Workflow Templates

Who this is for? Extract Amazon Best Seller Electronic Info is an automated workflow that extracts best seller data from Amazon's Electronics section using Bright Data Web Unlocker, transform it into structured JSON using Google Gemini's LLM, and forwards a fully structured JSON response to a specified webhook for downstream use. This workflow is tailored for: eCommerce Analysts** Who need to monitor Amazon best-seller trends in the Electronics category and track changes in real-time or on a schedule. Product Intelligence Teams** Who want structured insights on competitor offerings, including rankings, prices, ratings, and promotions. AI-powered Chatbot Developers** Who are building assistants capable of answering product-related queries with fresh, structured data from Amazon. Growth Hackers & Marketers** Looking to automate competitive research and surface trending product data to inform pricing strategies. Data Aggregators and Price Trackers** Who need reliable and smart scraping of Amazon data enriched with AI-driven parsing. What problem is this workflow solving? Keeping up with Amazon's best sellers in Electronics is a time-consuming, error-prone task when done manually.This workflow automates the process, ensuring:​ Automating Data Extraction from Amazon Best Sellers using Bright Data, ensuring reliable access to real-time, structured data. Enhancing Raw Data with Google Gemini, turning product lists into structured JSON using the Google Gemini LLM. Sending Results to a Webhook, enabling seamless integration into dashboards, databases, or chatbots. What this workflow does The workflow performs the following steps:​ Extracts Amazon Best Seller Electronics page info using Bright Data's Web Unlocker API. Processes the unstructured content using Google Gemini's Flash Exp model to extract structured product data. Sends the structured information to a webhook endpoint. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Amazon URL with the Bright Data zone by navigating to the Amazon URL with the Bright Data Zone node. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you're a market researcher, e-commerce entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: Change the Amazon Category** Update the Amazon URL with the topic of your interest such as Computers & Accessories, Home Audio, etc. Customize the Gemini Prompt** Update the Gemini prompt to get different styles of output — comparison tables, summaries, feature highlights, etc. Send Output to Other Destinations** Replace the Webhook URL to forward output to: Google Sheets Airtable Slack or Discord Custom API endpoints

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Description This workflow automates document processing and structured table extraction using the Nanonets API. You can submit a PDF file via an n8n form trigger or webhook—the workflow then forwards the document to Nanonets, waits for asynchronous parsing to finish, retrieves the results (including header fields and line items/tables), and returns the output as an Excel file. Ideal for automating invoice, receipt, or order data extraction with downstream business use. How It Works A document is uploaded (via n8n form or webhook). The PDF is sent to the Nanonets Workflow API for parsing. The workflow waits until processing is complete. Parsed results are fetched. Both top-level fields and any table rows/line items are extracted and restructured. Data is exported to Excel format and delivered to the requester. Setup Steps Nanonets Account: Register for a Nanonets account and set up a workflow for your specific document type (e.g., invoice, receipt). Credentials in n8n: Add HTTP Basic Auth credentials in n8n for the Nanonets API (never store credentials directly in node parameters). Webhook/Form Configuration: Option 1: Configure and enable the included n8n Form Trigger node for document uploads. Option 2: Use the included Webhook node to accept external POSTs with a PDF file. Adjust Workflow: Update any HTTP nodes to use your credential profile. Insert your Nanonets Workflow ID in all relevant nodes. Test the Workflow: Enable the workflow and try with a sample document. Features Accepts documents via n8n Form Trigger or direct webhook POST. Securely sends files to Nanonets for document parsing (credentials stored in n8n credentials manager). Automatically waits for async processing, checking Nanonets until results are ready. Extracts both header data and all table/line items into a tabular format. Exports results as an Excel file download. Modular nodes allow easy customization or extension. Prerequisites Nanonets account** with workflow configured for your document type. n8n** instance with HTTP Request, Webhook/Form, Code, and Excel/Spreadsheet nodes enabled. Valid HTTP Basic Auth credentials** saved in n8n for API access. Example Use Cases | Scenario | Benefit | |-----------------------|--------------------------------------------------| | Invoice Processing | Automated extraction of line items and totals | | Receipt Digitization | Parse amounts and charges for expense reports | | Purchase Orders | Convert scanned POs into structured Excel sheets | Notes You must set up credentials in the n8n credentials manager—do not store API keys directly in nodes. All configuration and endpoints are clearly explained with inline sticky notes in the workflow editor. Easily adaptable for other document types or similar APIs—just modify endpoints and result mapping.

