Workflow Templates

Transform chaotic training requests into strategic skill development - achieving 100% completion tracking, 30% cost reduction through intelligent planning, and data-driven L&D decisions. What This Workflow Does Revolutionizes corporate training management with AI-driven course recommendations and automated approval workflows: 📝 Training Request Capture - Jotform collects skill gaps, business justification, and training needs 💰 Budget Intelligence - Real-time department budget checking and utilization tracking 🤖 AI Course Recommendations - Matches requests to training catalog with 0-100% scoring 📊 ROI Analysis - AI assesses business impact, urgency, and return on investment ✅ Smart Approval Routing - Auto-approves within budget or routes to manager with AI insights 🎯 Skill Development Paths - Creates personalized learning journeys from current to desired levels 👥 Team Impact Assessment - Identifies knowledge sharing opportunities and additional attendees ⚠️ Risk Analysis - Evaluates delays risks and over-investment concerns 📧 Automated Notifications - Sends detailed approvals to managers and confirmations to employees 📈 Complete Tracking - Logs all requests with AI insights for L&D analytics Key Features AI Training Advisor: GPT-4 analyzes requests across 10+ dimensions including needs assessment, ROI, and implementation planning Course Catalog Matching: AI scores courses 0-100% based on skill level, topic relevance, and outcomes alignment Budget Management: Real-time tracking of department budgets with utilization percentages Preventability Scoring: Identifies skill gaps that could have been addressed earlier Alternative Options: AI suggests cost-effective alternatives (online courses, mentoring, job shadowing) Skill Development Pathways: Maps progression from current to desired skill level with timeframes Team Multiplier Effect: Identifies how training one person benefits entire team Manager Guidance: Provides key considerations, questions to ask, and approval criteria Implementation Planning: Suggests timeline, preparation needed, and post-training actions Success Metrics: Defines measurable outcomes for training effectiveness Risk Assessment: Flags delay risks and over-investment concerns Cost Optimization: Recommends ways to reduce costs while maintaining quality Perfect For Growing Tech Companies: 50-500 employees with high skill development needs Enterprise Organizations: Large corporations managing 1000+ training requests annually Professional Services: Consulting, legal, accounting firms requiring continuous upskilling Healthcare Systems: Medical organizations with compliance and clinical training requirements Manufacturing Companies: Technical skills training for operations and quality teams Sales Organizations: Sales enablement and product training at scale Financial Services: Compliance training and professional certification tracking What You'll Need Required Integrations Jotform - Training request form (free tier works) Create your form for free on Jotform using this link OpenAI API - GPT-4 for AI training analysis (~$0.30-0.60 per request) Gmail - Automated notifications to employees, managers, and HR Google Sheets - Training request database and L&D analytics Quick Start Import Template - Copy JSON and import into n8n Add OpenAI Credentials - Set up OpenAI API key (GPT-4 recommended) Create Jotform Training Request Configure Gmail - Add Gmail OAuth2 credentials Setup Google Sheets: Create spreadsheet with "Training_Requests" sheet Replace YOUR_GOOGLE_SHEET_ID in workflow Columns auto-populate on first submission Customize Training Catalog: Edit "Check Training Budget" node Update training catalog with your actual courses, providers, and costs Add your company's preferred vendors Customization Options Custom Training Catalog: Replace sample catalog with your company's actual training offerings Budget Rules: Adjust approval thresholds (e.g., auto-approve under $500) AI Prompt Tuning: Customize analysis criteria for your industry and culture Multi-Level Approvals: Add VP or director approval for high-cost training Compliance Training: Flag required certifications and regulatory training Vendor Management: Track preferred training vendors and volume discounts Learning Paths: Create role-specific career development tracks Certification Tracking: Monitor professional certifications and renewal dates Training Calendar: Integrate with company calendar for session visibility Waitlist Management: Queue employees when sessions are full Pre/Post Assessments: Add skill testing before and after training Knowledge Sharing: Schedule lunch-and-learns for employees to share learnings Expected Results 100% completion tracking - Digital trail from request to certificate 30% cost reduction - Strategic planning prevents redundant/unnecessary training 95% manager response rate - Automated reminders and clear AI guidance 50% faster approvals - AI pre-analysis speeds manager decisions 40% better course matching - AI recommendations vs manual course selection 60% reduction in budget overruns - Real-time budget visibility 3x increase in skill development velocity - Streamlined process removes friction 85% employee satisfaction - Clear process and fast responses Data-driven L&D strategy - Analytics identify trending skill gaps 25% increase in training ROI - Better targeting and follow-through Use Cases Tech Startup (150 Engineers) Engineer requests "Advanced Kubernetes" training. AI identifies skill gap as "high severity" due to upcoming cloud migration project. Checks department budget ($22K remaining of $50K), recommends $1,800 4-day course with 92% match score. Auto-routes to engineering manager with business impact analysis. Manager approves in 2 hours. Training scheduled for next month. Post-training, engineer leads internal workshop, multiplying impact across 10-person team. Migration completes 3 weeks early, saving $50K. Enterprise Sales Org (500 Reps) Sales rep requests "Negotiation Mastery" after losing 3 deals. AI assesses urgency as "justified" based on revenue impact. Recommends $1,100 2-day course but also suggests lower-cost alternative: internal mentoring from top performer ($0). Manager sees both options, chooses mentoring first. Rep closes next deal with new techniques. Training budget preserved for broader team enablement. ROI: $200K deal closed with $0 training spend. Healthcare System (2,000 Nurses) Nurse requests ACLS recertification. AI flags as "compliance-critical" with "immediate" urgency (expiring in 30 days). Checks budget, finds sufficient funds. Auto-approves and schedules next available session. Sends pre-training materials 1 week before. Tracks attendance, generates certificate upon completion. Updates nurse's credential profile in HRIS. Compliance maintained, no manual intervention needed. Financial Services Firm Analyst requests CFA Level 1 prep course ($2,500). AI assesses as "high ROI" but identifies budget constraint (department at 95% utilization). Recommends deferring to next quarter when new budget allocated. Suggests free Khan Academy courses as interim solution. Manager sees complete analysis, approves deferral, adds analyst to Q2 priority list. Transparent communication maintains morale despite delay. Manufacturing Company Maintenance tech requests PLC programming training. AI identifies 5 other techs