests based on company size and urgency mentions Consulting Firm: Identify clients ready to start projects vs those still researching E-commerce Store: Spot bulk buyers and wholesale inquiries vs casual browsers Marketing Agency: Prioritize clients with specific budgets and timelines mentioned 📈 Expected Results 70% faster** lead response times through smart prioritization 3x higher** conversion rates focusing on Hot leads first 50% time savings** on manual lead qualification 100% lead coverage** - never miss or ignore a prospect again 🛠️ Setup & Support 5-Minute Setup: Simple webhook configuration with any contact form Universal Integration: Works with WordPress, Webflow, custom forms, landing pages Team Training: Clear Slack notification format anyone can understand Scalable: Handles unlimited form submissions automatically 📞 Get Help & Resources YouTube: https://www.youtube.com/@YaronBeen/videos 💼 Sales Automation Support LinkedIn: https://www.linkedin.com/in/yaronbeen/ 📧 Direct Help Email: Yaron@nofluff.online - Response within 24 hours Ready to never miss another hot lead? Get this AI Lead Scoring Agent and transform your contact forms into intelligent lead qualification systems. Your sales team will always know which prospects to call first, and you'll never waste time on cold leads again. Stop treating all leads equally. Start prioritizing the ones ready to buy.

Who is this for? This workflow is ideal for businesses, accountants, and finance teams who receive bank slip images via LINE and want to automate the extraction of transaction details. It eliminates manual data entry and speeds up financial tracking. What problem does this workflow solve? Many businesses receive bank transfer slips via LINE from customers, but manually recording transaction details into spreadsheets is time-consuming and error-prone. This workflow automates the entire process, extracting structured data from the bank slips and storing it in Google Sheets for seamless record-keeping. What this workflow does: Receives bank slip images from LINE BOT Extracts transaction details (sender, receiver, amount, transaction ID) using SpaceOCR Automatically logs extracted data into Google Sheets Works with Standard Bank Slips & PromptPay transactions Eliminates manual data entry and reduces errors Setup Instructions: 1. Prerequisites A LINE BOT with Messaging API enabled A SpaceOCR API Key (Get from https://spaceocr.com/) A Google Sheets account to store extracted data An n8n instance running (Cloud or Self-hosted) 2. Setup Google Sheets Create a Google Sheet with the following structure: A (Date) B (Time) C (Sender) D (Receiver) E (Bank Name) F (Amount) G (Transaction ID) Ensure your Google Sheets API is enabled and connected to n8n. For an example of the required format, check this Google Sheets template: Google Sheets Template 3. Configure n8n Workflow 1. Webhook Node (Receives bank slip from LINE BOT) Set method:* Set Path:* 2. HTTP Request (Download Image from LINE Message) Retrieves image URL from the LINE message payload 3. SpaceOCR Node (Extract Text from Bank Slip) Input:* API Key:* #### 4. Google Sheets Node (Save Transaction Data) Select your Google Sheet Map extracted data (sender, receiver, amount, etc.) to the respective columns 4. Deploy & Test Activate the workflow in n8n Set Webhook URL in LINE Developer Console Send a test bank slip image to the LINE BOT Check Google Sheets for extracted transaction data