Workflow Templates

This workflow allows users to generate AI videos using the cheaper model Google Veo3 Fast, save them to Google Drive, generate optimized titles with GPT-4o, and automatically upload them to YouTube and TikTok with Upload-Post. The entire process is triggered from a Google Sheet that acts as the central interface for input and output. IT automates video creation, uploading, and tracking, ensuring seamless integration between Google Sheets, Google Drive, Google Veo3 Fast, TikTok and YouTube. Benefits of this Workflow 💡 No Code Interface**: Trigger and control the video production pipeline from a simple Google Sheet. ⚙️ Full Automation**: Once set up, the entire video generation and publishing process runs hands-free. 🧠 AI-Powered Creativity**: Generates engaging YouTube and TikTok titles using GPT-4o. Leverages advanced generative video AI from Google Veo3. 📁 Cloud Storage & Backup**: Stores all generated videos on Google Drive for safekeeping. 📈 YouTube Ready**: Automatically uploads to YouTube with correct metadata, saving time and boosting visibility. 📈 TikTok Ready**: Automatically uploads to TikTok with correct metadata, saving time and boosting visibility. 🧪 Scalable**: Designed to process multiple video prompts by looping through new entries in Google Sheets. 🔒 API-First**: Utilizes secure API-based communication for all services. How It Works Trigger: The workflow can be started manually ("When clicking ‘Test workflow’") or scheduled ("Schedule Trigger") to run at regular intervals (e.g., every 5 minutes). Fetch Data: The "Get new video" node retrieves unfilled video requests from a Google Sheet (rows where the "VIDEO" column is empty). Video Creation: The "Set data" node formats the prompt and duration from the Google Sheet. The "Create Video" node sends a request to the Fal.run API (Google Veo3 Fast) to generate a video based on the prompt. Status Check: The "Wait 60 sec." node pauses execution for 60 seconds. The "Get status" node checks the video generation status. If the status is "COMPLETED," the workflow proceeds; otherwise, it waits again. Video Processing: The "Get Url Video" node fetches the video URL. The "Generate title" node uses OpenAI (GPT-4.1) to create an SEO-optimized YouTube and TikTok title. The "Get File Video" node downloads the video file. Upload & Update: The "Upload Video" node saves the video to Google Drive. The "HTTP Request" node uploads the video to YouTube via the Upload-Post API. The "HTTP Request" node uploads the video to TikTok via the Upload-Post API. The "Update Youtube URL" and "Update result" nodes update the Google Sheet with the video URL and YouTube link. Set Up Steps Google Sheet Setup: Create a Google Sheet with columns: PROMPT, DURATION, VIDEO, and YOUTUBE_URL. Share the Sheet link in the "Get new video" node. API Keys: Obtain a Fal.run API key (for Veo3) and set it in the "Create Video" node (Header: Authorization: Key YOURAPIKEY). Get an Upload-Post API key (for YouTube uploads) and configure the "HTTP Request" node (Header: Authorization: Apikey YOUR_API_KEY). Get an Upload-Post API key (for TikTok uploads) and configure the "HTTP Request" node (Header: Authorization: Apikey YOUR_API_KEY). YouTube Upload Configuration: Replace YOUR_USERNAME in the "HTTP Request" node with your Upload-Post profile name. Schedule Trigger: Configure the "Schedule Trigger" node to run periodically (e.g., every 5 minutes). Need help customizing? Contact me for consulting and support or add me on Linkedin.