Scoring LinkedIn Profiles Against Your ICP Use Case This automation scores individual LinkedIn profiles against your Ideal Customer Profile (ICP) based on interest in AI, technical depth, and seniority level. It's ideal for prioritizing leads and understanding how well a person fits your ICP criteria. What This Automation Does Given a LinkedIn profile and an Airtop profile, it: Extracts relevant data from the person's profile Determines levels of AI interest, seniority, and technical depth Calculates an ICP score based on weighted criteria Returns the full enriched profile with the score Input parameters: LinkedIn Profile URL** (e.g., https://linkedin.com/in/janedoe) Airtop Profile** connected to LinkedIn ICP scoring method** in the Airtop node prompt Output fields in JSON format: Full name, job title, employer, company LinkedIn URL, location, number of connections and followers, about section content and more Calculated ICP Score (out of 100) How It Works Form Trigger or Workflow Trigger: Accepts input from either a form or another workflow. Parameter Assignment: Ensures proper variable names for downstream nodes. Airtop Enrichment Tool: Extracts and scores the person based on a detailed prompt. Scoring: Uses this point system: AI Interest: beginner (5), intermediate (10), advanced (25), expert (35) Technical Depth: basic (5), intermediate (15), advanced (25), expert (35) Seniority Level: junior (5), mid-level (15), senior (25), executive (30) Output Formatting: Cleans and returns the result as JSON. Setup Requirements IMPORTANT: Enter your ICP scoring method in the prompt field of the Airtop node Airtop Profile connected to LinkedIn. Airtop API credentials configured in n8n. Optional: a front-end form to collect profile URLs and trigger the automation. Next Steps Embed in CRM**: Trigger this automation on new leads to auto-score them. Batch Process Leads**: Run it over a list of profile URLs for segmentation. Customize Scoring**: Adjust point weights based on your sales priorities. Read more about Scoring LinkedIn Profiles Against Your ICP

How it works This workflow demonstrates a fundamental pattern for securing a webhook by requiring an API key. It acts as a gatekeeper, checking for a valid key in the request header before allowing the request to proceed. Incoming Request: The Secured Webhook node receives an incoming POST request. It expects an API key to be sent in the x-api-key header. API Key Verification: The Check API Key node takes the key from the incoming request's header. It then makes an internal HTTP request to a second webhook (Get API Key) which acts as a mock database. This second webhook retrieves a list of registered API keys (from the Registered API Keys node) and filters it to find a match for the key that was provided. Conditional Response: If a match is found, the API Key Identified node routes the execution to the "success" path, returning a 200 OK response with the identified user's ID. If no match is found, it routes to the "unauthorized" path, returning a 401 Unauthorized error. This pattern separates the public-facing endpoint from the data source, which is a good security practice. Set up steps Setup time: ~2 minutes This workflow is designed to be a self-contained example. Set up Credentials: This workflow uses "Header Auth" for its internal communication. Go to Credentials and create a new Header Auth credential. You can use any name and value (e.g., Name: X-N8N-Auth, Value: my-secret-password). Select this credential in all four webhook/HTTP Request nodes. Add Your API Keys: Open the Registered API Keys node. This is your mock database. Edit the array to include the user_id and api_key pairs you want to authorize. Activate the workflow. Test it: Use the Test Secure Webhook node to send a request. Try it with a valid key from your list to see the success response. Change the x-api-key header to an invalid key to see the 401 Unauthorized error. For Production: Replace the mock database part of this workflow (the Get API Key webhook and Registered API Keys node) with a real database node like Supabase, Postgres, or Baserow to look up keys.

