Workflow Templates

Automated project status tracking with Airtable and Motion Who's it for Project managers, team leads, and agencies who need to automatically monitor project completion status across multiple clients and send notifications when specific milestones are reached. What it does This workflow automatically tracks project progress by connecting Airtable project databases with Motion task management. It monitors specific tasks within active projects and triggers email notifications when key milestones are completed. The system is designed to handle multiple projects simultaneously and can be customized for various notification triggers. How it works The workflow follows a structured monitoring process: Data Retrieval: Fetches project information from Airtable (project names and Motion workspace IDs) Motion Integration: Connects to Motion API using HTTP requests to retrieve project details Project Filtering: Identifies only active projects with "Todo" status containing "SEO" in the name Task Monitoring: Checks for specific completed tasks (e.g., "Intégrer les articles de blog") Conditional Notifications: Sends email alerts only when target tasks are marked as "Completed" Database Updates: Updates Airtable with last notification timestamps Requirements Airtable account with project database Motion account with API access Gmail account for email notifications HTTP request authentication for Motion API How to set up Step 1: Configure your Airtable database Ensure your Airtable contains the following fields: Project names: Names of projects to monitor Motion Workspace ID: Workspace identifiers for Motion API calls Status - Calendrier éditorial: Project status field (set to "Actif" for active projects) Last sent - Calendrier éditorial: Timestamp tracking for notification frequency Email addresses: Client and team member contact information Step 2: Set up API credentials Configure the following authentication in n8n: Airtable Personal Access Token: For database access Motion API: HTTP header authentication for Motion integration Gmail OAuth2: For email notification sending Step 3: Configure Motion API integration Base URL: Uses Motion API v1 endpoints Project retrieval: Fetches projects using workspace ID parameter Task monitoring: Searches for specific task names and completion status Custom filtering: Targets projects with "SEO" in name and "Todo" status Step 4: Customize scheduling Default schedule: Runs daily between 10th-31st of each month at 8 AM Cron expression: 0 8 10-31 * * (modify as needed) Frequency options: Can be adjusted for weekly, daily, or custom intervals Step 5: Set up email notifications Configure Gmail settings: Recipients: Project managers, clients, and collaborators Subject line: Dynamic formatting with project name and month Message template: HTML-formatted email with professional signature Sender name: Customizable organization name How to customize the workflow Single project, multiple tasks monitoring To adapt for monitoring one project with several different tasks: Modify the filter conditions to target your specific project Add multiple HTTP requests for different task names Create conditional branches for each task type Set up different notification templates per task Multi-project customization Database fields: Add custom fields in Airtable for different project types Filtering logic: Modify conditions to match your project categorization Motion workspace: Support multiple workspaces per client Notification rules: Set different notification frequencies per project Alternative notification methods Replace or complement Gmail with: Slack notifications: Send updates to team channels Discord integration: Alert development teams SMS notifications: Urgent milestone alerts Webhook integrations: Connect to custom internal systems Teams notifications: Enterprise communication Task monitoring variations Multiple task types: Monitor different milestones (design, development, testing) Task dependencies: Check completion of prerequisite tasks Progress tracking: Monitor task progress percentages Deadline monitoring: Alert on approaching deadlines Conditional logic features Smart filtering system Active project detection: Only processes projects marked as "Actif" Date-based filtering: Prevents duplicate notifications using timestamp comparison Status verification: Confirms task completion before sending notifications Project type filtering: Targets specific project categories (SEO projects in this example) Notification frequency control Monthly notifications: Prevents spam by tracking last sent dates Conditional execution: Only sends emails when tasks are actually completed Database updates: Automatically records notification timestamps Loop management: Processes multiple projects sequentially Results interpretation Automated monitoring outcomes Project status tracking: Real-time monitoring of active projects Milestone notifications: Immediate alerts when key tasks complete Database synchronization: Automatic updates of notification records Team coordination: Ensures all stakeholders are informed of progress Email notification content Each notification includes: Project identification: Clear project name and context Completion confirmation: Specific task that was completed Calendar reference: Links to editorial calendars or project resources Professional formatting: Branded email template with company signature Action items: Clear next steps for recipients Use cases Agency project management Client deliverable tracking: Monitor when content is ready for client review Milestone notifications: Alert teams when phases complete Quality assurance: Ensure all deliverables meet completion criteria Client communication: Automated updates on project progress Editorial workflow management Content publication: Track when articles are integrated into websites Editorial calendar: Monitor content creation and publication schedules Team coordination: Notify writers, editors, and publishers of status changes Client approval: Alert clients when content is ready for review Development project tracking Feature completion: Monitor when development milestones are reached Testing phases: Track QA completion and deployment readiness Client delivery: Automate notifications for UAT and launch phases Team synchronization: Keep all stakeholders informed of progress Workflow limitations Motion API dependency: Requires stable Motion API access and proper authentication Single task monitoring: Currently tracks one specific task type per execution Email-only notifications: Default setup uses Gmail (easily expandable) Monthly frequency: Designed for monthly notifications (customizable) Project naming dependency: Filters based on specific naming conventions Manual configuration: Requires setup for each new project type or workspace

Transform chaotic employee departures into secure, insightful offboarding experiences - achieving zero security breaches, 100% equipment recovery, and actionable retention insights from every exit interview. What This Workflow Does Revolutionizes employee offboarding with AI-driven exit interview analysis and automated task orchestration: 📝 Exit Interview Capture - Jotform collects resignation details, ratings, feedback, and equipment inventory 🤖 AI Sentiment Analysis - Advanced AI analyzes exit interviews for retention insights, red flags, and patterns ⚠️ Red Flag Detection - Automatically identifies serious issues (harassment, discrimination, ethics) for immediate escalation 👤 Manager Intelligence - Flags management issues and provides coaching recommendations 🔐 Access Revocation - Schedules automatic system access removal on last working day 📦 Equipment Tracking - Generates personalized equipment return checklist for each employee 📚 Knowledge Transfer - Assesses knowledge transfer risk and creates handover plan 💰 Retention Analytics - Identifies preventable departures and competitive intelligence 📧 Automated Notifications - Sends checklists to employees, action items to managers, IT requests 📊 Boomerang Prediction - Calculates likelihood of rehire and maintains alumni relationships Key Features AI Exit Interview Analysis: GPT-4 provides 2+ analytical dimensions including sentiment, preventability, and red flags Preventability Scoring: AI calculates 0-100% score on whether departure was preventable Red Flag Escalation: Automatic detection of harassment, discrimination, ethics, or legal concerns Manager Performance Insights: Identifies management issues requiring coaching or intervention Sentiment Analysis: Analyzes tone, emotions, and overall sentiment from qualitative feedback Competitive Intelligence: Tracks where