🤖AI & Machine Learning

4 nodes6520 views

Track US Fintech & Healthtech Funding Rounds: Crunchbase to Google Sheets

by Yaron Been

Automatically monitor and track funding rounds in the US Fintech and Healthtech sectors using Crunchbase API, with daily updates pushed to Google Sheets for easy analysis and monitoring. 🚀 What It Does Daily Monitoring**: Automatically checks for new funding rounds every day at 8 AM Smart Filtering**: Focuses on US-based Fintech and Healthtech companies Data Enrichment**: Extracts and formats key funding information Automated Storage**: Pushes data to Google Sheets for easy access and analysis 🎯 Perfect For VC firms tracking investment opportunities Startup founders monitoring market activity Market researchers analyzing funding trends Business analysts tracking competitor funding ⚙️ Key Benefits ✅ Real-time funding round monitoring ✅ Focused industry tracking (Fintech & Healthtech) ✅ Automated data collection and organization ✅ Structured data output in Google Sheets ✅ Complete funding details including investors and amounts 🔧 What You Need Crunchbase API key Google Sheets account n8n instance Basic spreadsheet setup 📊 Data Collected Company Name Industry Funding Round Type Announced Date Money Raised (USD) Investors Crunchbase URL 🛠️ Setup & Support Quick Setup Deploy in 30 minutes with our step-by-step configuration guide 📺 Watch Tutorial 💼 Get Expert Support 📧 Direct Help Stay ahead of market movements with automated funding round tracking. Transform manual research into an efficient, automated process.

