Workflow Templates

Overview This workflow leverages the KIE. AI Veo3 model to generate AI videos from simple text descriptions. Users interact via a form interface, inputting a prompt (e.g., a scene description), and the system automatically submits the request to the KIE. AI API, monitors the generation status in real time, and retrieves the final video output. It's ideal for content creators, marketers, or developers exploring text-to-video AI creation, supporting intelligent video generation with minimal setup. Prerequisites A KIE. AI account and API key: Sign up at KIE.AI to obtain your free or paid API key. An active n8n instance (cloud or self-hosted) with HTTP Request and form submission capabilities. Basic knowledge of AI prompts for video generation to achieve optimal results. Setup Instructions Obtain API Key: Register at KIE. AI and generate your API key. Store it securely—do not share it publicly. Configure the Form: In the "On Form Submission" node, ensure fields like "prompt" (for video description) and "api_key" are set up. Example prompt: "A serene mountain landscape at sunset with birds flying." Test the Workflow: Click "Execute Workflow" in n8n. Access the generated form URL, submit your prompt and API key. The workflow will poll the API every 10 seconds until the video is ready, then display the results. Handle Outputs: The final node formats and displays the video file URL for download or embedding. Customization Tips Enhance Prompts**: Include specifics like duration, style (e.g., realistic, animated), actions, and visual elements to improve AI video quality. Keywords for SEO**: This template focuses on AI video generation, text-to-video models, Veo3 API integration, and automated workflows.

Overview This workflow provides Retell agent builders with a simple way to populate dynamic variables using n8n. The workflow fetches user information from a Google Sheet based on the phone number and sends it back to Retell. It is based on Retell's Inbound Webhook Call. Retell is a service that lets you create Voice Agents that handle voice calls simply, based on a prompt or using a conversational flow builder. Who is it for For builders of Retell's Voice Agents who want to make their agents more personalized. Prerequisites Have a Retell AI Account Create a Retell agent Purchase a phone number and associate it with your agent Create a Google Sheets - for example, make a copy of this one. Your Google Sheet must have at least one column with the phone number. The remaining columns will be used to populate your Retell agent’s dynamic variables. All fields are returned as strings to Retell (variables are replaced as text) How it works The webhook call is received from Retell. We filter the call using their whitelisted IP address. It extracts data from the webhook call and uses it to retrieve the user from Google Sheets. It formats the data in the response to match Retell's expected format. Retell uses this data to replace dynamic variables in the prompts. How to use it See the description for screenshots! Set the webhook name (keep it as POST). Copy the Webhook URL (e.g., https://your-instance.app.n8n.cloud/webhook/retell-dynamic-variables) and paste it into Retell's interface. Navigate to "Phone Numbers", click on the phone number, and enable "Add an inbound webhook". In your prompt (e.g., "welcome message"), use the variable with this syntax: {{variable_name}} (see Retell's documentation). These variables will be dynamically replaced by the data in your Google Sheet. Notes In Google Sheets, the phone number must start with '+. Phone numbers must be formatted like the example: with the +, extension, and no spaces. You can use any database—just replace Google Sheets with your own, making sure to keep the phone number formatting consistent. 👉 Reach out to us if you're interested in analysing your Retell Agent conversations.

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.

Automate Video Creation with Luma AI Dream Machine and Airtable (Part 2) Description This is the second part of the Luma AI Dream Machine automation. It captures the webhook response from Luma AI after video generation is complete, processes the data, and automatically updates Airtable with the video and thumbnail URLs. This completes the end-to-end automation for video creation and tracking. 👉 Airtable Base Template 👉 Tutorial Video Setup 1. Luma AI Setup Ensure you’ve created an account with Luma AI and generated an API key. Confirm that the API key has permission to manage video requests. 2. Airtable Setup Make sure your Airtable base includes the following fields (set up in Part 1): Use the Airtable Base Template linked above to simplify setup. Generation ID** – To match incoming webhook data. Status** – Workflow status (e.g., "Done"). Video URL** – Stores the generated video URL. Thumbnail URL** – Stores the thumbnail URL. 3. n8n Setup Ensure that the n8n workflow from Part 1 is set up and configured. Import this workflow and connect it to the webhook callback from Luma AI. How It Works 1. Webhook Trigger The Webhook node listens for a POST response from Luma AI once video generation is finished. The response includes: Video URL – Direct link to the video. Thumbnail URL – Link to the video thumbnail. Generation ID – Used to match the record in Airtable. 2. Process Webhook Data The Set node extracts the video data from the webhook response. The If node checks if the video URL is valid before proceeding. 3. Store in Airtable The Airtable node updates the record with: Video URL – Direct link to the video. Thumbnail URL – Link to the video thumbnail. Status – Marked as "Done." Uses the Generation ID to match and update the correct record. Why This Workflow is Useful ✅ Automates the completion step for video creation ✅ Ensures accurate record-keeping by matching generation IDs ✅ Simplifies the process of managing and organizing video content ✅ Reduces manual effort by automating the update process Next Steps Future Enhancements** – Adding more complex post-processing, video trimming, and multi-platform publishing.

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

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.