AI Lead Capture System - Complete Setup Guide Prerequisites n8n instance (cloud or self-hosted) Google AI Studio account (free tier available) Google account for Sheets integration Website with chat widget capability Phase 1: Core Infrastructure Setup Step 1: Set Up Google AI Studio Go to Google AI Studio Create account or sign in with Google Navigate to "Get API Key" Create new API key for your project Copy and securely store the API key Free tier limits: 15 requests/minute, 1 million tokens/month Step 2: Configure Google Sheets Create new Google Sheet for lead storage Add column headers (exact names): Full Name Company Name Email Address Phone Number Project Intent/Needs Project Timeline Budget Range Preferred Communication Channel How they heard about DAEX AI Copy the Google Sheet ID from URL (between /d/ and /edit) Ensure sheet is accessible to your Google account Step 3: Import n8n Workflow Open your n8n instance Create new workflow Click "..." menu → Import from JSON Paste the provided workflow JSON Workflow will appear with all nodes connected Phase 2: Credential Configuration Step 4: Set Up Google Gemini API In n8n, go to Credentials → Add Credential Search for "Google PaLM API" Enter your API key from Step 1 Test connection Link to the "Google Gemini Chat Model" node Step 5: Configure Google Sheets Access Go to Credentials → Add Credential Select "Google Sheets OAuth2 API" Follow OAuth flow to authorize your Google account Test connection with your sheet Link to the "Google Sheets" node Phase 3: Workflow Customization Step 6: Update Company Information Open the AI Agent node In the system message, replace all mentions of: Company name and description Service offerings and specializations FAQ knowledge base Typical project timelines and pricing ranges Adjust conversation tone to match your brand voice Step 7: Configure Lead Qualification Fields In the AI Agent system message, modify the required information list: Add/remove qualification questions Adjust budget ranges for your services Customize timeline options Update communication channel preferences In Google Sheets node, update column mappings if you changed fields Step 8: Set Up Sheet Integration Open Google Sheets node Click on Document ID dropdown Select your lead capture sheet Verify all column mappings match your sheet headers Test with sample data Phase 4: Website Integration Step 9: Get Webhook URL Open Webhook node in n8n Copy the webhook URL (starts with your n8n domain) Note: URL format is https://your-n8n-domain.com/webhook/[unique-id] Step 10: Connect Your Chat Widget Choose your integration method: Option A: Direct JavaScript Integration javascript// Add to your website function sendMessage(message, sessionId) { fetch('YOUR_WEBHOOK_URL', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ message: message, sessionId: sessionId || 'visitor-' + Date.now() }) }) .then(response => response.json()) .then(data => { // Display AI response in your chat widget displayMessage(data.message); }); } Option B: Chat Platform Webhook Open your chat platform settings (Intercom, Crisp, etc.) Find webhook/integration section Add webhook URL pointing to your n8n endpoint Configure to send message and session data Option C: Zapier/Make.com Integration Create new Zap/Scenario Trigger: New chat message from your platform Action: HTTP POST to your n8n webhook Map message content and session ID Phase 5: Testing & Optimization Step 11: Test Complete Flow Send test message through your chat widget Verify AI responds appropriately Check conversation context is maintained Confirm lead data appears in Google Sheets Test with various conversation scenarios Step 12: Monitor Performance Check n8n execution logs for errors Monitor Google Sheets for data quality Review conversation logs for improvement opportunities Track response times and conversion rates Step 13: Fine-Tune Conversations Analyze real conversation logs Update system prompts based on common questions Add new FAQ knowledge to the AI agent Adjust qualification questions based on lead quality Optimize for your specific customer patterns Phase 6: Advanced Features (Optional) Step 14: Add Lead Scoring Create new column in Google Sheets for "Lead Score" Update AI agent to calculate scores based on: Budget range (higher budget = higher score) Timeline urgency (sooner = higher score) Project complexity (complex = higher