Batch Process Prompts with Anthropic Claude API

by Greg Evseev

This workflow template provides a robust solution for efficiently sending multiple prompts to Anthropic's Claude models in a single batch request and retrieving the results. It leverages the Anthropic Batch API endpoint (/v1/messages/batches) for optimized processing and outputs each result as a separate item. Core Functionality & Example Usage Included This template includes: The Core Batch Processing Workflow: Designed to be called by another n8n workflow. An Example Usage Workflow: A separate branch demonstrating how to prepare data and trigger the core workflow, including examples using simple strings and n8n's Langchain Chat Memory nodes. Who is this for? This template is designed for: Developers, data scientists, and researchers** who need to process large volumes of text prompts using Claude models via n8n. Content creators** looking to generate multiple pieces of content (e.g., summaries, Q&As, creative text) based on different inputs simultaneously. n8n users** who want to automate interactions with the Anthropic API beyond single requests, improve efficiency, and integrate batch processing into larger automation sequences. Anyone needing to perform bulk text generation or analysis tasks with Claude programmatically. What problem does this workflow solve? Sending prompts to language models one by one can be slow and inefficient, especially when dealing with hundreds or thousands of requests. This workflow addresses that by: Batching:** Grouping multiple prompts into a single API call to Anthropic's dedicated batch endpoint (/v1/messages/batches). Efficiency:** Significantly reducing the time required compared to sequential processing. Scalability:** Handling large numbers of prompts (up to API limits) systematically. Automation:** Providing a ready-to-use, callable n8n structure for batch interactions with Claude. Structured Output:** Parsing the results and outputting each individual prompt's result as a separate n8n item. Use Cases: Bulk content generation (e.g., product descriptions, summaries). Large-scale question answering based on different contexts. Sentiment analysis or data extraction across multiple text snippets. Running the same prompt against many different inputs for research or testing. What the Core Workflow does (Triggered by the 'When Executed by Another Workflow' node) Receive Input: The workflow starts when called by another workflow (e.g., using the 'Execute Workflow' node). It expects input data containing: anthropic-version (string, e.g., "2023-06-01") requests (JSON array, where each object represents a single prompt request conforming to the Anthropic Batch API schema). Submit Batch Job: Sends the formatted requests data via POST to the Anthropic API /v1/messages/batches endpoint to create a new batch job. Requires Anthropic credentials. Wait & Poll: Enters a loop: Checks if the processing_status of the batch job is ended. If not ended, it waits for a set interval (10 seconds by default in the 'Batch Status Poll Interval' node). It then checks the batch job status again via GET to /v1/messages/batches/{batch_id}. Requires Anthropic credentials. This loop continues until the status is ended. Retrieve Results: Once the batch job is complete, it fetches the results file by making a GET request to the results_url provided in the batch status response. Requires Anthropic credentials. Parse Results: The results are typically returned in JSON Lines (.jsonl) format. The 'Parse response' Code node splits the response text by newlines and parses each line into a separate JSON object, storing them in an array field (e.g., parsed). Split Output: The 'Split Out Parsed Results' node takes the array of parsed results and outputs each result object as an individual item from the workflow. Prerequisites An active n8n instance (Cloud or self-hosted). An Anthropic API account with access granted to Claude models and the Batch API. Your Anthropic API Key. Basic understanding of n8n concepts (nodes, workflows, credentials, expressions, 'Execute Workflow' node). Familiarity with JSON data structures for providing input prompts and understanding the output. Understanding of the Anthropic Batch API request/response structure. (For Example Usage Branch) Familiarity with n8n's Langchain nodes (@n8n/n8n-nodes-langchain) if you plan to adapt that part. Setup Import Template: Add this template to your n8n instance. Configure Credentials: Navigate to the 'Credentials' section in your n8n instance. Click 'Add Credential'. Search for 'Anthropic' and select the Anthropic API credential type. Enter your Anthropic API Key and save the credential (e.g., name it "Anthropic account"). Assign Credentials: Open the workflow and locate the three HTTP Request nodes in the core workflow: Submit batch Check batch status Get results In each of these nodes, select the Anthropic credential you just configured from the 'Credential for Anthropic API' dropdown. Review Input Format: Understand the required input structure for the When Executed by Another Workflow trigger node. The primary inputs are anthropic-version (string) and requests (array). Refer to the Sticky Notes in the template and the Anthropic Batch API documentation for the exact schema required within the requests array. Activate Workflow: Save and activate the core workflow so it can be called by other workflows. ➡️ Quick Start & Input/Output Examples: Look for the Sticky Notes within the workflow canvas! They provide crucial information, including examples of the required input JSON structure and the expected output format. How to customize this workflow Input Source:* The core workflow is designed to be called. You will build *another workflow that prepares the anthropic-version and requests array and then uses the 'Execute Workflow' node to trigger this template. The included example branch shows how to prepare this data. Model Selection & Parameters:* Model (claude-3-opus-20240229, etc.), max_tokens, temperature, and other parameters are defined *within each object inside the requests array you pass to the workflow trigger. You configure these in the workflow calling this template. Polling Interval:** Modify the 'Wait' node ('Batch Status Poll Interval') duration if you need faster or slower status checks (default is 10 seconds). Be mindful of potential rate limits. Parsing Logic:** If Anthropic changes the result format or you have specific needs, modify the Javascript code within the 'Parse response' Code node. Error Handling:** Enhance the workflow with more specific error handling for API failures (e.g., using 'Error Trigger' or checking HTTP status codes) or batch processing issues (batch.status === 'failed'). Output Processing:* In the workflow that *calls this template, add nodes after the 'Execute Workflow' node to process the individual result items returned (e.g., save to a database, spreadsheet, send notifications). Example Usage Branch (Manual Trigger) This template also contains a separate branch starting with the Run example Manual Trigger node. Purpose:** This branch demonstrates how to construct the necessary anthropic-version and requests array payload. Methods Shown:** It includes steps for: Creating a request object from a simple query string. Creating a request object using data from n8n's Langchain Chat Memory nodes (@n8n/n8n-nodes-langchain). Execution:** It merges these examples, constructs the final payload, and then uses the Execute Workflow node to call the main batch processing logic described above. It finishes by filtering the results for demonstration. Note:** This branch is for demonstration and testing. You would typically build your own data preparation logic in a separate workflow. The use of Langchain nodes is optional for the core batch functionality. Notes API Limits:** According to the Anthropic API documentation, batches can contain up to 100,000 requests and be up to 256 MB in total size. Ensure your n8n instance has sufficient resources for large batches. API Costs:** Using the Anthropic API, including the Batch API, incurs costs based on token usage. Monitor your usage via the Anthropic dashboard. Completion Time:** Batch processing time depends on the number and complexity of prompts and current API load. The polling mechanism accounts for this variability. Versioning:** Always include the anthropic-version header in your requests, as shown in the workflow and examples. Refer to Anthropic API versioning documentation.

Telegram chat with PDF

by felipe biava cataneo

What this template does This template serves as a Chatbot that enables you to ask questions about the content of a PDF directly in Telegream. It checks incoming Telegram messages if they contain a document. If they do, it stores the PDF in a Pinecone Vector store. If there's no document, it will search the Vector Store for information and try to answer your question. Setup Open the Telegram app and search for the BotFather user (@BotFather) Start a chat with the BotFather Type /newbot to create a new bot Follow the prompts to name your bot and get a unique API token Save your access token and username Once you set your bot, you can send the pdf, and then ask questions about the content. How to adjust it to your needs You can exchange the Groq chat model with any model that you like Exchange Pinecone with any other vector store tool you like (e.g. Supabase, Postgres or QDrant) #Telegram, #Pinecone, #Openai, #GroQ