employees go and what competitors offer Knowledge Transfer Risk Assessment: Evaluates complexity and criticality of knowledge handover Boomerang Probability: Predicts likelihood (0-100%) of employee returning in future Department Trend Analysis: Identifies systemic issues in specific teams or departments Compensation Benchmarking: Flags compensation competitiveness issues Retention Recommendations: AI-generated actionable improvements prioritized by impact Equipment Tracking: Automatic inventory of laptops, phones, cards, and other company property Perfect For Growing Companies: 50-5,000 employees with monthly turnover requiring structured offboarding Tech Companies: Protecting IP and system access with departing engineers and developers Healthcare Organizations: Compliance-critical offboarding with HIPAA and patient data access Financial Services: Regulated industries requiring audit trails and secure access revocation Professional Services: Knowledge-intensive businesses where brain drain is costly Retail & Hospitality: High-turnover environments needing efficient, consistent offboarding Remote-First Companies: Distributed teams requiring coordinated equipment recovery What You'll Need Required Integrations Jotform - Exit interview and resignation form (free tier works) Create your form for free on Jotform using this link OpenAI API - GPT-4 for AI exit interview analysis (~$0.20-0.50 per exit interview) Gmail - Automated notifications to employees, managers, IT, and HR Google Sheets - Exit interview database and retention analytics Quick Start Import Template - Copy JSON and import into n8n Add OpenAI Credentials - Set up OpenAI API key (GPT-4 for best insights) Create Jotform Exit Interview - Build comprehensive form with these sections: Configure Gmail - Add Gmail OAuth2 credentials Setup Google Sheets: Create spreadsheet with "Exit_Interviews" sheet Replace YOUR_GOOGLE_SHEET_ID in workflow Columns will auto-populate on first submission Customization Options AI Prompt Refinement: Tailor analysis for your industry, company culture, and specific concerns Red Flag Categories: Customize what constitutes a red flag for your organization Equipment Types: Add specialized equipment (tools, uniforms, parking passes) Access Systems: Integrate with your specific IT systems for automated revocation Knowledge Transfer Templates: Create role-specific handover checklists Manager Notifications: Add more details based on department or seniority Exit Interview Questions: Add industry-specific or company-specific questions Retention Focus Areas: Adjust AI to focus on specific retention priorities Rehire Workflows: Add automatic alumni network invitations for boomerang candidates Severance Processing: Add nodes for severance agreement generation and tracking Reference Check Process: Include reference policy notifications Benefits COBRA: Automate COBRA benefits notification workflows Expected Results Zero security breaches from lingering access - automated revocation on last day 100% equipment recovery - automated tracking and follow-up 3x faster offboarding - 30 minutes vs 2 hours of manual coordination 85% actionable insights from exit interviews vs 20% with manual reviews 60% improvement in identifying preventable turnover 90% manager compliance with knowledge transfer (vs 40% manual) 50% reduction in repeat management issues through coaching identification 40% increase in boomerang rehires through positive offboarding experience Complete audit trail for legal compliance and investigations Department-level insights identifying systemic retention issues Use Cases Tech Startup (100 Employees, High Growth) Engineer resigns to join competitor. AI detects compensation issue (40% below market), flags manager micromanagement concerns, and identifies preventable departure (preventability: 85%). HR immediately initiates compensation review for engineering team, schedules manager coaching, and retains 3 other engineers considering leaving. Access to codebase revoked automatically on last day. Boomerang probability: 70% - maintains relationship for future recruiting. Healthcare System (500 Nurses) Nurse leaves citing burnout. AI identifies systemic staffing issues in ER department affecting 15% of departures. Flags potential HIPAA violation concern requiring investigation. Automatically revokes EHR access on final day. Equipment recovery (badge, pager, scrubs) tracked with 100% success. Exit insights lead to ER staffing model changes, reducing nurse turnover by 30%. Financial Services Firm Compliance officer departs. AI red flags potential ethics concern requiring immediate investigation. Legal team notified within minutes. Knowledge transfer flagged as "critical risk" due to regulatory expertise. Detailed 30-day handover plan auto-generated. All system access revoked immediately. Complete audit trail maintained for regulatory review. Investigation reveals process gap, not ethical issue, preventing regulatory exposure. Retail Chain (2,000 Employees) Store manager exits. AI aggregates insights across 50 recent retail departures, identifying district manager as common thread (manager rating consistently 2/5). Regional HR intervenes with district manager coaching. Equipment return (keys, registers codes, uniforms) automated via checklist. 95% equipment recovery vs previous 60%. Sentiment trends show seasonal staff prefer flexible scheduling - policy updated chain-wide. Remote Software Company Developer in different timezone resigns. Automated offboarding coordinates across 3 time zones: access revoked at EOD local time, equipment return label emailed internationally, knowledge transfer scheduled with overlap hours. AI detects "career growth" as preventable issue - company implements career ladder framework, reducing senior developer attrition by 45%. Pro Tips Timing Matters: Send Jotform link 1 week before last day for honest feedback (not on exit day) Anonymity Option: Consider anonymous feedback for more candid responses (separate form) Benchmark Scoring: After 50+ exits, calculate your company's average preventability score Manager Patterns: Track exits by manager to identify coaching needs early Department Trends: Monthly reviews of AI insights by department for systemic issues Compensation Data: Cross-reference "compensation issue" flags with market data Boomerang Program: Create formal alumni network for high-probability boomerang candidates Equipment Deposits: Consider requiring deposits for easier equipment recovery Exit Interview Training: Train managers on how to act on AI insights constructively Legal Review: Have legal team review red flag escalation categories quarterly Continuous Improvement: Use AI recommendations to create quarterly retention action plans Stay Interviews: Use exit interview insights to inform "stay interview" questions for current employees Learning Resources This workflow demonstrates advanced n8n automation patterns: AI Agents with complex structured output for multi-dimensional analysis, Sentiment analysis and natural language processing Conditional escalation based on severity and red flags Multi-stakeholder notifications with role-specific messaging Risk assessment algorithms for knowledge transfer and preventability Pattern recognition across qualitative feedback Equipment inventory management with dynamic list generation Compliance automation for access revocation scheduling Predictive analytics for boomerang probability Perfect for learning AI-powered HR automation and organizational analytics! 📊 Workflow Architecture 📝 Jotform Exit Interview Submission ↓ 🧾 Parse Offboarding Data ↓ 🤖 AI Exit Interview Analysis (GPT-4) │ ├─ Retention analysis (preventability scoring) │ ├─ Sentiment analysis (tone, emotions) │ ├─ Manager performance evaluation │ ├─ Department insights │ ├─ Compensation benchmarking │ ├─ Knowledge transfer risk assessment │ ├─ Competitor intelligence │ ├─ Red flag detection │ ├─ Boomerang probability │ └─ Action item generation ↓ 🔗 Extract AI Analysis (JSON) ↓ 🧩 Merge Exit Analysis with Data │ ├─ Calculate days until last day │ ├─ Build equipment checklist │ └─ Assess urgency levels ↓ ⚠️ Has Red Flags? ├─ TRUE → 🚨 Send Red Flag Alert (HR Director/Legal) │ ↓ └─ FALSE → 📧 Send Manager Action Items ↓ ✉️ Send Employee Checklist ↓ 🔐 Send IT Offboarding Request ↓ 📊 Log to Google Sheets Ready to transform employee offboarding? Import this template and turn departures into retention insights while maintaining security and professionalism. Every exit becomes a learning opportunity! 🚪✨ Questions or customization needs? The workflow includes detailed sticky notes explaining each AI analysis component and routing decision.