You Don’t Need More Tools. You Just Need the Right Leads. Why spend $1,000s on lead gen when your perfect leads are already waiting in Apollo? You’ve already filtered the ideal prospects. You know who they are, where they work, and what they do. Now imagine turning that list into enriched, ready-to-contact leads—without paying pricey Apollo's recurring subscription (spoiler: you will pay only 0.60$ per 500 leads). From Filter to Outreach-Ready in Seconds With the Lead Generation System, you just drop your Apollo search URL. The workflow does the rest: ✅ Scrapes all matching contacts from your Apollo filter ✅ Enriches and organizes the data (names, roles, emails, LinkedIns, companies, etc.) ✅ Delivers the final lead list to Airtable—or your CRM of choice No more manual exports. No CSV mess. No VA needed. Just qualified leads, cleaned and ready to go. Perfect For Founders doing DIY outbound Growth marketers scaling cold email Agencies running lead-gen for clients Anyone tired of paying too much for messy, outdated lists Setup Guide I built a step-by-step guide to setup this workflow in 5 to 10 minutes, available here: https://notanothermarketer.gitbook.io/home/templates/lead-generation This template is free. Enjoy!

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)

This workflow is a experiment to build HTML pages from a user input using the new Structured Output from OpenAI. How it works: Users add what they want to build as a query parameter The OpenAI node generate an interface following a structured output defined in the body The JSON output is then converted to HTML along with a title The HTML is encapsulated in an HTML node (where the Tailwind css script is added) The HTML is rendered to the user via the Webhook response. Set up steps Create an OpenAI API Key Create the OpenAI credentials Use the credentials for both nodes HTTP Request (as Predefined Credential type) and OpenAI Activate your workflow Once active, go to the production URL and add what you'd like to build as the parameter "query" Example: https://production_url.com?query=a%20signup%20form Example of generated page

How it works This workflow changes the file name, and therefore the extension and MIME type, of any binary file passed to it. This is perfect for converting file formats on the fly, like turning a Telegram voice message (.oga) into an MP3 for an AI transcription service. Set New File Name: The SET OUTPUT FILE NAME node is where you define the desired output file name and extension (e.g., audio.mp3). It also dynamically captures the property name of the incoming binary (e.g., data). Extract Binary Data: The workflow temporarily converts the binary file into a Base64 text string to make it accessible in the next step. Rebuild Binary with New Name: A Code node takes the Base64 data and reconstructs it as a binary file, but this time, it assigns the new file name you specified. n8n automatically sets the MIME type based on the new file extension. Set up steps Setup time: < 1 minute This workflow is designed to be used as a sub-workflow. In your main workflow, add an Execute Sub-Workflow node where you need to change a file's type. In the Workflow parameter, select this "Change Binary MimeType/Extension" workflow. Open this workflow and go to the SET OUTPUT FILE NAME node. Modify the output_file_name value to your desired file name (e.g., voice_message.mp3 or document.pdf). Save this workflow. Now, any binary file you send to it from your main workflow will be returned with the new fileName and mimeType.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. What this workflow does Monitors Google Drive for new driver license image uploads Downloads and processes images using VLM Run AI OCR Extracts key information including license number, name, DOB, and dates Saves structured data to Google Sheets for instant access Setup Prerequisites: Google Drive account, VLM Run API credentials, Google Sheets access, self-hosted n8n. You need to install VLM Run community node Quick Setup: Configure Google Drive OAuth2 and create license upload folder Add VLM Run API credentials Set up Google Sheets integration for data storage Update folder/sheet IDs in workflow nodes Test with sample license images and activate Perfect for Customer onboarding and identity verification KYC compliance and document processing HR employee verification and record keeping Insurance claim processing and validation Any business requiring license data extraction Key Benefits Asynchronous processing** handles high-resolution images without timeouts Multi-format support** for JPG, PNG, PDF, HEIC, WebP formats Structured data output** ready for databases and integrations Eliminates manual entry** saving hours of data input time High accuracy OCR** with multi-state license support How to customize Extend by adding: Address and additional field extraction Data validation and error checking Integration with CRM or customer databases Email notifications for processing completion Audit trails and compliance reporting Duplicate detection and data deduplication This workflow transforms manual license data entry into an automated, accurate, and compliant process, making identity verification seamless and reliable for your business operations.

Monitor Postgres Data Freshness and Email Alert If Stale This template monitors a set of tables inside a Postgres database to ensure they're getting updated. If the table hasn't been updated in 3 days (configurable), an email alert is sent containing the tables that are stale. Requirements You must have a Postgres database containing one or more tables that you'd like to monitor. Each table to monitor must have a date or timestamp column that tracks when data was pushed. For example, this might be: A timestamp column if your table holds event/timeseries data A last_updated column if your rows are expected to be modified Usage Use this template Add your Postgres and email credentials Adjust the Produce tables + date columns node to produce pairs of [table, date_column] that should be monitored for freshness 💁‍♂️ Note that a timestamp column also works (Optional) Adjust the Remove fresh tables node for your desired staleness window (default is 3 days, but you can adjust as you please) (Optional) Customize the Send alerts node to call whichever alerting workflow you please (I recommend my alerting workflow for easiest plug-and-play) How it works This template works by: Pulling the most recent row for each table Calculating how out-of-date each table is, in days Dropping fresh tables that have been updated within the past 3 days Sending an email alert with the stale tables that haven't been updated within the past 3 days