9 nodes16338 views

Intelligent Email Organization with AI-Powered Content Classification for Gmail

by Niranjan G

This workflow leverages AI to intelligently analyze incoming Gmail messages and automatically apply relevant labels based on the email content. The default configuration includes the following labels: Newsletter**: Subscription updates or promotional content. Inquiry**: Emails requesting information or responses. Invoice**: Billing and payment-related emails. Proposal**: Business offers or collaboration opportunities. Action Required**: Emails demanding immediate tasks or actions. Follow-up Reminder**: Emails prompting follow-up actions. Task**: Emails containing actionable tasks. Personal**: Non-work-related emails. Urgent**: Time-sensitive or critical communications. Bank**: Banking alerts and financial statements. Job Update**: Recruitment or job-related communications. Spam/Junk**: Unwanted or irrelevant bulk emails. Social/Networking**: Notifications from social platforms. Receipt**: Purchase confirmations and receipts. Event Invite**: Invitations or calendar-related messages. Subscription Renewal**: Reminders for subscription expirations. System Notification**: Technical alerts from services or systems. You can customize labels and definitions based on your specific use case. How it works: The workflow periodically retrieves new Gmail messages. Only emails without existing labels, regardless of read status, are sent to the AI for analysis. Email content (subject and body) is analyzed by an AI model to determine the appropriate label. Labels identified by the AI are applied to each email accordingly. Note: This workflow performs 100% better than the default Gmail trigger method, which is why the workflow was switched from Gmail trigger to a scheduled workflow. By selectively processing only unlabeled emails, it ensures comprehensive labeling while significantly reducing AI processing costs. Setup Steps: Configure credentials for Gmail and your chosen AI service (e.g., OpenAI). Ensure labels exist in your Gmail account matching the workflow definitions. Adjust the AI prompt to match your labeling needs. Optionally customize the polling interval (default: every 2 minutes). This workflow streamlines your email management, keeping your inbox organized effortlessly while optimizing resource usage.

4 nodes20913 views

🛠️ Gong Tool MCP Server

by David Ashby

Complete MCP server exposing all Gong Tool operations to AI agents. Zero configuration needed - all 4 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every Gong Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Gong Tool tool with full error handling 📋 Available Operations (4 total) Every possible Gong Tool operation is included: 🔧 Call (2 operations) • Get call • Get many calls 👤 User (2 operations) • Get user • Get many users 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native Gong Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every Gong Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

🛠️ State Management System for Long-Running Workflows with Wait Nodes

by Lucas Peyrin

How it works This template is a powerful, reusable utility for managing stateful, long-running processes. It allows a main workflow to be paused indefinitely at "checkpoints" and then be resumed by external, asynchronous events. This pattern is essential for complex automations and I often call it the "Async Portal" or "Teleport" pattern. The template consists of two distinct parts: The Main Process (Top Flow): This represents your primary business logic. It starts, performs some actions, and then calls the Portal to register itself before pausing at a Wait node (a "Checkpoint"). The Async Portal (Bottom Flow): This is the state-management engine. It uses Workflow Static Data as a persistent memory to keep track of all paused processes. When an external event (like a new chat message or an approval webhook) comes in with a specific session_id, the Portal looks up the corresponding paused workflow and "teleports" the new data to it by calling its unique resume_url. This architecture allows you to build sophisticated systems where the state is managed centrally, and your main business logic remains clean and easy to follow. When to use this pattern This is an advanced utility ideal for: Chatbots:** Maintaining conversation history and context across multiple user messages. Human-in-the-Loop Processes:** Pausing a workflow to wait for a manager's approval from an email link or a form submission. Multi-Day Sequences:** Building user onboarding flows or drip campaigns that need to pause for hours or days between steps. Any process that needs to wait for an unpredictable external event** without timing out. Set up steps This template is a utility designed to be copied into your own projects. The workflow itself is a live demonstration of how to use it. Copy the Async Portal: In your own project, copy the entire Async Portal (the bottom flow, starting with the A. Entry: Receive Session Info trigger) into your workflow. This will be your state management engine. Register Your Main Process: At the beginning of your main workflow, use an Execute Workflow node to call the Portal's trigger. You must pass it a unique session_id for the process and the resume_url from a Wait node. Add Checkpoints: Place Wait nodes in your main workflow wherever you need the process to pause and wait for an external event. Trigger the Portal: Configure your external triggers (e.g., your chatbot's webhook) to call the Portal's entry trigger, not your main workflow's trigger. You must pass the same session_id so the Portal knows which paused process to resume. To see it in action, follow the detailed instructions in the "How to Test This Workflow" sticky note on the canvas.