Overview This comprehensive workflow transforms Excel spreadsheets into professional, AI-generated reports with automated analysis and insights. Whether you're dealing with financial data, customer tracking, sales metrics, inventory management, or any structured data in Excel format, this template leverages artificial intelligence to create detailed, actionable reports with visualizations and key findings. How It Works Automated Report Generation Pipeline: File Processing Trigger**: Workflow initiates when Excel files are uploaded through a web form or file system Data Extraction & Validation**: Automatically reads Excel sheets, validates data structure, and identifies key metrics AI-Powered Analysis**: Uses advanced language models to analyze data patterns, trends, and anomalies Report Generation**: Creates comprehensive reports with executive summaries, detailed analysis, and actionable recommendations Multi-Format Output**: Generates reports in various formats (PDF, HTML, Word) with embedded charts and visualizations Automated Distribution**: Sends completed reports via email or saves to designated cloud storage locations Setup Instructions Estimated Setup Time: 10-15 minutes Prerequisites n8n instance (v0.200.0 or higher) OpenAI/Claude API key for AI analysis Email service credentials (for report distribution) Cloud storage access (Google Drive, Dropbox, etc.) - optional Configuration Steps Configure File Input Trigger Set up webhook or file system trigger for Excel file uploads Configure accepted file formats (.xlsx, .xls, .csv) Add file size and validation checks Setup Data Processing Nodes Configure Excel file reader with sheet selection options Set up data validation and cleaning processes Define column mapping and data type recognition Configure AI Analysis Engine Add your AI service API credentials (OpenAI, Anthropic, etc.) Customize analysis prompts based on your data types Set up context-aware report generation parameters Setup Report Generation Configure report templates for different data types Set up chart generation and data visualization options Define output formats and styling preferences Configure Distribution Channels Set up email service for automated report delivery Configure cloud storage integration for report archiving Add notification systems for completion alerts Use Cases Financial Reporting Budget Analysis**: Analyze spending patterns and budget variance reports P&L Statements**: Generate profit and loss summaries with trend analysis Cash Flow Reports**: Create comprehensive cash flow analysis with forecasting Expense Tracking**: Automated expense categorization and spending insights Sales & CRM Analytics Sales Performance**: Generate sales team performance reports with KPIs Customer Analysis**: Create customer segmentation and lifetime value reports Lead Tracking**: Analyze conversion funnels and lead quality metrics Territory Management**: Regional sales analysis and market penetration reports Operations Management Inventory Reports**: Stock level analysis with reorder recommendations Project Tracking**: Progress reports with timeline and resource analysis Quality Metrics**: Performance dashboards with trend identification Resource Planning**: Capacity utilization and allocation reports HR & Administrative Employee Performance**: Generate comprehensive performance review reports Attendance Tracking**: Analyze attendance patterns and productivity metrics Training Records**: Skills gap analysis and training effectiveness reports Compliance Reporting**: Regulatory compliance status and audit reports Key Features Intelligent Data Recognition**: Automatically identifies data types and relationships Contextual Analysis**: AI provides industry-specific insights and recommendations Professional Formatting**: Clean, corporate-ready report layouts Interactive Visualizations**: Embedded charts, graphs, and data visualizations Executive Summaries**: AI-generated executive summaries highlighting key findings Trend Analysis**: Historical data comparison and future projections Anomaly Detection**: Automatically flags unusual patterns or outliers Multi-Language Support**: Generate reports in multiple languages Batch Processing**: Handle multiple files simultaneously Error Handling**: Robust error management with detailed logging Technical Requirements n8n instance with sufficient memory for Excel processing (minimum 2GB RAM recommended) AI service API access (OpenAI GPT-4, Claude, or similar) Email service (Gmail, Outlook, SendGrid, etc.) Optional: Cloud storage service credentials Stable internet connectivity for AI API calls Supported Data Types Financial Data**: Revenue, expenses, budgets, forecasts Sales Data**: Transactions, leads, customer information, pipeline data Operational Data**: Inventory, production metrics, quality scores HR Data**: Employee records, performance metrics, attendance Marketing Data**: Campaign metrics, conversion rates, ROI analysis Custom Data**: Any structured Excel data with clear column headers Output Options PDF Reports**: Professional PDF documents with embedded charts HTML Dashboards**: Interactive web-based reports Word Documents**: Editable Word reports with tables and charts Excel Summaries**: Enhanced Excel files with analysis sheets PowerPoint Presentations**: Executive presentation slides Advanced Features Custom Branding**: Add your company logo and branding to reports Scheduled Processing**: Set up automated report generation schedules Template Customization**: Create custom report templates for different data types Integration Ready**: Easy integration with existing business systems Audit Trail**: Complete logging of all processing steps and data changes Support & Troubleshooting For optimal performance, ensure your Excel files have clear column headers and consistent data formatting. The AI analysis works best with clean, well-structured data. For complex financial calculations, verify results against your existing systems during initial setup.