score) Add conditional formatting in Google Sheets to highlight high-value leads Step 15: Set Up Notifications Add email notification node after Google Sheets Configure to send alerts for high-priority leads Include lead details and conversation summary Set up different notification rules for different lead scores Step 16: Analytics Dashboard Connect Google Sheets to Google Data Studio or similar Create dashboard showing: Daily lead volume Conversion rates by source Average qualification time Lead quality scores Revenue pipeline from captured leads Troubleshooting Common Issues AI Not Responding Check Google Gemini API key validity Verify API quota not exceeded Review n8n execution logs for errors Data Not Saving to Sheets Confirm Google Sheets permissions Check column name matching Verify sheet ID is correct Chat Widget Not Connecting Test webhook URL directly with curl/Postman Verify JSON format matches expected structure Check CORS settings if browser-based integration Conversation Context Lost Ensure sessionId is unique per visitor Check memory node configuration Verify sessionId is passed consistently

Extracting LinkedIn Profile Information Use Case Manually copying data from LinkedIn profiles is time-consuming and error-prone. This automation helps you extract structured, detailed information from any public LinkedIn profile—enabling fast enrichment, hiring research, or lead scoring. What This Automation Does This automation extracts profile details from a LinkedIn URL using the following input parameters: airtop_profile**: The name of your Airtop Profile connected to LinkedIn. linkedin_url**: The URL of the LinkedIn profile you want to extract data from. How It Works Starts with a form trigger or via another workflow. Assigns the LinkedIn URL and Airtop profile variables. Opens the LinkedIn profile in a real browser session using Airtop. Uses an AI prompt to extract structured information, including: Name, headline, location Current company and position About section, experience, and education history Skills, certifications, languages, connections, and recommendations Returns structured JSON ready for further use or storage. Setup Requirements Airtop API Key — free to generate. An Airtop Profile connected to LinkedIn (requires one-time login). Next Steps Sync with CRM**: Push extracted data into HubSpot, Salesforce, or Airtable for lead enrichment. Combine with Search Automation**: Use with a LinkedIn search scraper to process profiles in bulk. Adapt to Other Platforms**: Customize the prompt to extract structured data from GitHub, Twitter, or company sites. Read more about the Extract Linkedin Profile Information automation.

Automate Customer Feedback Analysis with Google Sheets, WhatsApp, and Email Introduction: Drowning in Data, Starving for Insight? Imagine this: Your team launches a new feature. Feedback starts pouring in emails, support tickets, social media mentions, and survey responses. You know gold is buried in there, but manually reading, tagging, and summarising hundreds, maybe thousands, of comments? It takes days, maybe weeks. By the time you have a clear picture, the moment might have passed. Sounds exhausting, right? What if you could have an AI assistant tirelessly working 24/7, instantly analysing every piece of feedback the moment it arrives? This isn't science fiction anymore. AI-powered automation can transform this slow, manual chore into a real-time insight engine, giving you the pulse of your customer base almost instantly. Let's explore how. What's the Goal? Understanding the Workflow Objective The core challenge is transforming raw, unstructured customer feedback into actionable intelligence quickly and efficiently. The Problem: Manual Overload: Sifting through vast amounts of feedback manually is incredibly time-consuming and prone to human error or bias. Delayed Insights: The lag between receiving feedback and understanding it means missed opportunities and slow responses to critical issues. Inconsistent Analysis: Different team members might interpret or categorize feedback differently, leading to unreliable trend spotting. The AI Solution: Automated Data Collection: Connects directly to feedback sources (surveys, social media, review sites, helpdesks). AI-Powered Analysis: Uses Large Language Models (LLMs) like GPT-4 or Claude to analyze sentiment, extract key topics, and summarize