beginner

2 nodes2 views

Automate Product Training & Customer Support via WhatsApp, GPT-4 & Google Sheets

by Dr. Firas

WhatsApp AI Agent: Auto-Train Product Data & Handle Customer Support Who Is This For This workflow is ideal for eCommerce founders, product managers, customer support teams, and automation builders who rely on WhatsApp to manage product information and interact with clients. It’s perfect for businesses that want to automate product data entry and support responses directly from WhatsApp messages using GPT-4 and Google Sheets. What Problem Does This Workflow Solve Manual Product Data Entry**: Collecting and organizing product data from links is tedious and error-prone. Slow Customer Response Times**: Responding to client questions manually leads to delays and inconsistent support. No Logging System for Issues**: Without automation, support issues often go undocumented, making it harder to learn and improve. What This Workflow Does Step 1 – Incoming Message Detection Listens for incoming messages via WhatsApp. If the message starts with train:, it routes to the product training process. Otherwise, it routes to the customer support assistant. Step 2 – Product Data Training Extracts URL** from the message using a regex script. Fetches HTML content** from the URL. Cleans HTML data** to extract readable product description. Saves raw data** (URL + description) into Google Sheets. Uses GPT-4** to enhance product data: → Name, price (one-time or subscription), topic, and FAQs. Updates the product row** in Google Sheets with structured information. Step 3 – Customer Support Flow Analyzes user messages with GPT-4 to understand the request or issue. Looks up relevant product info in Google Sheets. Detects potential problems (e.g. payment, login, delivery). Suggests an appropriate solution. Logs the problem, solution, and category to the Customer Issues sheet. Sends a response back to the client via WhatsApp. Step 4 – Client Response Sends the AI-generated response to the client via WhatsApp. Keeps the communication fast, clear, and professional. Setup Guide Prerequisites WhatsApp Business API access** OpenAI API Key** Google Account** with Google Sheets access A hosted instance of n8n (Cloud or self-hosted) Setup Steps Import the Workflow into your n8n instance. Connect your credentials for WhatsApp, OpenAI, and Google Sheets. Customize Google Sheet IDs and names as needed. Test by sending a train: message or a regular customer message to WhatsApp. Activate the workflow to make it live. How to Customize This Workflow Edit AI prompts** to reflect your product type, language style, or tone. Change the trigger keyword** (e.g. from train: to add: or anything else). Add integrations** like Notion, Airtable, or CRM tools. Expand the Sheets structure** with more product fields (e.g. stock status, image link). Add notifications** to Slack or email after product updates or issue logging. 📄 Documentation: Notion Guide Need help customizing? Contact me for consulting and support : Linkedin / Youtube