Transform your fleet operations from paper-based chaos to intelligent automation - achieving 40% reduction in breakdowns, 100% inspection compliance, and predictive maintenance that saves thousands in repair costs. What This Workflow Does Revolutionizes fleet management with AI-driven vehicle inspections and predictive maintenance: 📝 Digital Inspections - Jotform captures daily vehicle checks with photos, mileage, and comprehensive checklists 🤖 AI Condition Analysis - Advanced AI Agent evaluates vehicle condition, safety ratings, and maintenance needs ⚠️ Smart Prioritization - Automatically flags critical issues (brakes, safety concerns, DOT compliance) 🔧 Maintenance Routing - Routes issues to appropriate shop teams with detailed work orders 📊 Predictive Maintenance - Tracks mileage thresholds and predicts upcoming service needs ✉️ Automated Notifications - Sends alerts to maintenance teams and confirmation to drivers 📈 Compliance Tracking - Monitors DOT inspections, registrations, and annual certifications 💰 Cost Management - Estimates repair costs and tracks downtime to optimize fleet budget 📋 Complete Documentation - Logs all inspections to Google Sheets for audits and analytics Key Features AI-Powered Vehicle Assessment: GPT-4 analyzes inspection data across 10+ components with safety ratings (0-100) Critical Issue Detection: Automatic identification of safety concerns, DOT violations, and immediate action items Mileage-Based Scheduling: Tracks oil changes, tire rotations, brake inspections with automated reminders Compliance Management: Monitors annual inspections, DOT certifications, and registration expiries Work Order Generation: Creates detailed maintenance orders with instructions, parts needed, and cost estimates Driver Performance Tracking: Evaluates vehicle care quality and identifies training needs Predictive Analytics: Forecasts upcoming maintenance based on usage patterns and vehicle age Emergency Routing: Critical issues trigger immediate alerts to maintenance supervisors Photo Documentation: Captures damage and odometer photos for insurance and warranty claims Real-Time Fleet Status: Tracks operational, out-of-service, and maintenance-required vehicles Cost Estimation: AI-generated repair cost ranges and downtime predictions DOT Audit Ready: Complete inspection logs formatted for regulatory compliance Perfect For Commercial Fleet Operators: Delivery companies, logistics firms managing 10-500+ vehicles Transportation Companies: Trucking fleets requiring DOT compliance and safety standards Service Businesses: Plumbing, HVAC, electrical companies with service vehicle fleets Government Fleets: Municipal vehicles, police departments, public works departments Rental Car Companies: Daily inspections and damage documentation for rental fleets Construction Companies: Heavy equipment and vehicle maintenance tracking Food Delivery Services: High-mileage vehicles requiring frequent inspections Ride-Share Fleet Managers: TNC vehicles needing daily safety checks What You'll Need Required Integrations Jotform - Digital inspection form with photo upload (free tier works) Create your form for free on Jotform using this link OpenAI API - GPT-4 for AI vehicle analysis (~$0.15-0.40 per inspection) Gmail - Automated notifications to maintenance teams and drivers Google Sheets - Inspection database, maintenance tracking, and compliance logs Optional Enhancements Twilio - SMS alerts for critical issues and driver notifications Google Calendar - Automated maintenance scheduling QuickBooks - Expense tracking and repair cost management Fleet Management Software - Integration with Geotab, Samsara, or Fleetio Zapier - Additional integration bridges for specialty systems Google Drive - Photo backup and document storage Maintenance Software - Connect to shop management systems Telematics Integration - Real-time mileage and diagnostics data Quick Start Import Template - Copy JSON and import into n8n Add OpenAI Credentials - Set up OpenAI API key (GPT-4 recommended for accuracy) Create Jotform - Build vehicle inspection form with these essential fields: Driver Info: Name, Email Vehicle Details: Vehicle ID, Make, Model, Year, License Plate Mileage: Current Odometer Reading Fuel Level: Dropdown (Full, 3/4, 1/2, 1/4, Empty) Inspection Checklist: Dropdowns for each component (Good, Fair, Poor, Needs Immediate Attention) Tires Brakes Lights (headlights, taillights, turn signals) Fluid Levels (oil, coolant, brake fluid) Engine Transmission Interior Condition Exterior Condition Issues: Yes/No dropdown + Long text for description Photos: File upload for damage photos and odometer photo Cleanliness Rating: 1-5 star rating Driver Notes: Textarea for additional comments Configure Gmail - Add Gmail OAuth2 credentials for notifications Setup Google Sheets: Create new spreadsheet for fleet tracking Add sheet named "Inspections" Replace YOUR_GOOGLE_SHEET_ID in the workflow Google Sheets will auto-populate columns on first run Customization Options AI Prompt Refinement: Tailor analysis for specific vehicle types (trucks, vans, sedans, heavy equipment) Custom Maintenance Intervals: Adjust service schedules based on manufacturer recommendations Multi-Location Support: Route work orders to different shop locations based on vehicle assignment Priority Escalation: Add manager approval workflows for expensive repairs Driver Training Module: Track recurring issues per driver and generate training recommendations Seasonal Adjustments: Different inspection criteria for winter/summer (tire tread, AC, heating) Vehicle Categories: Separate workflows for passenger vehicles, trucks, specialty equipment Cost Approval Thresholds: Require manager sign-off for repairs over $X amount Parts Inventory Integration: Check parts availability before scheduling maintenance Vendor Management: Route different issue types to specialized vendors Mobile Optimization: Design Jotform specifically for mobile/tablet use in vehicles Offline Mode: Enable Jotform offline submissions for areas with poor connectivity Expected Results 40% reduction in breakdowns - Predictive maintenance catches issues early 100% inspection compliance - Digital tracking eliminates missed checks 24-hour turnaround on maintenance scheduling vs days of manual coordination 30% cost savings - Preventive maintenance avoids expensive emergency repairs 60% faster inspections - Digital forms take 5 minutes vs 15+ for paper Zero lost paperwork - All inspections digitally stored and searchable 85% better DOT audit results - Complete, organized documentation 50% reduction in vehicle downtime - Proactive maintenance scheduling 95% driver compliance - Easy mobile forms increase participation Real-time fleet visibility - Instant status of all vehicles Pro Tips QR Code Access: Place QR codes in each vehicle linking directly to that vehicle's inspection form Pre-Fill Vehicle Data: Use Jotform conditional logic to auto-fill vehicle details when driver enters Vehicle ID Photo Requirements: Make damage and odometer photos mandatory for compliance Daily Reminders: Set up automated daily email/SMS reminders for drivers to complete inspections Seasonal Checklists: Adjust inspection criteria seasonally (winter: tire tread/battery; summer: AC/coolant) Benchmark Analysis: After 100+ inspections, analyze AI accuracy and refine the prompt with real examples Driver Training: Use AI driver performance ratings to identify training needs Telematics Integration: Connect to vehicle GPS/diagnostics for automatic mileage updates Parts Pre-Ordering: Use predictive maintenance to pre-order common parts before needed Maintenance History: Track vehicle-specific patterns (e.g., Vehicle #12 always needs brake work) Incentive Programs: Reward drivers with best vehicle care ratings Mobile-First Design: Ensure Jotform works perfectly on phones - most inspections done on mobile Learning Resources This workflow demonstrates advanced n8n automation: AI Agents with structured JSON output for reliable vehicle assessment Conditional routing based on criticality and safety ratings Database lookups for vehicle maintenance history Predictive analytics using mileage thresholds and time intervals Multi-recipient notifications with role-based messaging Compliance tracking with automatic deadline monitoring Cost estimation algorithms for budget planning Photo handling for documentation and insurance claims Error handling with fallback assessments Perfect for learning fleet operations automation and AI integration! 📊 Workflow Architecture 📝 Jotform Daily Inspection ↓ 🧾 Parse Inspection Data ↓ 📊 Get Vehicle History │ ├─ Last service dates │ ├─ Mileage calculations │ └─ Compliance deadlines ↓ 🤖 AI Fleet Analysis (GPT-4) │ ├─ Condition assessment │ ├─ Safety rating (0-100) │ ├─ Critical issue detection │ ├─ Maintenance recommendations │ ├─ Cost estimation │ ├─ DOT compliance check │ └─ Work order generation ↓ 🔗 Extract & Merge AI Analysis ↓ ⚡ Critical Issue Check ├─ TRUE → 🚨 Critical Alert Email (Maintenance) └─ FALSE → 📋 Routine Report Email (Maintenance) ↓ ✉️ Driver Confirmation Email │ ├─ Inspection received │ ├─ Vehicle status │ ├─ Maintenance scheduled │ └─ Safety notices ↓ 📊 Log to Google Sheets └─ Inspection database └─ Audit trail └─ Analytics data 🔐 Compliance & Security Ready to transform your fleet management? Import this template and eliminate breakdowns, ensure compliance, and save thousands in maintenance costs through AI-powered predictive maintenance! 🚗✨ Questions or customization needs? The workflow includes detailed sticky notes explaining each component and decision point.