with same skill gap. Recommends group training session ($1,200 per person vs $2,000 individual). Calculates team multiplier effect: 6 techs trained = reduced downtime across 3 shifts. Manager approves group session, saving $4,800. All 6 techs complete training together, creating peer support network. Equipment downtime reduced 40%. Pro Tips Quarterly Planning: Use Google Sheets data to identify trending skill gaps and plan group training Budget Forecasting: Track monthly utilization to predict Q4 budget needs Course Ratings: Add post-training feedback to improve AI recommendations over time Internal Experts: Build database of employees who can provide mentoring (free alternative) Learning Paths: Create role-based tracks (e.g., "Junior Dev → Senior Dev" pathway) Compliance Flagging: Auto-identify regulatory/certification requirements Vendor Relationships: Track volume with vendors to negotiate discounts Knowledge Retention: Require post-training presentations to reinforce learning Manager Training: Educate managers on how to evaluate AI recommendations Budget Reallocation: Monthly reviews to move unused budget between departments Early Bird Discounts: AI can suggest booking 60+ days out for savings Continuous Learning: Supplement formal training with Udemy/LinkedIn Learning subscriptions Learning Resources This workflow demonstrates advanced automation: AI Agents with complex analysis across multiple decision dimensions Budget management algorithms with real-time calculations Course recommendation engines with scoring and matching Multi-criteria approval routing based on AI confidence Skill progression modeling from current to desired states ROI analysis balancing cost, impact, and urgency Alternative suggestion algorithms for cost optimization Team impact modeling for knowledge multiplication Risk assessment frameworks for training decisions Real-Time Budget Tracking: Live department budget visibility prevents overspending Audit Trail: Complete history for finance audits and compliance reviews Approval Documentation: Timestamped manager approvals for governance Cost Allocation: Track training costs by department, employee, category ROI Measurement: Compare training investment to business outcomes Compliance Monitoring: Flag required certifications and regulatory training Vendor Management: Track spending with training providers Ready to transform your corporate training? Import this template and turn training chaos into strategic skill development with AI-powered insights and automation! 📚✨ Questions or customization? The workflow includes detailed sticky notes explaining each AI analysis component.

WordPress AI Content Creator Overview Transform a few keywords into professionally written, SEO-optimized WordPress blog posts with custom featured images. This workflow leverages AI to research topics, structure content, write engaging articles, and publish them directly to your WordPress site as drafts ready for review. What This Workflow Does Core Features Keyword-to-Article Generation**: Converts simple keywords into comprehensive, well-structured articles Intelligent Content Planning**: Uses AI to create logical chapter structures and content flow Wikipedia Integration**: Researches factual information to ensure content accuracy and depth Multi-Chapter Writing**: Generates coherent, contextually-aware content across multiple sections Custom Image Creation**: Generates relevant featured images using DALL-E based on article content SEO Optimization**: Creates titles, subtitles, and content optimized for search engines WordPress Integration**: Automatically publishes articles as drafts with proper formatting and featured images Business Value Content Scale**: Produce high-quality blog posts in minutes instead of hours Research Efficiency**: Automatically incorporates factual information from reliable sources Consistency**: Maintains professional tone and structure across all generated content SEO Benefits**: Creates search-engine friendly content with proper HTML formatting Cost Savings**: Reduces need for external content creation services Prerequisites Required Accounts & Credentials WordPress Site with REST API enabled OpenAI API access (GPT-4 and DALL-E models) WordPress Application Password or JWT authentication Public-facing n8n instance for form access (or n8n Cloud) Technical Requirements WordPress REST API v2 enabled (standard on most WordPress sites) WordPress user account with publishing permissions n8n instance with LangChain nodes package installed Setup Instructions Step 1: WordPress Configuration Enable REST API (usually enabled by default): Check that yoursite.com/wp-json/wp/v2/ returns JSON data If not, contact hosting provider or install REST API plugin Create Application Password: In WordPress Admin: Users > Profile Scroll to "Application Passwords" Add new password with name "n8n Integration" Copy the generated password (save securely) Get WordPress Site URL: Note your full WordPress site URL (e.g., https://yourdomain.com) Step 2: OpenAI Configuration Obtain OpenAI API Key: Visit OpenAI Platform Create API key with access to: GPT-4 models (for content generation) DALL-E (for image creation) Add OpenAI Credentials in n8n: Navigate to Settings > Credentials Add "OpenAI API" credential Enter your API key Step 3: WordPress Credentials in n8n Add WordPress API Credentials: In n8n: Settings > Credentials > "WordPress API" URL: Your WordPress site URL Username: Your WordPress username Password: Application password from Step 1 Step 4: Update Workflow Settings Configure Settings Node: Open the "Settings" node Replace wordpress_url value with your actual WordPress URL Keep other settings as default or customize as needed Update Credential References: Ensure all WordPress nodes reference your WordPress credentials Verify OpenAI nodes use your OpenAI credentials Step 5: Deploy Form (Production Use) Activate Workflow: Toggle workflow to "Active" status Note the webhook URL from Form Trigger node Test Form Access: Copy the form URL Test form submission with sample data Verify workflow execution completes successfully Configuration Details Form Customization The form accepts three key inputs: Keywords**: Comma-separated topics for article generation Number of Chapters**: 1-10 chapters for content structure Max Word Count**: Total article length control You can modify form fields by editing the "Form" trigger node: Add additional input fields (category, author, publish date) Change field types (dropdown, checkboxes, file upload) Modify validation rules and requirements AI Content Parameters Article Structure Generation The "Create post title and structure" node uses these parameters: Model**: GPT-4-1106-preview for enhanced reasoning Max Tokens**: 2048 for comprehensive structure planning JSON Output**: Structured data for subsequent processing Chapter Writing The "Create chapters text" node configuration: Model**: GPT-4-0125-preview for consistent writing quality Context Awareness**: Each chapter knows about preceding/following content Word Count Distribution**: Automatically calculates per-chapter length Coherence Checking**: Ensures smooth transitions between sections Image Generation Settings DALL-E parameters in "Generate featured image": Size**: 1792x1024 (optimized for WordPress featured images) Style**: Natural (photographic look) Quality**: HD (higher quality output) Prompt Enhancement**: Adds photography keywords for better results Usage Instructions Basic Workflow Access the Form: Navigate to the form URL provided by the Form Trigger Enter your desired keywords (e.g., "artificial intelligence, machine learning, automation") Select number of chapters (3-5 recommended for most topics) Set word count (1000-2000 words typical) Submit and Wait: Click submit to trigger the workflow Processing takes 2-5 minutes depending on article length Monitor n8n execution log for progress Review Generated Content: Check WordPress admin for new draft post Review article structure and content quality Verify featured image is properly attached Edit as needed before publishing Advanced Usage Custom Prompts Modify AI prompts to change: Writing Style**: Formal, casual, technical, conversational Target Audience**: Beginners, experts, general public Content Focus**: How-to guides, opinion pieces, news analysis SEO Strategy**: Keyword density, meta descriptions, heading structure Bulk Content Creation For multiple articles: Create separate form submissions for each topic Schedule workflow executions with different keywords Use CSV upload to process multiple keyword sets Implement queue system for high-volume processing Expected Outputs Article Structure Generated articles include: SEO-Optimized Title**: Compelling, keyword-rich headline Descriptive Subtitle**: Supporting context for the main title Introduction**: ~60 words introducing the topic Chapter Sections**: Logical flow with HTML formatting Conclusions**: ~60 words summarizing key points Featured Image**: Custom DALL-E generated visual Content Quality Features Factual Accuracy**: Wikipedia integration ensures reliable information Proper HTML Formatting**: Bold, italic, and list elements for readability Logical Flow**: Chapters build upon each other coherently SEO Elements**: Optimized for search engine visibility Professional Tone**: Consistent, engaging writing style WordPress Integration Draft Status**: Articles saved as drafts for review Featured Image**: Automatically uploaded and assigned Proper Formatting**: HTML preserved in WordPress editor Metadata**: Title and content properly structured Troubleshooting Common Issues "No Article Structure Generated" Cause: AI couldn't create valid structure from keywords Solutions: Use more specific, descriptive keywords Reduce number of chapters requested Check OpenAI API quotas and usage Verify keywords are in English (default language) "Chapter Content Missing" Cause: Individual chapter generation failed Solutions: Increase max tokens in chapter generation node Simplify chapter prompts Check for API rate limiting Verify internet connectivity for Wikipedia tool "WordPress Publication Failed" Cause: Authentication or permission issues Solutions: Verify WordPress credentials are correct Check WordPress user has publishing permissions Ensure WordPress REST API is accessible Test WordPress URL accessibility "Featured Image Not Attached" Cause: Image generation or upload failure Solutions: Check DALL-E API access and quotas Verify image upload permissions in WordPress Review image file size and format compatibility Test manual image upload to WordPress Performance Optimization Large Articles (2000+ words) Increase timeout values in HTTP request nodes Consider splitting very long articles into multiple posts Implement progress tracking for user feedback Add retry mechanisms for failed API calls High-Volume Usage Implement queue system for multiple simultaneous requests Add rate limiting to respect OpenAI API limits Consider batch processing for efficiency Monitor and optimize token usage Customization Examples Different Content Types Product Reviews Modify prompts to include: Pros and cons sections Feature comparisons Rating systems Purchase recommendations Technical Tutorials Adjust structure for: Step-by-step instructions Code examples Prerequisites sections Troubleshooting guides News Articles Configure for: Who, what, when, where, why structure Quote integration Fact checking emphasis Timeline organization Alternative Platforms Replace WordPress with Other CMS Ghost**: Use Ghost API for publishing Webflow**: Integrate with Webflow CMS Strapi**: Connect to headless CMS Medium**: Publish to Medium platform Different AI Models Claude**: Replace OpenAI with Anthropic's Claude Gemini**: Use Google's Gemini for content generation Local Models**: Integrate with self-hosted AI models Multiple Models**: Use different models for different tasks Enhanced Features SEO Optimization Add nodes for: Meta Description Generation**: AI-created descriptions Tag Suggestions**: Relevant WordPress tags Internal Linking**: Suggest related content links Schema Markup**: Add structured data Content Enhancement Include additional processing: Plagiarism Checking**: Verify content originality Readability Analysis**: Assess content accessibility Fact Verification**: Multiple source confirmation Image Optimization**: Compress and optimize images Security Considerations API Security Store all credentials securely in n8n credential system Use environment variables for sensitive configuration Regularly rotate API keys and passwords Monitor API usage for unusual activity Content Moderation Review generated content before publishing Implement content filtering for inappropriate material Consider legal implications of auto-generated content Maintain editorial oversight and fact-checking WordPress Security Use application passwords instead of main account password Limit WordPress user permissions to minimum required Keep WordPress and plugins updated Monitor for unauthorized access attempts Legal and Ethical Considerations Content Ownership Understand OpenAI's terms regarding generated content Consider copyright implications for Wikipedia-sourced information Implement proper attribution where required Review content licensing requirements Disclosure Requirements Consider disclosing AI-generated content to readers Follow platform-specific guidelines for automated content Ensure compliance with advertising and content standards Respect intellectual property rights Support and Maintenance Regular Maintenance Monitor OpenAI API usage and costs Update AI prompts based on output quality Review and update Wikipedia search strategies Optimize workflow performance based on usage patterns Quality Assurance Regularly review generated content quality Implement feedback loops for improvement Test workflow with diverse keyword sets Monitor WordPress site performance impact Updates and Improvements Stay updated with OpenAI model improvements Monitor n8n platform updates for new features Engage with community for workflow enhancements Document custom modifications for future reference Cost Optimization OpenAI Usage Monitor token consumption patterns Optimize prompts for efficiency Consider using different models for different tasks Implement usage limits and budgets Alternative Approaches Use local AI models for cost reduction Implement caching for repeated topics Batch similar requests for efficiency Consider hybrid human-AI content creation License and Attribution This workflow template is provided under MIT license. Attribution to original creator appreciated when sharing or modifying. Generated content is subject to OpenAI's usage policies and terms of service.