comments. Intelligent Categorization: Automatically tags feedback based on predefined or dynamically identified themes (e.g., "bug report," "feature request," "pricing issue"). Real-time Reporting: Pushes structured insights into dashboards, databases, or triggers notifications for immediate awareness. Outcome: You move from reactive problem-solving based on stale data to proactive, strategic decisions driven by a near real-time understanding of customer sentiment and needs. Why Does It Matter? Achieving 100X Productivity and Efficiency Look, automating feedback isn't just about saving time; it's about scaling your ability to listen and respond smarter, not harder. When you leverage AI, the gains aren't incremental - they're exponential. Here’s why this is a game changer: Blazing Speed: Analyse feedback 100x Faster (or more!) than manual methods. Insights appear in minutes or hours, not days or weeks. Unhuman Scalability: Process virtually unlimited volumes of feedback without needing to scale your human team proportionally. AI doesn't get tired or bored. Consistent Accuracy: AI applies analysis rules consistently, reducing human bias and ensuring reliable categorisation and sentiment scoring over time. Proactive Trend Spotting: Identify emerging issues or popular requests much earlier by analysing aggregated data automatically. Spot patterns humans might miss. Free Up Your Team: Let your talented team focus on acting on insights – improving products, fixing issues, engaging customers – instead of drowning in data entry. How It Works: AI Automation Step by Step Getting this set up is more straightforward than you might think, especially with tools like n8n acting as the central hub. Automated Feedback Triggering CRM/Website Event Node Trigger feedback requests after: Purchases (eCommerce) Support ticket resolution Feature usage (SaaS) Time-Based Node Schedule recurring NPS surveys Customer health check-ups Chat App Node (WhatsApp/Telegram/Messenger) Send conversational feedback prompts: "How was your recent experience with [specific interaction]?" Multi-Channel Feedback Collection Email Node (SendGrid/Mailchimp) Send personalized feedback requests Embed 1-5 rating widgets SMS Node (Twilio) Short mobile surveys: "Reply 1-5: How satisfied with your purchase?" Webhook Node Capture in-app feedback Process chatbot responses Social Media Node Monitor Twitter/X, Instagram mentions Analyze comments for unsolicited feedback AI-Powered Real-Time Analysis OpenAI/ChatGPT Node (Sentiment Analysis) Prompt: "Analyze sentiment (positive/neutral/negative) and key themes from: [customer feedback]" Output fields: Sentiment score (1-5) Urgency flag (high/medium/low) Key topics (billing, support, product, etc.) Translation Node (Optional) Convert multilingual feedback into a consistent language Instant AI Response System Conditional Node (Routing Logic) Positive feedback → Send thank-you + referral ask Neutral feedback → Follow-up question for details Negative feedback → Escalate to the human team AI Response Generator Node Prompt: "Create a personalized response to [feedback type] about [topic] with sentiment [score]" Adjust tone (professional/friendly/empathetic) Escalation Node Route critical issues to the support team with full context Automated Insights & Alerts Dashboard Node Real-time sentiment tracking Emerging issue detection Alert Node (Slack/Teams/Email) Notify teams of negative trends: "3+ complaints about checkout flow in the past hour!" Report Node Auto-generate weekly/monthly summaries: "Top 5 customer pain points this week" Product Board Integration Auto-create feature requests Prioritize based on feedback volume Tools of the Trade: AI & Automation Tech Stack You don't need a massive, complex tech stack. Focus on a few core, powerful tools: n8n: The workflow automation platform. This is the 'glue' that connects everything and orchestrates the process without needing deep coding knowledge. Honestly, it's incredibly versatile. OpenAI (GPT-4/GPT-4o): State-of-the-art LLM for high-quality text analysis, summarization, and classification. Great for complex understanding. Anthropic (Claude 3 Sonnet/Opus): Another top-tier LLM, known for strong performance in analysis and handling large contexts. Often, a great alternative or complement to GPT models. Feedback Sources APIs: Connectors for where your feedback lives (e.g., Typeform, SurveyMonkey, Twitter