7 nodes32255 views

Generate AI-Powered LinkedIn Posts with Google Gemini and Gen-Imager

by Evoort Solutions

🚀 AI-Powered LinkedIn Post Automation 🧩 How It Works This workflow automatically generates LinkedIn posts based on a user-submitted topic, including both content creation and image generation, then publishes the post to LinkedIn. Ideal for marketers, content creators, or businesses looking to streamline their social media activity, without the need for manual post creation. High-Level Workflow: Trigger: The workflow is triggered when a user submits a form with a topic for the LinkedIn post. Data Mapping: The topic is mapped and prepared for the AI model. AI Content Generation: Calls the Google Gemini AI model to generate engaging post content and a visual image prompt. Image Creation: Sends the image prompt to the external API, gen-imager, to generate a professional image matching the topic. Post Creation: Publishes the text and image to LinkedIn, automatically updating the user's feed. ⚙️ Set Up Steps (Quick Overview) 🕐 Estimated Setup Time: ~10–20 minutes Connect Google Gemini: Set up your Google Gemini API credentials to interact with the AI model for content creation. Set Up External Image API: Configure the external image generation API (gen-imager API) for visual creation based on the post prompt. Connect LinkedIn: Set up OAuth2 credentials to authenticate your LinkedIn account and allow publishing posts. Form Submission Setup: Create a simple web form for users to submit the topic for LinkedIn posts. Activate the Workflow: Once everything is connected, activate the workflow. It will trigger automatically upon receiving form submissions. 💡 Important Notes: The flow uses Google Gemini (PaLM) for generating content based on the user's topic. Text to Image: The image generation process involves creating a professional, LinkedIn-appropriate image based on the post’s topic using the **gen-imager API. You can customize the visual elements of the posts and adjust the tone of the generated content based on preferences. 🛠 Detailed Node Breakdown: On Form Submission Trigger: Captures the user-submitted topic and initializes the workflow. Action: Start the process by gathering the topic information. Mapper (Field Mapping) Action: Maps the captured topic to a variable that is passed along for content generation. AI Agent (Content Generation) Action: Calls Google Gemini to generate professional LinkedIn post content and an image prompt based on the submitted topic. Key: Outputs content in a structured form — post text and image prompt. Google Gemini Chat Model Action: AI model that generates actionable insights, engaging copy, and an image prompt for LinkedIn post. Normalizer (Data Cleanup) Action: Cleans the output from the AI model to ensure the content and image prompt are correctly formatted for use in the next steps. Text to Image (Image Generation) Action: Sends the image prompt to the gen-imager API, which returns a custom image based on the post's topic. Decoder (Base64 Decoding) Action: Decodes the image from base64 format for easier uploading to LinkedIn. LinkedIn (Post Creation) Action: Publishes the generated text and image to LinkedIn, automatically creating a polished post for the user’s feed. ⏱ Execution Time Breakdown: Total Estimated Execution Time**: ~15–40 seconds per workflow run. On Form Submission: Instant (Trigger) Mapper (Field Mapping): ~1–2 seconds AI Content Generation: ~5–10 seconds (depending on server load) Text to Image: ~5–15 seconds (depends on external API) LinkedIn Post Creation: ~2–5 seconds 🚀 Ready to Get Started? Let’s get you started with automating your LinkedIn posts! Create your free n8n account and set up the workflow using this link. 📝 Notes & Customizations Form Fields**: Customize the form to gather more specific information for the LinkedIn posts (like audience targeting, post category, etc.). Image API Customization**: Adjust the image generation prompt to fit your brand’s style, or change the color palette as needed. Content Tone**: The tone can be adjusted by modifying the system message sent to Google Gemini for content generation.