Generate ready-to-publish short-form videos from text prompts using AI Click on the image to see the Example output in google drive Transform simple text concepts into professional short-form videos complete with AI-generated visuals, narrator voice, background music, and dynamic text overlays - all automatically generated and ready for Instagram, TikTok, or YouTube Shorts. This workflow demonstrates a cost-effective approach to video automation by combining AI-generated images with audio composition instead of expensive AI video generation. Processing takes 1-2 minutes and outputs professional 9:16 vertical videos optimized for social platforms. The template serves as both a showcase and building block for larger automation systems, with sticky notes providing clear guidance for customization and extension. Who's it for Content creators, social media managers, and marketers who need consistent, high-quality video content without manual production work. Perfect for motivational content, storytelling videos, educational snippets, and brand campaigns. How it works The workflow uses a form trigger to collect video theme, setting, and style preferences. ChatGPT generates cohesive scripts and image prompts, while Google Gemini creates themed background images and OpenAI TTS produces narrator audio. Background music is sourced from Openverse for CC-licensed tracks. All assets are uploaded to JsonCut API which composes the final video with synchronized overlays, transitions, and professional audio mixing. Results are stored in NocoDB for management. How to set up JsonCut API: Sign up at jsoncut.com and create an API key at app.jsoncut.com. Configure HTTP Header Auth credential in n8n with header name x-api-key OpenAI API: Set up credentials for script generation and text-to-speech Google Gemini API: Configure access for Imagen 4.0 image generation NocoDB (Optional): Set up instance for video storage and configure database credentials Requirements JsonCut free account with API key OpenAI API access for GPT and TTS Google Gemini API for image generation NocoDB (optional) for result storage How to customize the workflow This template is designed as a foundation for larger automation systems. The modular structure allows easy modification of AI prompts for different content niches (business, wellness, education), replacement of the form trigger with RSS feeds or database triggers for automated content generation, integration with social media APIs for direct publishing, and customization of visual branding through JsonCut configuration. The workflow can be extended for bulk processing, A/B testing multiple variations, or integration with existing content management systems. Sticky notes throughout the workflow provide detailed guidance for common customizations and scaling options.

Overview Get a weekly report on website traffic driven by large language models (LLMs) such as ChatGPT, Perplexity, and Gemini. This workflow helps you track how these tools bring visitors to your site. A weekly snapshot can guide better content and marketing decisions. How it works The trigger runs every Monday. Pull the number of sessions on your website by source/medium from Google Analytics. The code node uses the following regex to filter referral traffic from AI providers like ChatGPT, Perplexity, and Gemini: /^.openai.|.copilot.|.chatgpt.|.gemini.|.gpt.|.neeva.|.writesonic.|.nimble.|.outrider.|.perplexity.|.google.bard.|.bard.google.|.bard.|.edgeservices.|.astastic.|.copy.ai.|.bnngpt.|.gemini.google.$/i; Combine the filtered sessions into one list so they can be processed by an LLM. Generate a short report using the filtered data. Email the report to yourself. Setup Get or connect your OpenAI API key and set up your OpenAI credentials in n8n. Enable Google Analytics and Gmail API access in the Google Cloud Console. (Read more here). Set up your Google Analytics and Gmail credentials in n8n. If you're using the cloud version of n8n, you can log in with your Google account to connect them easily. In the Google Analytics node, add your credentials and select the property for the website you’re working with. Alternatively, you can use your property ID, which can be found in the Google Analytics admin panel under Property > Property Details. The property ID is shown in the top-right corner. Add this to the property field. Under Metrics, select the metric you want to measure. This workflow is configured to use sessions, but you can choose others. Leave the dimension as-is, since we need the source/medium dimension to filter LLMs. (Optional) To expand the list of LLMs being filtered, adjust the regex in the code node. You can do this by copying and pasting one of the existing patterns and modifying it. Example: |.example.| The LLM node creates a basic report. If you’d like a more detailed version, adjust the system prompt to specify the details or formatting you want. Add your email address to the Gmail node so the report is delivered to your inbox. Requirements OpenAI API key for report generation Google Analytics API enabled in Google Cloud Console Gmail API enabled in Google Cloud Console Customizing this workflow The regex used to filter LLM referral traffic can be expanded to include specific websites. The system prompt in the AI node can be customized to create a more detailed or styled report.