API, Zendesk API, Google Play/App Store review APIs). Data Storage/Destination: Where the processed insights go (e.g., Google Sheets, Airtable, Notion, PostgreSQL database, BigQuery). (Optional) Visualization Tool: Tools like Metabase, Grafana, Looker Studio, or Power BI to create dashboards from your structured feedback data. What's the Cost? Estimated Budget Let's talk investment. You're mainly looking at: Setup Costs: Primarily your time (or a consultant's) to design and build the initial workflow in n8n. Depending on complexity, this could range from a few hours to a few days. No major software licenses are usually needed upfront if using self-hosted n8n or starting with free/low-tier cloud plans. AI API Calls: You pay per usage to OpenAI/Anthropic. Costs depend heavily on volume but can start from $20-$50/month for moderate usage and scale up. Newer models are getting more cost-effective. n8n Hosting: Free if self-hosted (requires a server), or tiered cloud pricing starting around $20/month. Feedback Source APIs: Some platforms might have API access costs or rate limits on free tiers. Total Estimated Monthly Cost: For many businesses, ongoing costs can range from $50 - $500+ per month, highly dependent on feedback volume and AI model choice. The Return on Investment (ROI) is typically rapid. Consider the hours saved from manual analysis, the value of faster issue resolution, preventing churn, and the benefits of making product decisions based on real-time data. It often pays for itself very quickly. Who Benefits? Target Users and Industries This automated feedback loop isn't niche; it's valuable across many sectors and roles: Top Industries: SaaS (Software as a Service): Understanding user friction, feature requests, bug reports. E-commerce & Retail: Analyzing product reviews, post-purchase surveys, and support chats. Hospitality & Travel: Processing guest reviews, survey feedback. Mobile Apps: Monitoring app store reviews, in-app feedback. Financial Services: Gauging customer satisfaction with services, identifying pain points. Key Roles: Product Managers: Prioritizing features, understanding user needs, tracking launch reception. Customer Experience (CX) / Success Managers: Monitoring customer health, identifying churn risks, and improving support processes. Marketing Teams: Understanding brand perception, campaign feedback, and voice of the customer. Support Leads: Identifying recurring issues, measuring support quality, spotting training needs. This approach works for businesses of all sizes, from startups wanting to stay lean and agile to large enterprises needing to manage massive feedback volumes. How to use workflow? Importing a workflow in n8n is a straightforward process that allows you to use pre-built or shared workflows to save time. Below is a step-by-step guide to import a workflow in n8n, based on the official documentation and community resources. Steps to Import a Workflow in n8n 1. Obtain the Workflow JSON Source the Workflow:** Workflows are typically shared as JSON files or code snippets. You might receive them from: The n8n community (e.g., n8n.io workflows page). A colleague or tutorial (e.g., a .json file or copied JSON code). Exported from another n8n instance (see export instructions below if needed). Format:** Ensure you have the workflow in JSON format, either as a file (e.g., workflow.json) or as text copied to your clipboard. 2. Access the n8n Workflow Editor Log in to n8n:** Open your n8n instance (via n8n Cloud or your - self-hosted instance). Navigate to the Workflows tab in the n8n dashboard. Open a New Workflow:** Click Add Workflow to create a blank workflow, or open an existing workflow if you want to merge the imported workflow. 3. Import the Workflow Option 1: Import via JSON Code (Clipboard): In the n8n editor, click the three dots (⋯) in the top-right corner to open the menu. Select Import from Clipboard. Paste the JSON code of the workflow into the provided text box. Click Import to load the workflow into the editor. Option 2: Import via JSON File: In the n8n editor, click the three dots (⋯) in the top-right corner. Select Import from File. Choose the .json file from your computer. Click Open to import the workflow. Note: If the workflow includes nodes for apps requiring credentials (e.g., Google Sheets), you’ll need to configure those credentials separately after importing.