Description Automate your team's daily stand-ups with AI-powered morning briefs, directly pulled from ClickUp tasks and shared via Slack and Gmail every morning. ☀️📋💬 What This Template Does Triggers automatically at 9:15 AM each morning via cron. ⏰ Fetches the latest sprint and all active or due-today tasks from ClickUp. Categorizes tasks by status, priority, and assignee for clear visibility. Uses Azure OpenAI GPT-4o to generate a detailed, structured morning summary. Formats the AI summary into a clean HTML email and a Slack post. Sends automated updates to Gmail and Slack channels. Includes real-time error detection and Slack alerts for quick debugging. Key Benefits ✅ Eliminates manual stand-up prep by generating AI-driven daily reports. ✅ Keeps teams aligned with clear task summaries and blocker tracking. ✅ Automatically distributes updates across Slack and Gmail. ✅ Provides HTML-formatted emails and Slack markdown summaries. ✅ Reduces time spent on daily check-ins and sprint reviews. Features Fully automated daily scheduling using cron triggers. Real-time task fetching and categorization from ClickUp. GPT-4-powered summarization for executive-style briefs. Responsive HTML email builder for beautiful reports. Slack integration for quick, shareable updates. Error handling with dedicated Slack notifications. Requirements ClickUp OAuth2 credentials for task access. Azure OpenAI GPT-4o API credentials for summary generation. Slack API credentials for channel posting. Gmail OAuth2 credentials for sending email reports. Target Audience Project managers and team leads needing automated daily briefings 👩‍💼 Development teams using ClickUp for sprint and task tracking 💻 Agencies or operations teams coordinating across tools 🔄 Remote teams seeking quick alignment and productivity boosts 🌍 Step-by-Step Setup Instructions Connect your ClickUp account via OAuth2 and update Team, Space, and Folder IDs. ⚙️ Add Azure OpenAI GPT-4o credentials for AI summary generation. 🤖 Configure Slack OAuth2 credentials and set the target channel ID. 💬 Connect Gmail OAuth2 and define recipient email addresses. 📧 Customize the schedule trigger time (default: 9:15 AM). 🕒 Test the workflow to verify proper data retrieval and message delivery. 🚀

Travel Reimbursement - OCR & Expense Extraction Workflow Overview This is a lightweight n8n workflow that accepts chat input and uploaded receipts, runs OCR, stores parsed results in Supabase, and uses an AI agent to extract structured travel expense data and compute totals. Designed for zero retention operation and fast integration. Workflow Structure Frontend:** Chat UI trigger that accepts text and file uploads. Preprocessing:** Binary normalization + per-file OCR request. Storage:** Store OCR-parsed blocks in Supabase temp_table. Core AI:** Travel reimbursement agent that extracts fields, infers missing values, and calculates totals using the Calculator tool. Output:** Agent responds to the chat with a concise expense summary and breakdowns. Chat Trigger (Frontend) Trigger node:** When chat message received public: true, allowFileUploads: true, sessionId used to tie uploads to the chat session. Custom CSS + initial messages configured for user experience. Binary Presence Check Node:** CHECK IF BINARY FILE IS PRESENT OR NOT (IF) Checks whether incoming payload contains files. If files present -> route to Split Out -> NORMALIZE binary file -> OCR (ANY OCR API) -> STORE OCR OUTPUT -> Merge. If no files -> route directly to Merge -> Travel reimbursement agent. Binary Normalization Node:** Split Out and NORMALIZE binary file (Code) Split Out extracts binary entries into a data field. NORMALIZE binary file picks the first binary key and rewrites payload to binary.data for consistent downstream shape. OCR Node:** OCR (ANY OCR API ) (HTTP Request) Sends multipart/form-data to OCR endpoint, expects JSONL or JSON with blocks. Body includes mode=single, output_type=jsonl, include_images=false. Store OCR Output Node:** STORE OCR OUTPUT (Supabase) Upserts into temp_table with session_id, parsed blocks, and file_name. Used by agent to fetch previously uploaded receipts for same session. Memory & Tooling Nodes:** Simple Memory and Simple Memory1 (memoryBufferWindow) Keep last 10 messages for session context. Node:** Calculator1 (toolCalculator) Used by agent to sum multiple charges, handle currency arithmetic and totals. Travel Reimbursement Agent (Core) Node:** Travel reimbursement agent (LangChain agent) Model: Mistral Cloud Chat Model (mistral-medium-latest) Behavior: Parse OCR blocks and non-file chat input. Extract required fields: vendor_name, category, invoice_date, checkin_date, checkout_date, time, currency, total_amount, notes, estimated. When fields are missing, infer logically and mark estimated: true. Use Calculator tool to sum totals across multiple receipts. Fetch stored OCR entries from Supabase when user asks for session summaries. Always attempt extraction; never reply with "unclear" or ask for a reupload unless user requests audit-grade precision. Final output: Clean expense table and Grand Total formatted for chat. Data Flow Summary User sends chat message plus or minus file. IF file present -> Split Out -> Normalize -> OCR -> Store OCR output -> Merge with chat payload. Travel reimbursement agent consumes merged item, extracts fields, uses Calculator tool for sums, and replies with a formatted expense summary. Integrations Used | Service | Purpose | Credential | |---------|---------|-----------| | Mistral Cloud | LLM for agent | Mistral account | | Supabase | Store parsed OCR blocks and session data | Supabase account | | OCR API | Text extraction from images/PDFs | Configurable HTTP endpoint | | n8n Core | Flow control, parsing, editing | Native | Agent System Prompt Summary > You are a Travel Expense Extraction and Calculation AI. Extract vendor, dates, currency, category, and total amounts from uploaded receipts, invoices, hotel bills, PDFs, and images. Infer values when necessary and mark them as estimated. When asked, fetch session entries from Supabase and compute totals using the Calculator tool. Respond in a concise business professional format with a category wise breakdown and a Grand Total. Never reply "unclear" or ask for a reupload unless explicitly asked. Required final response format example: Key Features Zero retention friendly design: OCR output stored only to temp_table per session. Robust