How it works Automatically generates professional PDF invoices from webhook data and delivers them via email while storing backups in Google Drive. Perfect for freelancers, small businesses, and service providers who need automated billing workflows. Set up steps Configure environment variables for company information (name, address, email, phone). Set up your PDF generation API service account (PDFShift recommended). Configure SMTP email credentials for invoice delivery. Set up Google Drive OAuth2 for cloud storage. Deploy the workflow and test with sample invoice data. Key features Smart invoice number generation if not provided Automatic tax calculations with configurable rates Professional HTML templates with company branding Parallel processing for email and storage Comprehensive error handling and validation Detailed success confirmation responses Required environment variables COMPANY_NAME - Your business name COMPANY_ADDRESS - Business mailing address COMPANY_EMAIL - Billing contact email COMPANY_PHONE - Business phone number PDF_API_URL - PDF generation service endpoint PDF_API_KEY - API authentication key GDRIVE_INVOICE_FOLDER_ID - Google Drive folder ID API requirements PDF generation service (PDFShift, HTML/CSS to PDF API, or similar), SMTP email service for delivery, Google Drive API access for storage. Input format { "customerName": "John Smith", "customerEmail": "john@example.com", "items": [ {"description": "Web Design", "quantity": 1, "price": 500} ], "dueDate": "2025-02-15" }

Weekly Google Analytics 4 Report - Full WoW Tracking & Auto-Generated with Gemini AI Stop manually building weekly analytics reports. This workflow automatically fetches your GA4 data every Monday morning, generates an AI-written executive summary using Gemini, builds a premium formatted HTML email with deep Week-over-Week (WoW) comparisons for every metric, and delivers it straight to your stakeholders' inboxes — fully hands-free. 👤 Who is this for? Marketing teams* and *agency owners** who report GA4 metrics weekly to clients Product managers* and *founders** who want a Monday morning performance digest Freelancers** managing analytics for multiple clients who want to automate reporting Anyone who spends 30–60 minutes every week manually pulling GA4 numbers and calculating WoW changes 🚩 What problem does this solve? Manual GA4 reporting is repetitive, error-prone, and time-consuming. This template eliminates that entirely — every Monday at 8:00 AM in your configured timezone, a fully formatted report lands in your inbox. With the new dual-node architecture, it automatically calculates accurate WoW trends for your overall metrics, specific pages, traffic sources, and more, all contextualized by a Gemini-generated executive summary. ⚙️ What this workflow does ⏰ Triggers every Monday at 8:00 AM via the Schedule Trigger node 📡 Fetches 14 GA4 reports in parallel - pulling both Current Week and Previous Week data simultaneously for speed: Overview metrics Top 5 Screens / Pages by views Top 5 Traffic Sources / Referrals Top 5 Events by count Top 5 Countries by sessions Device breakdown (mobile / desktop / tablet) New vs Returning users 🔀 Merges all 14 responses and passes the complete historical dataset forward 🤖 Gemini writes a 3-5 bullet point summary analyzing the full WoW dataset to highlight performance trends, audience behaviour, and actionable recommendations 🧮 Code node processes all data - aligns current vs. previous week data, calculates WoW % changes for every single category, handles new/dropped entries, and builds the full inline-CSS HTML email 📧 Sends the report via standard SMTP / Email node to your configured recipients 📧 What the email report includes Header** - dark luxury card with 4 KPI tiles (Users, Sessions, Bounce Rate, Avg Duration) and WoW arrows AI Executive Summary** - 3-5 bullet point Gemini-generated insight (hidden automatically if Gemini fails) Overview Table** - all 5 core metrics with This Week / Last Week / WoW % change pill badges Audience** - New vs Returning users with visual progress bars and WoW changes Top Screens** - ranked by views, including previous week values and WoW trend pills. (not set) and (empty) rows are preserved for transparency Traffic Sources** - top referral channels with WoW changes; direct traffic auto-labelled as Direct / App Open Top Events** - tracks interaction trends with WoW changes; system events (first_open, os_update, etc.) are filtered out automatically Geography** - top 5 countries by sessions with WoW comparisons Devices** - mobile / desktop / tablet with visual progress bars and WoW trend pills 🛠️ Setup Instructions Step 1 - Google Analytics 4 Credential Go to n8n Credentials → Add new → search Google Analytics OAuth2 Sign in with the Google account that has access to your GA4 property Assign this credential to all 14 GA4 nodes Step 2 - Set your GA4 Property ID Open each of the 14 GA4 nodes In the Property ID field, replace {YOUR_PROPERTY_ID} with your GA4 numeric property ID Find your Property ID at: GA4 Admin → Property Settings → Property ID (looks like 123456789) Step 3 - Gemini API Credential Get your free API key at aistudio.google.com Go to n8n Credentials → Add new → search Google Gemini Paste your API key and assign it to the Generate AI Summary node Step 4 - Email / SMTP Credential Go to n8n Credentials → Add new → search SMTP (or swap the node for Gmail OAuth2 if preferred) Enter your email host, port, and login credentials Assign to the Send Weekly Report node Step 5 - Set Recipients Open the Send Weekly Report node (or the Code node depending on your mapping preference) Update the To Email field with your recipient address(es): 'email@example.com, email2@example.com' Alternatively, update the recipients: line at the bottom of the Build Report & Email HTML Code node. Step 6 - Set Timezone Open the Weekly Monday Trigger node Update the workflow settings to match your local timezone so the 8:00 AM trigger fires correctly. 