5 nodes3466 views

Automate CSAT Surveys with Freshdesk & Store Responses in Google Sheets

by PollupAI

Never forget to send a satisfaction survey again! This workflow helps you automatically send CSAT surveys when a Freshdesk ticket is marked “Resolved” – and logs every response in Google Sheets for easy analysis, reporting, and escalation workflows. 💡 Built for CS and ops teams who care about real feedback This template is perfect for: Customer Support Teams who want timely, consistent survey delivery after every resolved ticket. Ops Leads & Admins tired of managing spreadsheets and survey tools manually. Businesses using Freshdesk looking for a no-code feedback loop. Automation fans who want to track, trigger, and take action — automatically. 🧩 What problem does it solve? Manual survey processes are slow, inconsistent, and hard to scale. This automation ensures: Fast survey delivery when experiences are still fresh. No duplicate emails thanks to a built-in tracking system. Centralized feedback in a Google Sheet — no more digging through platforms. Data you can act on, like triggering Slack alerts for poor scores. ⚙️ How it works 📨 Part 1: Auto-send the survey when a ticket is resolved Trigger: Workflow runs on a schedule (or manually via “Test”). Pull ticket status from Freshdesk. Compare ticket status to the last known status in Google Sheets. Detect resolution: If status = “Resolved” (ID 4), move forward. Update the Google Sheet to track that the survey was sent. Fetch the customer’s email from Freshdesk. Create & send the survey email, personalized with ticket info and your brand. Convert Markdown → HTML for a well-formatted email. 📥 Part 2: Collect responses and store in Sheets Form Trigger: Customer clicks the survey link and fills in the form. Capture responses (e.g. rating + comments). Log feedback in a second Google Sheet for analysis. You can extend this by adding escalation steps (e.g. flagging 1–2 star ratings to managers). 🚀 Setup Instructions 🔐 Connect your tools Freshdesk**: Add your API credentials to the get tickets and get client nodes. Google Sheets**: Authenticate in the get existing tickets, update status, and save survey nodes. Email (SMTP)**: Add your SMTP details in the “Send Email” node, or swap in Gmail, SendGrid, etc. 🛠 Set your data In the Set your data node, enter: Your name, email, company, and position Your survey form link (see below) 🔗 Get the form link Activate the workflow (toggle it ON) Go to the “Survey” (Form Trigger) node Copy the Production URL Paste it into the survey link field in the Set your data node 🧾 Prepare your Google Sheets Sheet 1: Freshdesk Tickets (status tracking) Used by: get existing tickets update status Create a new empty Google Sheet. Add the Spreadsheet ID + Sheet Name into the nodes. Sheet 2: Feedback freshdesk (survey responses) Used by: save survey to google sheet Create a new sheet or tab. It will auto-create columns based on your survey form field labels. Add the Spreadsheet ID + Sheet Name/GID to the save node. 🔧 Customize the workflow 📝 Survey Questions Modify them in the Survey (Form Trigger) node. Adjust the save survey to google sheet node as needed (or use auto-map). 💬 Email Content Edit the subject and message in the Create the email text (Set) node. 🏷 Freshdesk Status ID If your “Resolved” status ID isn’t 4, update the second condition in the If ticket resolved node. 📉 Escalate poor feedback Add logic after the save survey to google sheet node: If rating is low: Notify Slack Create a new internal ticket Email a team lead 🔁 Schedule Trigger Adjust the Schedule Trigger node to your desired interval (e.g., hourly). 🔄 Use a Webhook Instead (Optional) If Freshdesk supports ticket webhook events, swap the schedule trigger for a Webhook Trigger node to send surveys instantly on ticket resolution. 🤖 Why Pollup AI is building this At Pollup AI, we help CS and support teams stop drowning in tools and manual tasks. This template is part of our growing AI agent library: plug-and-play automations that connect your tools, clean your data, and free up your time – without writing a line of code. Try this workflow and let Pollup AI handle the boring parts, so your team can focus on what customers are really saying. Learn more at Pollup AI

beginner

3 nodes602 views

Create Marketing Content with Voice Commands using GPT-4 & AI Tools in Telegram

by Muhammad Ashar

How It Works – Your AI Marketing Team in Action This automation acts as your AI-powered content and image marketing assistant inside Telegram. With just a voice note or text message, it can: 🧠 Understand your request – Whether you send a message or speak into Telegram, it transcribes and processes your input using GPT-4. 🎨 Create and edit content – Based on what you say, it can generate: ✍️ Blog posts 💼 LinkedIn posts 🎬 Faceless videos 🖼️ AI-generated images 🪄 Edits to existing images 🔎 Searches through your image database 💬 Replies directly in Telegram – It sends you back the result—whether that’s a post, image, or video link—without leaving the app. 🧩 Built using LangChain agent logic – It intelligently chooses the right tool from a suite of sub-workflows like "Create Image", "Blog Post", or "Video" using agent reasoning. 🛠️ Setup Steps – Get Started in Minutes! ⌛ Time Estimate: ~15–30 minutes (faster if you're familiar with n8n) 🔗 1. Import the Template Pack 📥 Download and install these workflows into your n8n: Create Image, Edit Image, Search Images Blog Post, LinkedIn Post, Video 🔐 2. Add Required Credentials Telegram Bot 🤖 OpenRouter AI 🧠 Tavily API (for smart research) 📚 ElevenLabs 🎙️ (for voice in videos) PiAPI & Runway 🎞️ (for faceless videos) 🧩 3. Link the Tools to the Agent Node – Make sure the "Marketing Team Agent" is connected to each of the content creation tools as shown in the workflow. 📎 4. Download Templates & Logs 🧾 Google Sheets Log Template (to track output) 🖼️ Creatomate Template (optional for enhanced image control – shared in Skool group) 📌 Pro Tip: All detailed step-by-step setup instructions are included as sticky notes inside the n8n canvas. Just follow along!

🤖AI & Machine Learning