Who is this for? This template is designed for content creators, marketing teams, educators, or media managers who want to repurpose video content into written blog posts with visuals. It's ideal for anyone looking to automate the process of transforming YouTube videos into professional blog articles and custom images. What problem is this workflow solving? Creating written content from video material is time-consuming and manual. This workflow solves that by automating the entire pipeline: from detecting new YouTube video uploads to transcribing the audio, turning it into an engaging blog post, generating a matching visual, and saving both in Airtable. It saves hours of work while keeping your blog or social feed active and consistent. What this workflow does This automation listens for new YouTube videos added to a Google Drive folder, extracts the full transcript using Dumpling AI, and sends it to GPT-4o to generate a blog post and image prompt. Dumpling AI then turns the prompt into a 16:9 visual. The blog and visual are saved into Airtable for easy publishing or curation. Setup Google Drive Trigger Create a folder in Google Drive and upload your YouTube videos there. Link this folder in the "Watch Folder for New YouTube Videos" node. Enable polling every minute or adjust as needed. Download & Prepare the Video The video is downloaded and converted into base64 format by the next two nodes: Download Video File and Convert Downloaded Video to Base64. Transcription with Dumpling AI The base64 video is sent to Dumpling AI’s extract-video endpoint. You must have a Dumpling AI account and an API key with access to this endpoint: Dumpling AI Docs Generate Blog Content with GPT-4o GPT-4o takes the transcript and generates: A human-like blog post A descriptive prompt for AI image generation Make sure your OpenAI credentials are configured. Generate the Visual The prompt is passed to Dumpling AI’s generate-ai-image endpoint using model FLUX.1-pro. The result is a clean 1024x576 image. Save to Airtable Blog content is stored under the Content field in Airtable. The image prompt is also added to the Attachments column as a visual reference. Ensure Airtable base and table are preconfigured with the correct field names. How to customize this workflow to your needs Change the GPT prompt to alter the tone or format of the blog post (e.g., add bullet points or SEO tags). Modify the Dumpling AI prompt to generate different image styles. Add a scheduler or webhook trigger to run at different intervals or through other integrations. Connect this output to Ghost, Notion, or your CMS using additional nodes. 🧠 Sticky Note Summary Part 1: Transcription & Blog Prompt Watches a Google Drive folder for new video uploads. Downloads and encodes the video. Transcribes full audio with Dumpling AI. GPT-4o writes a blog post and descriptive image prompt. Part 2: Image Generation & Airtable Save Dumpling AI generates a visual from the image prompt. Blog content is saved to Airtable. The image prompt is patched into the Attachments field in the same record. ✅ Use this if you want to automate repurposing YouTube videos into blog content with zero manual work.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Transform your expense tracking with automated AI receipt processing that extracts data and organizes it instantly. What this workflow does Monitors Google Drive for new receipt uploads (images/PDFs) Downloads and processes files automatically Extracts key data using VLM Run community node (merchant, amount, currency, date) Saves structured data to Google Sheets for easy tracking Setup Prerequisites: Google Drive/Sheets accounts, VLM Run API credentials, n8n instance. You need to install VLM Run community node. To install Community nodes you need to follow steps, Settings -> Community Nodes -> Install -> Search with name @vlm-run/n8n-nodes-vlmrun Quick Setup: Configure Google Drive OAuth2 and create receipt upload folder Add VLM Run API credentials Create Google Sheets with columns: Customer, Merchant, Amount, Currency, Date Update folder/sheet IDs in workflow nodes Test and activate How to customize this workflow to your needs Extend functionality by: Adding expense categories and approval workflows Connecting to accounting software (QuickBooks, Xero) Including Slack notifications for processed receipts Adding data validation and duplicate detection This workflow transforms manual receipt processing into an automated system that saves hours while improving accuracy.

Overview This workflow is designed for eCommerce store owners and marketing teams who use WooCommerce. It helps segment customers based on their purchasing behavior using the RFM (Recency, Frequency, Monetary) model. By identifying high-value customers, new buyers, and at-risk segments, you can tailor your marketing strategies and improve customer retention. How It Works Trigger: The workflow can be started manually or on a scheduled basis (e.g., weekly). Retrieve Orders: It fetches completed orders from your WooCommerce store from the past year. RFM Analysis: It groups orders by customer and calculates their RFM scores. Customer Segmentation: Based on RFM scores, customers are categorized into marketing segments (e.g., Champions, At Risk, Lost). Summary Report: Generates a styled HTML report with a table summarizing customer segments and suggested marketing actions. Setup Instructions Connect WooCommerce: Go to the WooCommerce node. Add or select your WooCommerce API credentials. You need the Base URL, Consumer Key, and Consumer Secret. Ensure API access is enabled in your WooCommerce settings. Customize Segmentation (Optional): In the "Calculate RFM Scores" code node, you can adjust the logic that assigns segment labels based on score combinations. You can also update the marketing suggestions in the second "Code" node. Run the Workflow: Use the "Manual Start" node for testing. Enable the "Weekly Trigger" node to automate execution. View Report: The final HTML node outputs a complete styled report. You can send this via email or integrate it with other services. Requirements WooCommerce store with API access enabled. Valid API credentials (Base URL, Consumer Key, Consumer Secret). n8n instance with access to the internet.

Enhance Query Resolution with the Knowledge Base Tool! Our KB Tool - Confluence KB is crafted to seamlessly integrate into the IT Ops AI SlackBot Workflow, enhancing the IT support process by enabling sophisticated search and response capabilities via Slack. Workflow Functionality: Receive Queries**: Directly accepts user queries from the main workflow, initiating a dynamic search process. AI-Powered Query Transformation**: Utilizes OpenAI's models or local ai to refine user queries into searchable keywords that are most likely to retrieve relevant information from the Knowledge Base. Confluence Integration**: Executes searches within Confluence using the refined keywords to find the most applicable articles and information. Deliver Accurate Responses**: Gathers essential details from the Confluence results, including article titles, links, and summaries, preparing them to be sent back to the parent workflow for final user response. To view a demo video of this workflow in action, click here. Quick Setup Guide: Ensure correct configurations are set for OpenAI and Confluence API integrations. Customize query transformation logic as per your specific Knowledge Base structure to improve search accuracy. Need Help? Dive into our Documentation or get support from the Community Forum! Deploy this tool to provide precise and informative responses, significantly boosting the efficiency and reliability of your IT support workflow.

Overview This workflow leverages the LangChain code node to implement a fully customizable conversational agent. Ideal for users who need granular control over their agent's prompts while reducing unnecessary token consumption from reserved tool-calling functionality (compared to n8n's built-in Conversation Agent). Setup Instructions Configure Gemini Credentials: Set up your Google Gemini API key (Get API key here if needed). Alternatively, you may use other AI provider nodes. Interaction Methods: Test directly in the workflow editor using the "Chat" button Activate the workflow and access the chat interface via the URL provided by the When Chat Message Received node Customization Options Interface Settings: Configure chat UI elements (e.g., title) in the When Chat Message Received node Prompt Engineering: Define agent personality and conversation structure in the Construct & Execute LLM Prompt node's template variable ⚠️ Template must preserve {chat_history} and {input} placeholders for proper LangChain operation Model Selection: Swap language models through the language model input field in Construct & Execute LLM Prompt Memory Control: Adjust conversation history length in the Store Conversation History node Requirements: ⚠️ This workflow uses the LangChain Code node, which only works on self-hosted n8n. (Refer to LangChain Code node docs)