🔧 How to Customise Change the schedule** → Open the trigger node, adjust the day and time to any cadence you need (daily, bi-weekly, monthly) Change the client brand in the footer** → Search for AppStoneLab Technologies in the Code node and replace it with your client or company name Filter or change events** → In the Code node, find the EXCLUDE_EVENTS array and add/remove event names to control which events appear in the report Change the AI summary language or tone** → Edit the prompt inside the Generate AI Summary Gemini node to match your client's preferred reporting style 📦 Requirements | Service | Purpose | Free Tier Available | | --- | --- | --- | | Google Analytics 4 | Source of all report data | ✅ Yes | | Google Gemini API | AI executive summary generation | ✅ Yes (via AI Studio) | | SMTP / Email | Email delivery | ✅ Yes | ⚠️ Notes Mobile app properties** - If your GA4 property tracks a mobile app, the workflow uses unifiedScreenName instead of pagePath, which works correctly for both web and app properties WoW calculations** - Bounce rate change is intentionally inverted (a decrease is shown as positive/green). If a metric like a specific page or country is new this week, the workflow dynamically tags it as "New" instead of breaking the calculation. Gemini failure handling** - If the Gemini node fails for any reason, the AI summary section is automatically hidden and the rest of the report sends normally Execute Once** - All 14 GA4 nodes have Execute Once enabled to prevent duplicate rows from the merge operation

This workflow automatically notifies travelers about their pending trip payments and provides secure payment links through Email and WhatsApp. It runs twice daily (at 7 AM and 7 PM) to ensure timely reminders before the due date. Designed for travel agencies, it simplifies payment tracking, reduces manual follow-up, and ensures every traveler receives personalized reminders with real-time payment status updates. 🔧 Main Components Daily Payment Check – 7 AM & 7 PM Scheduled triggers that start the workflow daily at 7 AM and 7 PM. Read Pending Travel Payment Fetches traveler payment records from an Excel sheet (using getAll method). Process Payment Reminders Filters records to find pending payments due within the next 3 days. Create Payment Reminders Generates personalized payment reminders. Make Reminder For Email Prepares email-friendly messages with payment links. Send Email Reminder Sends the payment reminder email with a secure payment link to the traveler. Prepare WhatsApp Reminder Generates WhatsApp-friendly messages with payment and payment details. Send WhatsApp Message Sends the message to the traveler’s WhatsApp number using a message API. Update Status Of Reminder Updates the Excel file to mark reminders as sent to avoid duplicates. 🧩 Channels Used 📧 Email – with personalized payment link 💬 WhatsApp – formatted reminder message 🔐 Payment Integration Secure payment links are auto-generated per traveler to enable direct and safe online payments. ✅ Essential Prerequisites Excel sheet with payment records (travel_payment_data.xlsx) SMTP credentials for sending email WhatsApp API or provider integration (like Twilio or Gupshup) Access to a payment gateway or service for link generation File storage access to update reminder status in Excel 📁 Required Excel File Structure (travel_payment_data.xlsx) | Traveler ID | Name | Email | Phone | Payment Due Date | Amount | Reminder Sent | |-------------|------------|-------------------|---------------|------------------|---------|---------------| | TR001 | Arjun Patel| arjun@example.com | +919876543210 | 2025-10-20 | ₹3000 | No | 🧾 Expected Input Format Example { "travelerId": "TR001", "name": "Arjun Patel", "email": "arjun@example.com", "phone": "+919876543210", "dueDate": "2025-10-20", "amount": "₹3000", "reminderSent": "No" } 🚀 Key Features ⏰ Scheduled Daily Execution – Fully automated at 7 AM and 7 PM 🧮 Due-Date Filtering – Only targets payments due in the next 3 days 💬 Multi-Channel Notifications – Sends reminders via both Email and WhatsApp 🔗 Secure Payment Links – Auto-generated for each traveler 🔄 Reminder Tracking – Prevents duplicate reminders by updating status ⚙️ Quick Setup Guide Import Workflow JSON into your n8n instance. Configure schedule in the “Daily Payment Check” node (default: 7 AM & 7 PM). Set Excel file path in the “Read Pending Travel Payment” node. Update your payment processing logic in the “Process Payment Reminders” node. Add email credentials in the “Send Email Reminder” node. Integrate WhatsApp provider API in the “Send WhatsApp Message” node. Define how you generate secure payment links. Test with sample data and activate workflow.

📄 What this workflow does This workflow automatically turns any uploaded video into structured blog research using AI tools. It transcribes the video, extracts keywords, runs research based on those keywords, and saves the final result to a Google Sheet. It uses Dumpling AI for transcription and research, OpenAI for keyword extraction, and Google Sheets for organizing the output. 👤 Who is this for This workflow is perfect for: Content creators who repurpose video content into blog posts SEO and marketing teams looking to extract topics and keyword insights from video materials Anyone who wants to automate video-to-text and research workflows without doing it manually ✅ Requirements Google Drive** account with a folder to watch for video uploads Dumpling AI** API access for transcription and agent research OpenAI (GPT-4o)** credentials for keyword extraction Google Sheets** document with the following column headers: Keywords topicsFromPerplexity blogPostsFromGoogle ⚙️ How to set up Connect your Google Drive and choose the folder where videos will be uploaded. Set up your Dumpling AI and OpenAI GPT-4o API credentials. Create a Google Sheet with the required columns. Replace the default folder ID and spreadsheet ID in the workflow with your own. Activate the workflow to start watching for new videos. 🔁 How it works (Workflow Steps) Watch Uploaded Videos: Triggers when a new video is added to your selected Google Drive folder. Download Video: Downloads the uploaded video file. Convert Video to Base64: Prepares the video for API submission by converting it to base64. Transcribe with Dumpling AI: Sends the video to Dumpling AI to get a full transcript. Extract Keywords with OpenAI: Analyzes the transcript and extracts five key SEO keywords. Run Competitor Research via Dumpling AI: Uses those keywords to fetch related topics and blog examples from Perplexity and Google. Format Results for Google Sheets: Formats the research results into clean text blocks. Append to Google Sheets: Saves the data into your specified Google Sheet. 🛠️ Customization Ideas Add a translation step after transcription to support multilingual content research. Modify the GPT prompt to extract summaries or titles instead of keywords. Change the Google Sheet structure to log video filenames and timestamps. Add email or Slack notifications to alert you when research is complete.