extraction with inference when OCR quality is imperfect. Session aware: agent retrieves stored receipts for consolidated totals. Calculator integration for accurate numeric sums and currency handling. Configurable OCR endpoint so you can swap providers without changing logic. Setup Checklist Add Mistral Cloud and Supabase credentials. Configure OCR endpoint to accept multipart uploads and return blocks. Create temp_table schema with session_id, file, file_name. Test with single receipts, multipage PDFs, and mixed uploads. Validate agent responses and Calculator totals. Summary A practical n8n workflow for travel expense automation: accept receipts, run OCR, store parsed data per session, extract structured fields via an AI agent, compute totals, and return clean expense summaries in chat. Built for reliability and easy integration. Need Help or More Workflows? We can integrate this into your environment, tune the agent prompt, or adapt it for different OCR providers. We can help you set it up for free — from connecting credentials to deploying it live. Contact: shilpa.raju@digitalbiz.tech Website: https://www.digitalbiz.tech LinkedIn: https://www.linkedin.com/company/digitalbiztech/ You can also DM us on LinkedIn for any help.

Try It Out, HireMind – AI-Driven Resume Intelligence Pipeline! This n8n template demonstrates how to automate resume screening and evaluation using AI to improve candidate processing and reduce manual HR effort. A smart and reliable resume screening pipeline for modern HR teams. This workflow combines Google Drive (JD & CV storage), OpenAI (GPT-4-based evaluation), Google Sheets (position mapping + result log), and Slack/SendGrid integrations for real-time communication. Automatically extract, evaluate, and track candidate applications with clarity and consistency. How it works A candidate submits their application using a form that includes name, email, CV (PDF), and a selected job role. The CV is uploaded to Google Drive for record-keeping and later reference. The Profile Analyzer Agent reads the uploaded resume, extracts structured candidate information, and transforms it into a standardized JSON format using GPT-4 and a custom output parser. The corresponding job description PDF file is automatically retrieved from a Google Sheet based on the selected job role. The HR Expert Agent evaluates the candidate profile against the job description using another GPT-4 model, generating a structured assessment that includes strengths, gaps, and an overall recommendation. The evaluation result is parsed and formatted for output. The evaluation score will be used to mark candidate as qualified or unqualified, based on that an email will be sent to applicant or the message will be send to hiring team for the next process The final evaluation result will be stored in a Google Sheet for long-term tracking and reporting. Google drive structure ├── jd # Google drive folder to store your JD (pdf) │ ├── Backend_Engineer.pdf │ ├── Azure_DevOps_Lead.pdf │ └── ... │ ├── cv # Google drive folder, where workflow upload candidate resume │ ├── John_Doe_DevOps.pdf │ ├── Jane_Smith_FullStack.pdf │ └── ... │ ├── Positions (Sample: https://docs.google.com/spreadsheets/d/1pW0muHp1NXwh2GiRvGVwGGRYCkcMR7z8NyS9wvSPYjs/edit?usp=sharing) # 📋 Mapping Table: Job Role ↔ Job Description (Link) │ └── Columns: │ - Job Role │ - Job Description File URL (PDF in jd/) │ └── Evaluation form (Google Sheet) # ✅ Final AI Evaluation Results How to use Set up credentials and integrations: Connect your OpenAI account (GPT-4 API). Enable Google Cloud APIs: Google Sheets API (for reading job roles and saving evaluation results) Google Drive API (for storing CVs and job descriptions) Set up SendGrid (to send email responses to candidates) Connect Slack (to send messages to the hiring team) Prepare your Google Drive structure: Create a root folder, then inside it create: /jd → Store all job descriptions in PDF format /cv → This is where candidate CVs will be uploaded automatically Create a Google Sheet named Positions with the following structure: | Job Role | Job Description Link | |------------------------------|----------------------------------------| | Azure DevOps Engineer | https://drive.google.com/xxx/jd1.pdf | | Full-Stack Developer (.NET) | https://drive.google.com/xxx/jd2.pdf | Update your application form: Use the built-in form, or connect your own (e.g., Typeform, Tally, Webflow, etc.) Ensure the Job Role dropdown matches exactly the roles in the Positions sheet Run the AI workflow: When a candidate submits the form: Their CV is uploaded to the /cv folder The job role is used to match the JD from /jd The Profile Analyzer Agent extracts candidate info from the CV The HR Expert Agent evaluates the candidate against the matched JD using GPT-4 Distribute and store results: Store the evaluation results in the Evaluation form Google Sheet Optionally notify your team: ✉️ Send an email to the candidate using SendGrid 💬 Send a Slack message to the hiring team with a summary and next steps Requirements OpenAI GPT-4 account for both Profile Analyzer and HR Expert Agents Google Drive account (for storing CVs and evaluation sheet) Google Sheets API credentials (for JD source and evaluation results) Need Help? Join the n8n Discord or ask in the n8n Forum! Happy Hiring! 🚀

Overview This project is an AI-powered WhatsApp virtual receptionist built using n8n, designed to handle both text and voice-based customer messages automatically. The workflow integrates Google Gemini, Pinecone, and the WhatsApp Business API to provide intelligent, context-aware responses that feel natural and professional. How It Works Message Detection The workflow begins when a message arrives on WhatsApp. It identifies whether the message is text or voice and routes it accordingly. Voice Message Handling Audio messages are securely downloaded from WhatsApp. The files are converted to Base64 format and sent to the Gemini API for transcription. The transcribed text is then passed to the AI Agent for further processing. AI Agent Processing The LangChain AI Agent acts as the brain of the system. It uses: Google Gemini Chat Model** for natural language understanding and response generation. Pinecone Vector Store** to retrieve company-specific information and product data. Memory Buffer** to remember the last 20 user messages, ensuring context-aware responses. The agent also follows a set of custom