How It Works This N8N template demonstrates using Foxit's Extraction API to get information from an incoming document and then using Diffbot's APIs to turn the text into a list of organizations mentioned in the document and create a summary. How it works Listen for a new file added to a Google Drive folder. When executed, the bits are downloaded. Upload the bits to Foxit. Call the Extract API to get the text contents of the document. Poll the API to see if it's done, and when it is, grab the text. Send the text to Diffbot API to get a list of entities mentioned in the doc as well as the summary. Use a code step to filter the entities returned from Diffbot to ones that are organizations, as well as filtering to a high confidence score. Use another code step to make an HTML string from the previous data. Email it using the GMail node. Requirements A Google account for Google Drive and GMail Foxit developer account (https://developer-api.foxit.com) Diffbot developer account (https://app.diffbot.com/get-started) Next Steps This workflow assumes PDF input, but Foxit has APIs to convert Office docs to PDF and that flow could be added before the Extract API is called. Diffbot returns an incredible set of information and more could be used in the email. Instead of emailing, you could sort documents by organizations into new folders.

Who this is for This workflow is designed for teams that collect feedback or survey responses via Jotform and want to automatically: Analyze sentiment (positive, neutral, negative) of each response. Extract key topics and keywords from qualitative text. Generate AI summaries and structured insights. Store results in Google Sheets and n8n DataTables for easy reporting and analysis. Use Cases Customer experience analysis Market research & survey analysis Product feedback clustering Support ticket prioritization AI-powered blog or insight generation from feedback What this workflow does This n8n automation connects Jotform, Google Gemini, and Google Sheets to turn raw responses into structured insights with sentiment, topics, and keywords. Pipeline Overview Jotform → Webhook → Gemini (Topics + Keywords) → Gemini (Sentiment) → Output Parser → Merge → Google Sheets Jotform Trigger Captures each new submission from your Jotform (e.g., a feedback or survey form). Extracts raw fields ($json.body.pretty) such as name, email, and response text. Format Form Data (Code Node) Converts the Jotform JSON structure into a clean string for AI input. Ensures the text is readable and consistent for Gemini. Topics & Keyword Extraction (Google Gemini + Output Parser) Goal: Identify the main themes and important keywords from responses. { "topics": [ { "topic": "Product Features", "summary": "Users request more automation templates.", "keywords": ["AI templates", "automation", "workflow"], "sentiment": "positive", "importance_score": 0.87 } ], "global_keywords": ["AI automation", "developer tools"], "insights": ["Developers desire more creative, ready-to-use AI templates."], "generated_at": "2025-10-08T10:30:00Z" } Sentiment Analyzer (Google Gemini + Output Parser) Goal: Evaluate overall emotional tone and priority. { "customer_name": "Ranjan Dailata", "customer_email": "ranjancse@gmail.com", "feedback_text": "Please build more interesting AI automation templates.", "sentiment": "positive", "confidence_score": 0.92, "key_phrases": ["AI automation templates", "developer enablement"], "summary": "Customer requests more AI automation templates to boost developer productivity.", "alert_priority": "medium", "timestamp": "2025-10-08T10:30:00Z" } Merge + Aggregate Combines the topic/keyword extraction and sentiment output into a single structured dataset. Aggregates both results for unified reporting. Persist Results (Google Sheets) Writes combined output into your connected Google Sheet. Two columns recommended: feedback_analysis → Sentiment + Summary JSON topics_keywords → Extracted Topics + Keywords JSON Enables easy visualization, filtering, and reporting. Visualization (Optional) Add Sticky Notes or a logo image node in your workflow to: Visually describe sections (e.g., “Sentiment Analysis”, “Topic Extraction”). Embed brand logo: Example AI Output (Combined) { "feedback_analysis": { "customer_name": "Ranjan Dailata", "sentiment": "positive", "summary": "User appreciates current templates and suggests building more advanced AI automations.", "key_phrases": ["AI automation", "developer templates"] }, "topics_keywords": { "topics": [ { "topic": "AI Template Expansion", "keywords": ["AI automation", "workflow templates"], "sentiment": "positive", "importance_score": 0.9 } ], "global_keywords": ["automation", "AI development"] } } Setup Instructions Pre-requisite If you are new to Jotform, Please do signup using Jotform Signup For the purpose of demonstation, we are considering the Jotforms Prebuilt New Customer Registration Form as a example. However, you are free to consider for any of the form submissions. Step 0: Local n8n (Optional) If using local n8n, set up ngrok: ngrok http 5678 Use the generated public URL as your Webhook URL base for Jotform integration. Step 1: Configure the Webhook Copy the Webhook URL generated by n8n (e.g., /webhook-test/f3c34cda-d603-4923-883b-500576200322). You can copy the URL by double clicking on the Webhook node. Make sure to replace the base url with the above Step 0, if you are running the workflow from your local machine. In Jotform, go to your form → Settings → Integrations → Webhooks → paste this URL. Now, every new form submission will trigger the n8n workflow. Step 2: Connect Google Gemini Create a Google Gemini API Credential in n8n. Select the model models/gemini-2.0-flash-exp. Step 3: Create Data Storage Create a DataTable named JotformFeedbackInsights with columns: feedback_analysis (string) topics_keywords (string) Step 4: Connect Google Sheets Add credentials under Google Sheets OAuth2. Link to your feedback tracking sheet. Step 5: Test the Workflow Submit a form via Jotform. Check results: AI nodes return structured JSON. Google Sheet updates with new records. Customization Tips Change the Prompt You can modify the topic extraction prompt to highlight specific themes: You are a research analyst. Extract main topics, keywords, and actionable insights from this feedback: {{ $json.body }} Extend the Output Schema Add more fields like: { "suggested_blog_title": "", "tone": "", "recommendations": [] } Then update your DataTable or Sheets schema accordingly. Integration Ideas Send sentiment alerts to Slack for high-priority feedback. Push insights into Notion, Airtable, or HubSpot. Generate weekly reports summarizing trends across all submissions. Summary This workflow turns raw Jotform submissions into actionable insights using Google Gemini AI — extracting topics, keywords, and sentiment while automatically logging everything to Google Sheets.

📧 AI-Powered Email Categorization & Labeling in Zoho Mail This n8n template demonstrates how to use AI text classification to automatically categorize incoming emails in Zoho Mail and apply the correct label (e.g., Support, Billing, HR). It saves time by keeping your inbox structured and ensures emails are routed to the right category. Use cases include: Routing customer support requests to the correct team. Organizing billing and finance communications separately. Streamlining HR and recruitment email handling. Reducing inbox clutter and ensuring no important message is missed. ℹ️ Good to know You’ll need to configure Zoho OAuth credentials — see Self Client Overview, Authorization Code Flow, and Zoho Mail OAuth Guide. The labels must already exist in Zoho Mail (e.g., Support, Billing, HR). The workflow fetches these labels and applies them automatically. The Zoho Mail API domain changes depending on your account region: .com → Global accounts (https://mail.zoho.com/api/...) .eu → EU accounts (https://mail.zoho.eu/api/...) .in → India accounts (https://mail.zoho.in/api/...) Example: For an EU account, the endpoint would be: https://mail.zoho.eu/api/accounts/<accountID>/updatemessage The AI model used for text classification may incur costs depending on your provider (e.g., OpenRouter). Start by testing with a small set of emails before enabling for your full inbox. 🔄 How it works A new email in Zoho Mail triggers the workflow. OAuth authentication retrieves access to Zoho Mail’s API. All available labels are fetched, and a label map (display name → ID) is created. The AI model analyzes the subject and body to predict the correct category. The workflow routes the email to the right category branch. The matching Zoho Mail label is applied (final node is deactivated by default). 🛠️ How to use Create the required labels (e.g., Support, Billing, HR, etc.) in your Zoho Mail account before running the workflow. Replace the Zoho Mail Account ID in the Set Account ID node. Configure your Zoho OAuth credentials in the Get Access Token node. Update the API base URL to match your Zoho account’s region (.com, .eu, .in, etc.). Activate the Apply Label to Email node once ready for production. Optionally, adjust categories in the AI classifier prompt to fit your organization’s needs. 📋 Requirements Zoho Mail account with API access enabled. Labels created in Zoho Mail for each category you want to classify. OAuth credentials set up in n8n. Correct Zoho Mail API domain (.com, .eu, .in) based on your account region. An AI model (via OpenRouter or other provider) for text classification. 🎨 Customising this workflow This workflow can be adapted to many inbox management scenarios. Examples include: Auto-routing customer inquiries to specific departments. Prioritizing VIP client emails with special labels. Filtering job applications directly into an HR-managed folder.

Social Media Post & Caption Generator (Google Drive → AI Caption → Approval → Auto-Post) Automatically turn your existing content library into approved, AI-written social media posts. This workflow selects a random file from Google Drive, generates an Instagram caption using AI, sends it to you for approval, and—once approved—uploads and publishes the post via Blotato. 🎥 Watch Step-By-Step Guide: https://youtu.be/9XU9ECcj9dg What this template does On a scheduled basis (default: 10:00 AM), this workflow: Searches a specified Google Drive folder for content files Randomly selects one file to avoid repetitive posting Uses AI to generate an Instagram-ready caption based on the file name Sends the caption + file link to you via email for approval If approved: Downloads the file from Drive Uploads the media to Blotato Creates and publishes the social media post If rejected: Automatically loops back and selects a different random file Why it’s useful Keeps your social media consistent with minimal manual effort Adds a human-in-the-loop approval step for quality control Eliminates the need to manually write captions or pick content Ideal for creators, solo marketers, and small teams managing content at scale Requirements Before using this template, connect the following credentials in n8n: Google Drive OAuth (searching & downloading files) OpenAI API (caption generation) Gmail OAuth (approval email workflow) Blotato API (media upload & social posting) All credentials must be added manually after importing the template. No sensitive data is included in the template. How it works (Node overview) Schedule Trigger Runs the workflow at a fixed time each day. Google Drive – Search Files and Folders Fetches all files from a specified Drive folder. Randomizer (Code Node) Selects a random file from the available list to ensure content variety. Caption Generator AI Uses an AI model to generate a descriptive Instagram caption based on the file name. Gmail – Send for Approval and Wait Emails the generated caption and file link to you and pauses execution until approval or rejection. IF (Approved) Yes: proceeds to download, upload, and publish No: loops back to select another random file Google Drive – Download File Downloads the approved content file. Blotato – Upload Media & Create Post Uploads the media and publishes the post to the connected social account. Setup instructions Import the template into your n8n workspace Open the Google Drive nodes and connect your Drive OAuth credential Replace the Folder ID with your own content folder Connect your OpenAI credential in the Caption Generator node Connect Gmail OAuth and set your approval email address Connect your Blotato account and select the target social profile Run the workflow once to test the approval loop Activate the workflow to start automated posting Customization ideas Adjust the AI system prompt to change tone (funny, educational, sales-focused) Add hashtag rules (e.g. max 5 hashtags, niche-specific only) Replace random selection with “least recently posted” logic using a Data Table Duplicate the Blotato node to post to multiple platforms Add a fallback step to auto-edit captions that exceed character limits Troubleshooting No files found: confirm the Google Drive folder ID and permissions Approval email not received: check Gmail OAuth scopes and spam folder Caption quality not ideal: refine the AI system prompt Upload fails: confirm Blotato account permissions and social account connection