communication rules — replying only in approved languages, skipping greetings, and focusing on direct, helpful, and professional responses (e.g., product recommendations, support, or guidance). Knowledge Retrieval The AI Agent connects to a Pinecone database containing detailed company data, such as product catalogs or service FAQs. Using Gemini-generated embeddings, it retrieves the most relevant information for each user query. Response Delivery Once the AI Agent prepares the response, it is instantly sent back to the user via WhatsApp, completing the conversational loop. Who It’s For This system is ideal for businesses seeking to automate their customer communication through WhatsApp. It’s especially valuable for: Product-based companies** with frequent customer inquiries. Service providers** offering 24/7 customer assistance or quote requests. SMBs** looking to scale their communication without hiring additional staff. Tech Stack & Requirements n8n** – Workflow automation and orchestration. WhatsApp Cloud API** – For sending and receiving messages. Google Gemini (PaLM)** – For LLM-based transcription and response generation. Pinecone** – Vector database for product and service knowledge retrieval. LangChain Integration** – For connecting memory, vector store, and reasoning tools. Custom Business Rules** – Configurable within the AI Agent node to manage tone, style, and workflow behavior. Key Features Handles both text and voice messages seamlessly. Responds in multiple languages, including English. Maintains conversation memory per user session. Retrieves accurate company-specific information using vector search. Fully automated, with customizable behavior for different industries or use cases. Setup Instructions 1. Prerequisites Before importing the workflow, ensure you have: An active n8n instance (self-hosted or n8n Cloud). WhatsApp Cloud API credentials** from Meta. Google Gemini API key** with model access (for chat and transcription). Pinecone API key** with a preconfigured vector index containing your company data. 2. Environment Setup Install all required credentials under Settings → Credentials in n8n. Add environment variables (if applicable) for keys like: GOOGLE_API_KEY=your_google_gemini_key PINECONE_API_KEY=your_pinecone_key WHATSAPP_ACCESS_TOKEN=your_whatsapp_token 3. Pinecone Configuration Create a Pinecone index named, for example, products-index. Upload company documents or product details as vector embeddings using Gemini or LangChain utilities. Adjust the retrieval limit in the Pinecone node settings for broader or narrower search responses. 4. WhatsApp API Configuration Set up a WhatsApp Business Account via Meta Developer Dashboard. Create a webhook endpoint URL (n8n’s public URL) to receive WhatsApp messages. Use the WhatsApp Trigger Node to capture messages in real time. 5. AI Agent Customization You can personalize how the AI behaves by editing the system prompt inside the AI Agent node: Modify tone, response length, or product focus. Add new “rules” for language preferences or conversation flow. Include links or custom text output (e.g., quotation formats, product catalog messages). 6. Handling Voice Messages Ensure your WhatsApp Business Account has media message permissions enabled. Verify the HTTP Request node that connects to the Gemini API for transcription is correctly authenticated. You can adjust the transcription model or prompt if you prefer shorter, keyword-based outputs. 7. Testing Send both text and voice messages from a test WhatsApp number. Check response time and message formatting. Use n8n’s execution logs to debug errors (especially for media downloads or API credentials). Customization Options 🧩 AI Behavior Modify the AI Agent’s system message to adapt tone and personality (e.g., sales-oriented, support-driven). Update memory length (default: last 20 messages) for longer or shorter conversations. 🌍 Multi-language Support Add or remove allowed languages in the rules section of the AI Agent node. For multilingual businesses, duplicate the AI Agent path and route messages by language detection. 📦 Industry Adaptation Swap the Pinecone dataset to suit different industries — retail, hospitality, logistics, etc. Replace product data with FAQs, customer records, or support documentation.

This n8n template demonstrates how to automatically generate and publish blog posts using trending keywords, AI-generated content, and watermarked stock images. Use cases include maintaining an active blog with fresh SEO content, scaling content marketing without manual writing, and automating the full publishing pipeline from keyword research to WordPress posting. Good to know At time of writing, each AI content generation step will incur costs depending on your OpenAI pricing plan. Image search is powered by Pexels, which provides free-to-use stock images. The workflow also applies a watermark for branding. Google Trends data may vary by region, and results depend on availability in your selected location. How it works The workflow begins with a scheduled trigger that fetches trending keywords from Google Trends. The XML feed is converted to JSON and filtered for relevant terms, which are logged into a Google Sheet for tracking. One random keyword is selected, and OpenAI is used to generate blog content around it. A structured output parser ensures the text is clean and well-formatted. The system then searches Pexels for a matching image, uploads it, adds metadata for SEO, and applies a watermark. Finally, the complete article (text and image) is published directly to WordPress. How to use The schedule trigger is provided as an example, but you can replace it with other triggers such as webhooks or manual inputs. You can also customize the AI prompt to match your niche, tone, or industry focus. For higher volumes, consider adjusting the keyword filtering and batching logic. Requirements OpenAI account for content generation Pexels API key for stock image search Google account with Sheets for keyword tracking WordPress site with API access for publishing Customising this workflow This automation can be adapted for different use cases. Try adjusting the prompts for technical blogs, fashion, finance, or product reviews. You can also replace the image source with other providers or integrate your own media library. The watermark feature ensures branding, but it can be modified or removed depending on your needs.

Try it out This n8n workflow template lets you chat with your Google Drive documents (.docx, .json, .md, .txt, .pdf) using OpenAI and Pinecone Assistant. It retrieves relevant context from your files in real time so you can get accurate, context-aware answers about your proprietary data—without the need to train your own LLM. What is Pinecone Assistant? Pinecone Assistant allows you to build production-grade chat and agent-based applications quickly. It abstracts the complexities of implementing retrieval-augmented (RAG) systems by managing the chunking, embedding, storage, query planning, vector search, model orchestration, reranking for you. Prerequisites A Pinecone account and API key A GCP project with Google Drive API enabled and configured Note: When setting up the OAuth consent screen, skip steps 8-10 if running on localhost An Open AI account and API key Setup Create a Pinecone Assistant in the Pinecone Console here Name your Assistant n8n-assistant and create it in the United States region If you use a different name or region, update the related nodes to reflect these changes No need to configure a Chat model or Assistant instructions Setup your Google Drive OAuth2 API credential in n8n In the File added node -> Credential to connect with, select Create new credential Set the Client ID and Client Secret from the values generated in the prerequisites Set the OAuth Redirect URL from the n8n credential in the Google Cloud Console (instructions) Name this credential Google Drive account so that other nodes reference it Setup Pinecone API key credential in n8n In the Upload file to assistant node -> PineconeApi section, select Create new credential Paste in your Pinecone API key in the API Key field Setup Pinecone MCP Bearer auth credential in n8n In the Pinecone Assistant node -> Credential for Bearer Auth section, select Create new credential Set the Bearer Token field to your Pinecone API key used in the previous step Setup the Open AI credential in n8n In the OpenAI Chat Model node -> Credential to connect with, select Create new credential Set the API Key field to your OpenAI API key Add your files to a Drive folder named n8n-pinecone-demo in the root of your My Drive If you use a different folder name, you'll need to update the Google Drive triggers to reflect that change Activate the workflow or test it with a manual execution to ingest the documents Chat with your docs! Ideas for customizing this workflow Customize the System Message on the AI Agent node to your use case to indicate what kind of knowledge is stored in Pinecone Assistant Change the top_k value of results returned from Assistant by adding "and should set a top_k of 3" to the System Message to help manage token consumption Configure the Context Window Length in the Conversation Memory node Swap out the Conversation Memory node for one that is more persistent Make the chat node publicly available or create your own chat interface that calls the chat webhook URL. Need help? You can find help by asking in the Pinecone Discord community, asking on the Pinecone Forum, or filing an issue on this repo.

A fully autonomous, HTX Spot Market AI Agent (Huobi AI Agent) built using GPT-4o and Telegram. This workflow is the primary interface, orchestrating all internal reasoning, trading logic, and output formatting. ⚙️ Core Features 🧠 LLM-Powered Intelligence: Built on GPT-4o with advanced reasoning ⏱️ Multi-Timeframe Support: 15m, 1h, 4h, and 1d indicator logic 🧩 Self-Contained Multi-Agent Workflow: No external subflows required 🧮 Real-Time HTX Market Data: Live spot price, volume, 24h stats, and order book 📲 Telegram Bot Integration: Interact via chat or schedule 🔄 Autonomous Runs: Support for webhook, schedule, or Telegram triggers 📥 Input Examples | User Input | Agent Action | | --------------- | --------------------------------------------- | | btc | Returns 15m + 1h analysis for BTC | | eth 4h | Returns 4-hour swing data for ETH | | bnbusdt today | Full day snapshot with technicals + 24h stats | 🖥️ Telegram Output Sample 📊 BTC/USDT Market Summary 💰 Price: $62,400 📉 24h Stats: High $63,020 | Low $60,780 | Volume: 89,000 BTC 📈 1h Indicators: • RSI: 68.1 → Overbought • MACD: Bearish crossover • BB: Tight squeeze forming • ADX: 26.5 → Strengthening trend 📉 Support: $60,200 📈 Resistance: $63,800 🛠️ Setup Instructions Create your Telegram Bot using @BotFather Add Bot Token in n8n Telegram credentials Add your GPT-4o or OpenAI-compatible key under HTTP credentials in n8n (Optional) Add your HTX API credentials if expanding to authenticated endpoints Deploy this main workflow using: ✅ Webhook (HTTP Request Trigger) ✅ Telegram messages ✅ Cron / Scheduled automation 🎥 Live Demo 🧠 Internal Architecture | Component | Role | | ------------------ | -------------------------------------------------------- | | 🔄 Telegram Trigger | Entry point for external or manual signal | | 🧠 GPT-4o | Symbol + timeframe extraction + strategy generation | | 📊 Data Collector | Internal tools fetch price, indicators, order book, etc. | | 🧮 Reasoning Layer | Merges everything into a trading signal summary | | 💬 Telegram Output | Sends formatted HTML report via Telegram | 📌 Use Case Examples | Scenario | Outcome | | -------------------------------------- | ------------------------------------------------------- | | Auto-run every 4 hours | Sends new HTX signal summary to Telegram | | Human requests “eth 1h” | Bot replies with real-time 1h chart-based summary | | System-wide trigger from another agent | Invokes webhook and returns response to parent workflow | 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding permitted. 🔗 For support: Don Jayamaha – LinkedIn

Transcript → AI Analysis → Formatted Doc This workflow automatically converts Fathom meeting transcripts into beautifully formatted Google Docs with AI-generated summaries, key points, decisions, and action items. Good to know Works fully with Fathom free account Webhook responds immediately to prevent Fathom timeout and duplicate triggers Validates transcript quality (3+ conversation turns) before AI processing to save costs Uses Google Gemini API (generous free tier and rate limits, typically enough to avoid paying for API requests, but check latest pricing at Google AI Pricing) Creates temporary HTML file that's auto-deleted after conversion Who's it for Individuals or teams using Fathom for meetings who want more control and flexibility with their AI meeting analysis and storage independently of Fathom, as well as automatic, formatted documentation without manual note-taking. Perfect for recurring syncs, client meetings, or interview debriefs. How it works Fathom webhook triggers when meeting ends and sends transcript data Validates transcript has meaningful conversation (3+ turns) Google Gemini AI analyzes transcript and generates structured summary (key points, decisions, actions, next steps) Creates formatted HTML with proper styling Uploads to Google Drive and converts to native Google Doc Reduces page margins for readability and deletes temporary HTML file Requirements Fathom account with API webhook access (available on free tier) Google Drive account (OAuth2) Google Docs account (OAuth2) Google Gemini API key (Get free key here) How to set up Add credentials: Google Drive OAuth2, Google Docs OAuth2, Google Gemini API Copy the webhook URL from the Get Fathom Meeting webhook node (Test URL first, change to Production URL when ready) In Fathom: Settings → API Access → Add → Add webhook URL and select all events including "Transcript" Test with a short meeting, verify Google Doc appears in Drive Activate workflow Customizing this workflow Change save location: Edit "Upload File as HTML" node → update "Parent Folder" Modify AI output: Edit "AI Meeting Analysis" node → customize the prompt to add/remove sections (e.g., risks, follow-ups, sentiment, etc) Adjust document margins: Edit "Reduce Page Margins" node → change margin pixel values Add notifications: E.g. add Slack/Email node after "Convert to Google Doc" to notify team when summary is ready Quick Troubleshooting "Transcript Present?" fails: Fathom must send transcript_merged with 3+ conversation turns (i.e. only send to Gemini for analysis if there's a genuine transcript) HTML as plain text: Check "Convert to Google Doc" uses POST to /copy endpoint 401/403 errors: Re-authorize Google credentials Inadequate meeting notes: Edit prompts in "AI Meeting Analysis" node Sample File and Storage Output Google Doc meeting notes - sample Google Drive sample folder output: