by Sean Lon
Team Wellness - AI Burnout Detector Agent devex github π― Demo sample report github action code alternative How it works π― Overview A comprehensive n8n workflow that analyzes developer workload patterns from GitHub repositories to detect potential software engineering team burnout risks and provide actionable team wellness recommendations. This workflow automatically monitors team activity patterns, analyzes them using AI, and provides professional wellness reports with actionable recommendations which will automate GitHub issue creation and do email notifications for critical alerts. β¨ Features Automated Data Collection**: Fetches commits, pull requests, and workflow data from GitHub Pattern Analysis**: Identifies late-night work, weekend activity, and workload distribution AI-Powered Analysis**: Uses Groq's LLM for professional burnout risk assessment Automated Actions**: Creates GitHub issues and sends email alerts based on criticality Professional Guardrails**: Ensures objective, evidence-based analysis with privacy protection Scheduled Monitoring**: Weekly automated wellness checks ποΈ Architecture 1. Data Collection Layer GitHub Commits API**: Fetches commit history and timing data GitHub Pull Requests API**: Analyzes collaboration patterns GitHub Workflows API**: Monitors CI/CD pipeline health 2. Pattern Analysis Engine Work Pattern Signals**: Late-night commits, weekend activity Developer Activity**: Individual contribution analysis Workflow Health**: Pipeline success/failure rates Collaboration Metrics**: PR review patterns and merge frequency 3. AI Analysis Layer Professional Guardrails**: Objective, evidence-based assessments Risk Assessment**: Burnout risk classification (Low/Medium/High) Health Scoring**: Team wellness score (0-100) Recommendation Engine**: Actionable suggestions for improvement π Sample Output π Team Health Report π Summary Overall, the team is maintaining a healthy delivery pace, but there are emerging signs of workload imbalance due to increased after-hours activity. π’ Health Score Value:** 68 / 100 Confidence:** 87% Limitations:** Based solely on commit and PR activity; meeting load and non-code tasks not captured. π Observed Patterns β° After-hours activity 29% of commits occurred between 10pmβ1am (baseline: 12%). Confidence: 0.90 β οΈ Systemic Risks Sustained after-hours work may indicate creeping burnout risk. Evidence: 3 consecutive weeks of elevated late-night commits. Confidence: 0.85 β Recommendations π Facilitate a team discussion on workload distribution and sprint commitments. (Priority: Medium) π Introduce automated nudges discouraging late-night commits. (Priority: Low) π οΈ Rotate PR review responsibilities or adopt lightweight review guidelines. (Priority: High) π Quick Start Prerequisites n8n instance (cloud or self-hosted) GitHub repository with API access Groq API key Gmail account (optional, for email notifications) Setup Instructions Import Workflow Import the workflow JSON file into your n8n instance Configure Credentials GitHub API: Create a personal access token with repo access Groq API: Get your API key from Groq Console Gmail OAuth2: Set up OAuth2 credentials for email notifications Update Configuration { "repoowner": "your-github-username", "reponame": "your-repository-name", "period": 7, "emailreport": "your-email@company.com" } Test Workflow Run the workflow manually to verify all connections Check that data is being fetched correctly Verify AI analysis is working Schedule Automation Enable the schedule trigger for weekly reports Set up monitoring for critical alerts π§ Configuration Configuration Node Settings repoowner: GitHub username or organization reponame: Repository name period: Analysis period in days (default: 7) emailreport: Email address for critical alerts AI Model Settings Model**: openai/gpt-oss-120b (Groq) Temperature**: 0.3 (for consistent analysis) Max Tokens**: 2000 Safety Settings**: Professional content filtering π Metrics Analyzed Repository-Level Metrics Total commits count Pull requests opened/closed Workflow runs and success rate Failed workflow percentage Work Pattern Signals Late-night commits (10PM-6AM) Weekend commits (Saturday-Sunday) Work intensity patterns Collaboration bottlenecks Developer-Level Activity Individual commit counts Late-night activity per developer Weekend activity per developer Workload distribution fairness π‘οΈ Privacy & Ethics Professional Guardrails Never makes personal judgments about individual developers Only analyzes observable patterns in code activity data Always provides evidence-based reasoning for assessments Never suggests disciplinary actions or performance reviews Focuses on systemic issues and team-level recommendations Respects privacy and confidentiality of team members Data Protection No personal information is stored or transmitted Analysis is based solely on public repository data and public data All recommendations are constructive and team-focused Confidence scores indicate analysis reliability There is added redaction prompt. Note that LLM is not deterministic and usually, you will need to refine your own prompt to enhance difference level of criticality of privacy you need censored or displayed. In some cases ,you will need the engineer account names to help identify f2f conversation. π Workflow Nodes Core Nodes Schedule Trigger: Weekly automation (configurable) Config: Repository and email configuration Github Get Commits: Fetches commit history Github Get Workflows: Retrieves workflow runs Get Prs: Pulls pull request data Analyze Patterns Developer: JavaScript pattern analysis AI Agent: Groq-powered analysis with guardrails Update Github Issue: Creates wellness tracking issues Send a message in Gmail: Email notifications Data Flow Schedule Trigger β Config β Github APIs β Pattern Analysis β AI Agent β Actions π¨ Alert Levels (Optional and Prompt configurable) Critical Alerts (Health Score < 90) GitHub Issue**: Automatic issue creation with detailed analysis Email Notification**: Immediate alert to team leads Slack Integration**: Critical team notifications Warning Alerts (Health Score 90-95) GitHub Issue**: Tracking issue for monitoring Slack Notification**: Team awareness message Normal Reports (Health Score > 95) Weekly Report**: Comprehensive team health summary Slack Summary**: Positive reinforcement message π§ Troubleshooting Common Issues GitHub API Rate Limits Solution: Use authenticated requests, implement rate limiting Check: API token permissions and repository access AI Analysis Failures Solution: Verify Groq API key, check model availability Check: Input data format and prompt structure Email Notifications Not Sending Solution: Verify Gmail OAuth2 setup, check email permissions Check: SMTP settings and authentication Workflow Execution Errors Solution: Check node connections, verify data flow Check: Error logs and execution history π€ Contributing Development Setup Fork the repository link above demo part Create a feature branch Make your changes Test thoroughly Submit a pull request Testing Test with different repository types Verify AI analysis accuracy Check alert threshold sensitivity Validate email and GitHub integrations π License This project is licensed under the MIT License π Acknowledgments Groq**: For providing the AI analysis capabilities GitHub**: For the comprehensive API ecosystem n8n**: For the powerful workflow automation platform Community**: For feedback and contributions π Support Getting Help Issues**: Create a GitHub issue for bugs or feature requests Discussions**: Use GitHub Discussions for questions Documentation**: Check the comprehensive setup guides Contact Email**:aiix.space.noreply@gmail.com LinkedIn**: SeanLon β οΈ Important: This tool is designed for team wellness monitoring and should be used responsibly. Always respect team privacy and use the insights constructively to improve team health and productivity.
by JKingma
π PDF-to-Order Automation for Magento2 (Adobe commerce / open source) Description This n8n template demonstrates how to automatically process PDF purchase orders received via email and convert them into sales orders in Adobe Commerce (Magento 2) using Company Credit as the payment method. This is especially useful for B2B companies receiving structured orders from clients by email. Use cases include: Automating incoming B2B orders Reducing manual entry for sales teams Ensuring fast order creation in Adobe Commerce Reliable error handling and customer validation Good to know This workflow is tailored for Adobe Commerce, using the Company Credit payment method. It requires that the customer already has an account in Adobe Commerce and is authorized to use Company Credit. The same flow can be easily adapted for other payment methods (e.g. Purchase Order, Bank Transfer). No third-party services are required aside from n8n and access to Adobe Commerce with API credentials. How it works Trigger β Monitors an email inbox for incoming emails with PDF attachments. Extract PDF β Downloads the attached PDF and parses order data (e.g. SKU, quantity, customer reference). Validate Customer β Checks if the sender matches an existing customer in Adobe Commerce and verifies Company Credit eligibility. Create Order β Generates a new order in Magento using the extracted product data and Company Credit. Handle Errors β Logs issues and can notify a designated channel (email, Slack, etc.) if something goes wrong. Optional Enhancements β Add logging to Airtable, send confirmations to customers, or attach parsed order data to CRM entries. How to use A manual trigger is included as an example, but you can replace it with an IMAP Email Trigger, Gmail Trigger, or Webhook, depending on your setup. Customize the PDF parser node to fit your specific document layout and field structure. Configure Adobe Commerce API credentials in the HTTP nodes (or use environment variables). Optionally connect error steps to Slack, Email, or dashboards for monitoring. Requirements β n8n instance (self-hosted or cloud) β Adobe Commerce (Magento 2) instance with API access and Company Credit enabled β Structured PDF templates used by your customers (Optional) Slack/Email/Airtable for notifications and logs Customising this workflow This workflow can be adapted for: Other payment methods (e.g. Purchase Orders, Online Payment) Magento open source ready. Just use your own payment method Alternate order sources (e.g. uploading PDFs via a portal instead of email) Parsing other document formats (e.g. CSV, Excel) Direct integration into ERP systems
by Jitesh Dugar
AI-Powered Feedback Automation with PDF Reports & Team Notifications Transform customer feedback into actionable insights automatically with AI analysis, professional PDF reports, personalized emails, and real-time team notifications. Table of Contents Overview Features Demo Prerequisites Quick Start Configuration Usage Troubleshooting License Overview AI-Powered Feedback Automation is a complete, production-ready n8n workflow that automatically processes customer feedback submissions with artificial intelligence, generates beautiful branded PDF reports, sends personalized email responses, logs data for analytics, and notifies your team in real-time. What Problem Does This Solve? Manual feedback processing is time-consuming and inconsistent. This workflow eliminates all manual work by: Automatically analyzing** sentiment and extracting key insights using OpenAI Generating professional** PDF reports with custom branding Sending personalized** thank-you emails to customers Logging everything** to Google Sheets for analytics and reporting Notifying your team** instantly via Slack with actionable summaries Perfect For Product Teams** - Collect and analyze user feedback systematically Educational Institutions** - Process student/parent feedback efficiently Customer Support** - Track customer satisfaction and sentiment trends E-commerce** - Manage product reviews and customer suggestions Healthcare** - Collect patient feedback and satisfaction scores Event Management** - Gather attendee feedback post-event Consulting Firms** - Streamline client feedback collection Features AI-Powered Analysis Sentiment Classification** - Automatically categorizes feedback as Positive, Neutral, or Negative Key Highlights Extraction** - Identifies the most important points from customer comments Actionable Recommendations** - AI generates specific suggestions based on feedback Executive Summaries** - Creates concise 2-3 sentence overviews of each submission Professional Report Generation Beautiful PDF Reports** - Branded, professional documents with custom styling Visual Elements** - Star ratings, color-coded sentiment badges, organized sections Responsive Design** - Mobile-friendly and print-optimized layouts 30-Day Hosting** - PDF reports automatically hosted with expiration dates Automated Email Communications Personalized Messages** - Thank-you emails customized with customer name and feedback PDF Attachments** - Direct download links to full feedback reports Sentiment Indicators** - Color-coded visual feedback summaries Professional Templates** - Modern, responsive email designs Data Logging & Analytics Google Sheets Integration** - Automatic logging of all feedback submissions Complete Audit Trail** - Tracks submission IDs, timestamps, and processing status Analytics Ready** - Structured data perfect for dashboards and trend analysis Historical Records** - Permanent storage of all feedback data Team Notifications Slack Integration** - Real-time alerts to team channels Rich Formatting** - Structured messages with highlights and action items Direct Links** - Quick access to full PDF reports from Slack Thread Discussions** - Enable team conversations around feedback Robust Error Handling Email Validation** - Automatically checks and handles invalid email addresses Fallback Mechanisms** - Continues workflow even if email sending fails Data Cleaning** - Sanitizes and normalizes all input data Graceful Degradation** - AI parsing failures handled with intelligent fallbacks Demo Workflow Overview User Submits Feedback β Data Cleaning & Validation β AI Sentiment Analysis (OpenAI) β HTML Report Generation β PDF Conversion β Email Validation ββ¬β Valid β Send Email ββ Invalid β Skip β Log to Google Sheets β Notify Team (Slack) β Webhook Response Sample Input { "name": "Sarah Johnson", "email": "sarah@example.com", "rating": 4, "comments": "Great product! Delivery was a bit slow but customer service was helpful.", "suggestions": "Improve shipping speed and tracking updates." } Sample Output β AI Analysis: "Positive" sentiment with 3 key highlights β PDF Report: Professional 2-page document with branding β Email Sent: Personalized thank-you message delivered β Data Logged: New row added to Google Sheet β Team Notified: Slack message with summary posted β Webhook Response: 200 OK with submission details Prerequisites Required Services & Accounts n8n Instance (v0.220.0 or higher) Self-hosted or n8n Cloud Installation Guide OpenAI Account API key with GPT-3.5-turbo or GPT-4 access Sign Up Google Account (Gmail + Google Sheets) OAuth2 setup for Gmail API OAuth2 setup for Google Sheets API Setup Guide Slack Workspace Admin access to create apps or OAuth Bot token with chat:write and channels:read scopes Create Slack App HTML to PDF API Service GET at: PDFMunk API key required VerifiEmail API GET at: VerfiEmail API key required Quick Start 1. Import Template Option A: Import via URL Copy the workflow JSON URL and paste in n8n: Settings β Import from URL β [Paste URL] Option B: Import via File Download workflow.json In n8n: Workflows β Import from File Select the downloaded JSON file Click "Import" 2. Configure Credentials (5 minutes) Navigate to: Settings β Credentials and add: β OpenAI API - Add API key from OpenAI dashboard β Gmail OAuth2 - Connect and authorize your Gmail account β Google Sheets OAuth2 - Use same Google account β Slack OAuth2 - Install app to workspace and authorize β HTML to PDF API - Add API key from your PDF service β VerifiEmail API - Add API key from VerifiEmail dashboard 3. Create Google Sheet (2 minutes) Create a new Google Sheet named "Feedback Log" with these column headers: Submission ID | Timestamp | Name | Email | Rating | Sentiment | Comments | Suggestions | AI Summary | PDF URL | PDF Available Until | Email Sent 4. Configure Workflow (3 minutes) Open the imported workflow Click "Log Feedback Data" node Select your "Feedback Log" spreadsheet Click "Notify Team" node Select your Slack channel (e.g., #feedback) 5. Test & Activate (5 minutes) Execute the "Webhook" node to get test URL Send test POST request (see test data below) Verify all nodes execute successfully Check email, Google Sheet, and Slack Click "Active" toggle to enable workflow Total Setup Time: ~15-20 minutes Configuration Webhook Configuration The workflow receives feedback via POST webhook: URL Format: https://your-n8n-domain.com/webhook/feedback-submission Expected Payload: { "name": "string (required)", "email": "string (optional, validated)", "rating": "integer 1-5 (required)", "comments": "string (optional)", "suggestions": "string (optional)" } Usage Testing the Workflow Using Postman/Insomnia: Create new POST request URL: https://your-n8n-domain.com/webhook/feedback-submission Headers: Content-Type: application/json Body (raw JSON): { "name": "Test User", "email": "your-email@example.com", "rating": 5, "comments": "This is a test feedback submission. Everything works great!", "suggestions": "Maybe add more features in the future." } Send request Expected response (200 OK): { "success": true, "message": "Thank you for your feedback! We've sent you a detailed report via email.", "data": { "submissionId": "FB-1234567890123-abc123xyz", "name": "Test User", "email": "your-email@example.com", "rating": "5", "sentiment": "Positive", "emailSent": "true", "reportUrl": "https://generated-pdf-url.com/report.pdf", "reportAvailableUntil": "2025-11-10" } } Using cURL: curl -X POST https://your-n8n-domain.com/webhook/feedback-submission \ -H "Content-Type: application/json" \ -d '{ "name": "Sarah Johnson", "email": "sarah@example.com", "rating": 4, "comments": "Great product! Delivery was a bit slow but customer service was helpful.", "suggestions": "Improve shipping speed and tracking updates." }' Monitoring & Maintenance Daily: Check Slack for new feedback notifications Review Google Sheet for any anomalies Weekly: Verify workflow execution success rate Check OpenAI API usage and costs Review sentiment trends in Google Sheet Monthly: Analyze feedback patterns and trends Update AI prompts if needed Check PDF service usage limits Review and optimize workflow performance Best Practices Rate Limiting Monitor for spam submissions Add rate limiting to webhook if needed Use n8n's built-in throttling Data Privacy Ensure GDPR/privacy compliance Add data retention policies Implement data deletion workflow Error Handling Set up error notifications Create error logging workflow Monitor execution failures Performance Keep Google Sheet under 50,000 rows Archive old data quarterly Use database for high volume (1000+/month) Troubleshooting Common Issues Issue 1: Webhook Not Receiving Data Symptoms: Webhook node shows no executions Forms submit but nothing happens Solutions: β Verify workflow is Active (toggle at top right) β Check webhook URL is correct in form β Test webhook with Postman/cURL first β Check n8n logs for errors: Settings β Log Streaming β Verify firewall/network allows incoming webhooks Issue 2: OpenAI Node Fails Symptoms: Error: "API key invalid" Error: "Insufficient credits" Node times out Solutions: β Verify API key is correct and active β Check OpenAI account has sufficient credits β Check API usage limits: platform.openai.com/usage β Increase node timeout in workflow settings β Try with shorter feedback text Issue 3: PDF Not Generating Symptoms: "PDF generation failed" error Empty PDF URL 404 when accessing PDF Solutions: β Verify PDF API key is valid β Check API service status β Verify HTML content is valid (test in browser) β Check API usage limits/quota β Try alternative PDF service Issue 4: Email Not Sending Symptoms: Gmail node shows error Email doesn't arrive "Permission denied" error Solutions: β Re-authenticate Gmail OAuth2 credential β Check email address is valid β Check spam/junk folder β Verify Gmail API is enabled in Google Console β Check daily sending limits not exceeded β Test with different email address Issue 5: Google Sheets Not Updating Symptoms: No new rows added "Spreadsheet not found" error Permission errors Solutions: β Verify spreadsheet ID is correct β Check sheet name matches exactly (case-sensitive) β Verify column headers match exactly β Re-authenticate Google Sheets credential β Check spreadsheet isn't protected/locked β Verify spreadsheet isn't full (limit: 10M cells) Issue 6: Slack Not Posting Symptoms: Slack node fails Message doesn't appear in channel "Channel not found" error Solutions: β Verify bot is invited to channel: /invite @BotName β Check bot has chat:write permission β Re-authenticate Slack credential β Verify channel ID is correct β Check Slack workspace isn't on free plan limits β Test with different channel Debugging Tips Enable Debug Mode Settings β Executions β Save execution progress Watch each node execute step-by-step Check Execution Logs Click on failed node View "Input" and "Output" tabs Check error messages Test Nodes Individually Click "Execute Node" on each node Verify output before proceeding Use Browser Console Open Developer Tools (F12) Check for JavaScript errors Monitor network requests Enable Verbose Logging For self-hosted n8n N8N_LOG_LEVEL=debug npm start π License This template is licensed under the MIT License - see the LICENSE file for details.
by Growth AI
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., "IntΓ©grer 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 Γ©ditorial: Project status field (set to "Actif" for active projects) Last sent - Calendrier Γ©ditorial: 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
by Jitesh Dugar
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: π Exit Interview Capture - Jotform collects resignation details, ratings, feedback, and equipment inventory π€ AI Sentiment Analysis - Advanced AI analyzes exit interviews for retention insights, red flags, and patterns β οΈ Red Flag Detection - Automatically identifies serious issues (harassment, discrimination, ethics) for immediate escalation π€ Manager Intelligence - Flags management issues and provides coaching recommendations π Access Revocation - Schedules automatic system access removal on last working day π¦ Equipment Tracking - Generates personalized equipment return checklist for each employee π Knowledge Transfer - Assesses knowledge transfer risk and creates handover plan π° Retention Analytics - Identifies preventable departures and competitive intelligence π§ Automated Notifications - Sends checklists to employees, action items to managers, IT requests π 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! π Workflow Architecture π Jotform Exit Interview Submission β π§Ύ Parse Offboarding Data β π€ AI Exit Interview Analysis (GPT-4) β ββ Retention analysis (preventability scoring) β ββ Sentiment analysis (tone, emotions) β ββ Manager performance evaluation β ββ Department insights β ββ Compensation benchmarking β ββ Knowledge transfer risk assessment β ββ Competitor intelligence β ββ Red flag detection β ββ Boomerang probability β ββ Action item generation β π Extract AI Analysis (JSON) β π§© Merge Exit Analysis with Data β ββ Calculate days until last day β ββ Build equipment checklist β ββ Assess urgency levels β β οΈ Has Red Flags? ββ TRUE β π¨ Send Red Flag Alert (HR Director/Legal) β β ββ FALSE β π§ Send Manager Action Items β βοΈ Send Employee Checklist β π Send IT Offboarding Request β π 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! πͺβ¨ Questions or customization needs? The workflow includes detailed sticky notes explaining each AI analysis component and routing decision.
by Jitesh Dugar
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: π Digital Inspections - Jotform captures daily vehicle checks with photos, mileage, and comprehensive checklists π€ AI Condition Analysis - Advanced AI Agent evaluates vehicle condition, safety ratings, and maintenance needs β οΈ Smart Prioritization - Automatically flags critical issues (brakes, safety concerns, DOT compliance) π§ Maintenance Routing - Routes issues to appropriate shop teams with detailed work orders π Predictive Maintenance - Tracks mileage thresholds and predicts upcoming service needs βοΈ Automated Notifications - Sends alerts to maintenance teams and confirmation to drivers π Compliance Tracking - Monitors DOT inspections, registrations, and annual certifications π° Cost Management - Estimates repair costs and tracks downtime to optimize fleet budget π 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! π Workflow Architecture π Jotform Daily Inspection β π§Ύ Parse Inspection Data β π Get Vehicle History β ββ Last service dates β ββ Mileage calculations β ββ Compliance deadlines β π€ AI Fleet Analysis (GPT-4) β ββ Condition assessment β ββ Safety rating (0-100) β ββ Critical issue detection β ββ Maintenance recommendations β ββ Cost estimation β ββ DOT compliance check β ββ Work order generation β π Extract & Merge AI Analysis β β‘ Critical Issue Check ββ TRUE β π¨ Critical Alert Email (Maintenance) ββ FALSE β π Routine Report Email (Maintenance) β βοΈ Driver Confirmation Email β ββ Inspection received β ββ Vehicle status β ββ Maintenance scheduled β ββ Safety notices β π Log to Google Sheets ββ Inspection database ββ Audit trail ββ Analytics data π 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! πβ¨ Questions or customization needs? The workflow includes detailed sticky notes explaining each component and decision point.
by Jitesh Dugar
Jotform AI-Powered Loan Application & Pre-Approval Automation System Transform manual loan processing into same-day pre-approvals - achieving 50% faster closings, 90% reduction in manual review time, and automated underwriting decisions with AI-powered financial analysis and instant applicant notifications. What This Workflow Does Revolutionizes mortgage and loan processing with AI-driven financial analysis and automated decision workflows: π Digital Application Capture - Jotform collects complete applicant data, income, employment, and loan details π€ AI Financial Analysis - GPT-4 calculates debt-to-income ratio, loan-to-value ratio, and approval likelihood π³ Automated Credit Assessment - Instant credit score evaluation and payment history analysis π Risk Scoring - AI assigns 1-100 risk scores based on multiple financial factors β Intelligent Routing - Automatic pre-approval, conditional approval, or denial based on lending criteria π§ Instant Notifications - Applicants receive approval letters within minutes of submission π Underwriter Alerts - Pre-approved loans automatically route to loan officers with complete analysis π Document Tracking - Required documents list generated based on application specifics π Closing Scheduling - Approved loans trigger closing coordination workflows π Complete Audit Trail - Every application logged with financial metrics and decision rationale Key Features AI Underwriting Analyst: GPT-4 evaluates loan applications across 10+ financial dimensions including debt ratios, risk assessment, and approval recommendations Debt-to-Income Calculation: Automatically calculates DTI ratio and compares against lending standards (43% threshold for qualified mortgages) Loan-to-Value Analysis: Evaluates down payment adequacy and property value against loan amount requested Credit Score Integration: Simulated credit assessment (ready for real credit bureau API integration like Experian, Equifax, TransUnion) Approval Likelihood Scoring: AI predicts approval probability as high/medium/low based on complete financial profile Risk Assessment: 1-100 risk score considers income stability, debt levels, credit history, and employment status Interest Rate Recommendations: AI suggests appropriate rate ranges based on applicant qualifications Conditional Approval Logic: Identifies specific requirements needed for final approval (additional documentation, debt paydown, etc.) Multi-Path Routing: Different workflows for pre-approved (green path), conditional (yellow path), and denied (red path) applications Monthly Payment Estimates: AI calculates estimated mortgage payments including principal, interest, taxes, and insurance Employment Verification Tracking: Flags employment status and stability in approval decision Document Requirements Generator: Custom list of required documents based on applicant situation and loan type Underwriter Dashboard Integration: Pre-approved applications automatically notify underwriters with complete financial summary Applicant Communication: Professional, branded emails for every outcome (pre-approval, conditional, denial) Alternative Options for Denials: Denied applicants receive constructive guidance on improving qualifications Compliance Ready: Decision rationale documented for regulatory compliance and audit requirements Perfect For Mortgage Lenders: Banks and credit unions processing home loan applications (purchase, refinance, HELOC) Commercial Lenders: Business loan and commercial real estate financing institutions Auto Finance Companies: Car dealerships and auto loan providers needing instant credit decisions Personal Loan Providers: Fintech companies and online lenders offering consumer loans Credit Unions: Member-focused financial institutions streamlining loan approval processes Mortgage Brokers: Independent brokers managing applications for multiple lenders Hard Money Lenders: Alternative lenders with custom underwriting criteria Student Loan Services: Educational financing with income-based qualification What You'll Need Required Integrations Jotform - Loan application form (free tier works, Pro recommended for file uploads) Create your form for free on Jotform using this link: https://www.jotform.com OpenAI API - GPT-4 for AI financial analysis and underwriting decisions (approximately 0.30-0.50 USD per application) Gmail - Automated notifications to applicants and underwriters Google Sheets - Loan application database and pipeline tracking Optional Integrations (Recommended for Production) Credit Bureau APIs - Experian, Equifax, or TransUnion for real credit pulls Document Management - DocuSign, HelloSign for e-signatures and document collection Property Appraisal APIs - Automated valuation models for property verification Calendar Integration - Calendly or Google Calendar for closing date scheduling CRM Systems - Salesforce, HubSpot for lead management and follow-up Loan Origination Software (LOS) - Encompass, Calyx, BytePro integration Quick Start Import Template - Copy JSON and import into n8n Add OpenAI Credentials - Set up OpenAI API key (GPT-4 required for accurate underwriting) Create Jotform Loan Application: Full Name (q3_fullName) Email (q4_email) Phone (q5_phone) Social Security Number (q6_ssn) - encrypted field Monthly Income (q7_monthlyIncome) - number field Monthly Debts (q8_monthlyDebts) - number field (credit cards, car loans, student loans) Loan Amount Requested (q9_loanAmount) - number field Down Payment (q10_downPayment) - number field Property Value (q11_propertyValue) - number field Employment Status (q12_employmentStatus) - dropdown (Full-time, Part-time, Self-employed, Retired) Additional fields: Date of Birth, Address, Employer Name, Years at Job, Property Address Configure Gmail - Add Gmail OAuth2 credentials (same for all 4 Gmail nodes) Setup Google Sheets: Create spreadsheet with "Loan_Applications" sheet Replace YOUR_GOOGLE_SHEET_ID in workflow 16 columns auto-populate: timestamp, applicationId, applicantName, email, phone, loanAmount, downPayment, monthlyIncome, monthlyDebts, creditScore, dtiRatio, ltvRatio, riskScore, approvalStatus, monthlyPayment, interestRate Customize Approval Criteria (Optional): Edit "Check Approval Status" node Adjust credit score minimum (default: 680) Modify DTI threshold (default: 43%) Set LTV requirements Configure Credit Integration: Replace "Simulate Credit Check" node with real credit bureau API Or keep simulation for testing/demo purposes Brand Email Templates: Update company name, logo, contact information Customize approval letter formatting Add compliance disclosures as required Set Underwriter Email: Update underwriter contact in "Notify Underwriter" node Add CC recipients for loan ops team Test Workflow - Submit test applications with different scenarios: High income, low debt (should pre-approve) Moderate income, high debt (should conditional) Low income, excessive debt (should deny) Compliance Review - Have legal/compliance team review automated decision logic Go Live - Deploy form on website, share with loan officers, integrate with marketing Customization Options Loan Type Variations: Customize for conventional, FHA, VA, USDA, jumbo, or commercial loans Custom Underwriting Rules: Adjust DTI limits, credit minimums, LTV requirements per loan product Manual Review Triggers: Flag edge cases for manual underwriter review before automation Document Upload Integration: Add Jotform file upload fields for paystubs, tax returns, bank statements Income Verification APIs: Integrate with Plaid, Finicity, or Argyle for automated income verification Employment Verification: Connect to The Work Number or other employment databases Property Appraisal Automation: Integrate AVMs (Automated Valuation Models) from CoreLogic, HouseCanary Co-Borrower Support: Add fields and logic for joint applications with multiple income sources Business Loan Customization: Modify for business financials (revenue, EBITDA, business credit scores) Rate Shopping: Integrate rate tables to provide real-time interest rate quotes Pre-Qualification vs Pre-Approval: Create lighter version for soft credit pull pre-qualification Conditional Approval Workflows: Automated follow-up sequences for document collection Closing Coordination: Integrate with title companies, attorneys, closing services Regulatory Compliance: Add TRID timeline tracking, adverse action notices, HMDA reporting Multi-Language Support: Translate forms and emails for Spanish, Chinese, other languages Expected Results Same-day pre-approval - Applications processed in minutes vs 3-5 days manual review 50% faster closings - Streamlined process reduces time from application to closing 90% reduction in manual review time - AI handles initial underwriting, humans only review exceptions 95% applicant satisfaction - Instant decisions and clear communication improve experience 75% reduction in incomplete applications - Required fields force complete submission 60% fewer applicant calls - Automated status updates reduce "where's my application" inquiries 100% application tracking - Complete audit trail from submission to final decision 40% increase in loan officer productivity - Focus on high-value activities, not data entry 80% decrease in approval errors - Consistent AI analysis eliminates human calculation mistakes 30% improvement in compliance - Automated documentation and decision rationale for audits Pro Tips Test with Multiple Scenarios: Submit applications with various income/debt combinations to validate routing logic works correctly Adjust DTI Thresholds for Loan Type: Conventional mortgages: 43% max. FHA loans: 50% max. Auto loans: 35-40% max. Personal loans: 40-45% max. Credit Score Tiers Matter: Build rate sheets with score tiers (740+: prime, 680-739: near-prime, 620-679: subprime, below 620: denied or hard money) Income Verification Priorities: W-2 employees (easy), self-employed (complex), commission/bonus heavy (average 2 years), rental income (75% counts), gig economy (difficult) Document Checklist Customization: Vary required docs by loan type, amount, and risk profile to avoid over-documentation for low-risk loans Conditional Approval vs Outright Denial: When in doubt, use conditional - gives applicants path to approval and keeps them in pipeline Adverse Action Notices: For denials, include specific reasons (per FCRA requirements) and instructions for disputing credit report errors Pre-Qualification vs Pre-Approval: Pre-qual uses soft credit pull (no impact on score), pre-approval uses hard pull (official decision) Co-Borrower Logic: When DTI is high, automatically suggest co-borrower as option to strengthen application Rate Lock Automation: Pre-approved applications should include rate lock expiration date (typically 30-60 days) Property Appraisal Triggers: Auto-order appraisals for pre-approved mortgage applications to keep process moving Underwriter Dashboard: Build Google Sheets dashboard with filters for underwriters to sort by approval status, loan amount, date Compliance Monitoring: Regular audits of AI decisions to ensure no discriminatory patterns (disparate impact analysis) Customer Service Integration: Link application IDs to support tickets so agents can quickly pull up loan status Marketing Attribution: Track lead sources in form to measure which marketing channels produce best-quality applicants Learning Resources This workflow demonstrates advanced automation: AI Agents for Financial Analysis: Multi-dimensional loan qualification using BANT-style underwriting criteria Complex Conditional Logic: Multi-path routing with nested IF conditions for approval/conditional/denial workflows Financial Calculations: Automated DTI, LTV, DSCR, and payment estimation algorithms Risk Scoring Models: Comprehensive risk assessment combining credit, income, debt, and employment factors Decision Documentation: Complete audit trail with AI reasoning for regulatory compliance Email Customization: Dynamic content generation based on approval outcomes and applicant situations Data Pipeline Design: Structured data flow from application through analysis to decision and notification Simulation vs Production: Credit check node designed for easy swap from simulation to real API integration Parallel Processing: Simultaneous logging and notification workflows for efficiency Workflow Orchestration: Coordination of multiple decision points and communication touchpoints Questions or customization? The workflow includes detailed sticky notes explaining each analysis component and decision logic. Template Compatibility β n8n version 1.0+ β Works with n8n Cloud and Self-Hosted β Production-ready for financial institutions β Fully customizable for any loan type Compliance Note: This template is designed for demonstration and automation purposes. Always consult with legal counsel to ensure compliance with TILA, RESPA, ECOA, FCRA, and applicable state lending regulations before deploying in production.
by vinci-king-01
Software Vulnerability Tracker with Pushover and Notion β οΈ COMMUNITY TEMPLATE DISCLAIMER: This is a community-contributed template that uses ScrapeGraphAI (a community node). Please ensure you have the ScrapeGraphAI community node installed in your n8n instance before using this template. This workflow automatically scans multiple patent databases on a weekly schedule, filters new filings relevant to selected technology domains, saves the findings to Notion, and pushes instant alerts to your mobile device via Pushover. It is ideal for R&D teams and patent attorneys who need up-to-date insights on emerging technology trends and competitor activity. Pre-conditions/Requirements Prerequisites An n8n instance (self-hosted or n8n cloud) ScrapeGraphAI community node installed Active Notion account with an integration created Pushover account (user key & application token) List of technology keywords / CPC codes to monitor Required Credentials ScrapeGraphAI API Key** β Enables web scraping of patent portals Notion Credential** β Internal Integration Token with database write access Pushover Credential** β App Token + User Key for push notifications Additional Setup Requirements | Service | Needed Item | Where to obtain | |---------|-------------|-----------------| | USPTO, EPO, WIPO, etc. | Public URLs for search endpoints | Free/public | | Notion | Database with properties: Title, Abstract, URL, Date | Create in Notion | | Keyword List | Text file or environment variable PATENT_KEYWORDS | Define yourself | How it works This workflow automatically scans multiple patent databases on a weekly schedule, filters new filings relevant to selected technology domains, saves the findings to Notion, and pushes instant alerts to your mobile device via Pushover. It is ideal for R&D teams and patent attorneys who need up-to-date insights on emerging technology trends and competitor activity. Key Steps: Schedule Trigger**: Fires every week (default Monday 08:00 UTC). Code (Prepare Queries)**: Builds search URLs for each keyword and data source. SplitInBatches**: Processes one query at a time to respect rate limits. ScrapeGraphAI**: Scrapes patent titles, abstracts, links, and publication dates. Code (Normalize & Deduplicate)**: Cleans data, converts dates, and removes already-logged patents. IF Node**: Checks whether new patents were found. Notion Node**: Inserts new patent entries into the specified database. Pushover Node**: Sends a concise alert summarizing the new filings. Sticky Notes**: Document configuration tips inside the workflow. Set up steps Setup Time: 10-15 minutes Install ScrapeGraphAI: In n8n, go to βSettings β Community Nodesβ and install @n8n-nodes/scrapegraphai. Add Credentials: ScrapeGraphAI: paste your API key. Notion: add the internal integration token and select your database. Pushover: provide your App Token and User Key. Configure Keywords: Open the first Code node and edit the keywords array (e.g., ["quantum computing", "Li-ion battery", "5G antenna"]). Point to Data Sources: In the same Code node, adjust the sources array if you want to add/remove patent portals. Set Notion Database Mapping: In the Notion node, map properties (Name, Abstract, Link, Date) to incoming JSON fields. Adjust Schedule (optional): Double-click the Schedule Trigger and change the CRON expression to your preferred interval. Test Run: Execute the workflow manually. Confirm that the Notion page is populated and a Pushover notification arrives. Activate: Switch the workflow to βActiveβ to enable automatic weekly execution. Node Descriptions Core Workflow Nodes: Schedule Trigger** β Defines the weekly execution time. Code (Build Search URLs)** β Dynamically constructs patent search URLs. SplitInBatches** β Sequentially feeds each query to the scraper. ScrapeGraphAI** β Extracts patent metadata from HTML pages. Code (Normalize Data)** β Formats dates, adds UUIDs, and checks for duplicates. IF** β Determines whether new patents exist before proceeding. Notion** β Writes new patent records to your Notion database. Pushover** β Sends real-time mobile/desktop notifications. Data Flow: Schedule Trigger β Code (Build Search URLs) β SplitInBatches β ScrapeGraphAI β Code (Normalize Data) β IF β Notion & Pushover Customization Examples Change Notification Message // Inside the Pushover node "Message" field return { message: π ${items[0].json.count} new patent(s) detected in ${new Date().toDateString()}, title: 'π Patent Alert', url: items[0].json.firstPatentUrl, url_title: 'Open first patent' }; Add Slack Notification Instead of Pushover // Replace the Pushover node with a Slack node { text: ${$json.count} new patents published:\n${$json.list.join('\n')}, channel: '#patent-updates' } Data Output Format The workflow outputs structured JSON data: { "title": "Quantum Computing Device", "abstract": "A novel qubit architecture that ...", "url": "https://patents.example.com/US20240012345A1", "publicationDate": "2024-06-01", "source": "USPTO", "keywordsMatched": ["quantum computing"] } Troubleshooting Common Issues No data returned β Verify that search URLs are still valid and the ScrapeGraphAI selector matches the current page structure. Duplicate entries in Notion β Ensure the βNormalize Dataβ code correctly checks for existing URLs or IDs before insert. Performance Tips Limit the number of keywords or schedule the workflow during off-peak hours to reduce API throttling. Enable caching inside ScrapeGraphAI (if available) to minimize repeated requests. Pro Tips: Use environment variables (e.g., {{ $env.PATENT_KEYWORDS }}) to manage keyword lists without editing nodes. Chain an additional βHTTP Request β ML Modelβ step to auto-classify patents by CPC codes. Create a Notion view filtered by publicationDate is within past 30 days for quick scanning.
by Hemanth Arety
Handle WhatsApp customer inquiries with AI and intent routing (Whatsapp Chatbot) An intelligent, fully customizable WhatsApp customer support chatbot template that works for ANY business - whether you sell fashion, electronics, food, furniture, cosmetics, or anything else. This workflow combines pre-built responses for common queries with AI for complex questions, creating a cost-effective 24/7 customer support solution that adapts to your specific products and services. Who it's for This universal template works for ANY business type: E-commerce stores** (fashion, electronics, home goods, beauty, etc.) Local retail shops** (boutiques, grocery stores, bookshops, etc.) Service businesses** (salons, repair services, consultancies, etc.) Restaurants & cafes** (food delivery, reservations, menu inquiries) Any business** using WhatsApp Business API for customer communication What it does This is a UNIVERSAL template - it works for ANY business by simply updating the product categories, company information, and response templates. No coding knowledge required for basic customization! The workflow automates WhatsApp customer support through intelligent routing and AI assistance: Receives WhatsApp messages via WhatsApp Business API webhook trigger Parses message data extracting user info, chat ID, and message text Classifies intent using pattern matching to determine what the customer wants (product inquiry, contact info, support, greeting, etc.) Routes intelligently to the most appropriate response handler: Product inquiries β Pre-built catalog responses with pricing and links Contact information β Static company details (address, phone, hours) Complex queries β AI agent with full company context Maintains conversation context using memory to remember previous messages Sends formatted responses back to the customer via WhatsApp with markdown formatting The hybrid approach (pre-built responses + AI) balances speed, cost, and intelligence - common questions get instant answers while complex queries receive personalized AI assistance. How to set up Requirements You'll need: WhatsApp Business API** access (via Twilio, 360Dialog, Meta Cloud API, or other providers) Google Gemini API key** (for AI responses) - Get API key Google Docs** (optional - for product catalog reference) n8n instance** with WhatsApp nodes installed Setup Steps Configure WhatsApp Business API Sign up with a WhatsApp Business API provider (Twilio, 360Dialog, or Meta) Get your API credentials (phone number ID, access token, webhook verify token) Add credentials to n8n's WhatsApp node Copy the webhook URL from n8n and configure it in your provider's dashboard Customize Company Information Open the "Build AI System Prompt" node Replace all placeholder text with your actual company details: Company name Address and phone numbers Email and website Product categories and brands Policies (COD, warranty, returns, delivery) Store hours Configure Product Responses Edit the "Generate Product Response" node Replace the sample products with your actual catalog: Product names and specifications Prices (update currency if not using INR) Product URLs from your website Add/remove product categories as needed Update Contact Details Edit the "Generate Contact Info Response" node Add your complete contact information Update store hours and addresses Set Up AI Credentials Add your Google Gemini API key to the credential manager (Optional) Connect Google Docs if you want to use a product catalog document Activate and Test Activate the workflow in n8n Send test messages to your WhatsApp Business number Test different intents: greetings, product questions, contact requests Verify responses are accurate and timely WhatsApp Business API Providers Option 1: Meta Cloud API (Official, free for moderate usage) Sign up at: https://developers.facebook.com/ Requires Facebook Business account Best for: Small to medium businesses Option 2: Twilio (Reliable, paid service) Sign up at: https://www.twilio.com/whatsapp Pay-per-message pricing Best for: Businesses needing high reliability Option 3: 360Dialog (WhatsApp-focused) Sign up at: https://www.360dialog.com/ Competitive pricing Best for: WhatsApp-heavy businesses Option 4: MessageBird, Vonage, others Various pricing and features Research and compare based on your needs How it works Intent Classification System The workflow uses keyword pattern matching to classify user intent into these categories: Priority 1: Contact Information (highest priority) Triggers: "where is store", "address", "contact", "phone number" Response: Static contact details Why first: Users asking for contact info need immediate, accurate answers Priority 2: Greetings Triggers: "hi", "hello", "hey", "good morning" Response: Friendly welcome with menu options Helps: Sets a positive tone for the conversation Priority 3: Product Inquiries Triggers: Category keywords (monitor, processor, GPU, RAM, etc.) Response: Pre-built catalog with products, prices, and links Categories: Customizable based on your products Priority 4: AI Fallback Triggers: Everything else (comparisons, complex questions, multi-step queries) Response: Google Gemini AI with company knowledge Features: Conversation memory, personalized recommendations Response Architecture Pre-Built Responses (Fast & Cost-Effective) Instant answers (no API calls) Handles 70-80% of queries Consistent, accurate information No ongoing costs Used for: Product lists, contact info, FAQs AI Agent (Intelligent & Flexible) Handles complex questions Maintains conversation context Provides personalized recommendations Adapts to different query styles Used for: Comparisons, custom builds, technical questions Conversation Memory The workflow uses buffer window memory to remember recent conversation: Stores last 10 messages per user Enables multi-turn conversations AI can reference previous questions Creates more natural interactions Memory is user-specific (isolated by user ID) Message Flow Example User: "Hi" β Intent: greeting β Response: Welcome message with menu User: "Show me monitors" β Intent: product_inquiry (monitors) β Response: Pre-built list of monitors with prices User: "Which one is best for gaming?" β Intent: general_inquiry (complex) β Response: AI analyzes previous context (monitors) and recommends gaming-focused option User: "What's your address?" β Intent: contact_info β Response: Complete contact details How to customize the workflow STEP 1: Customize Product Categories The workflow comes with example categories for multiple business types. Replace them with YOUR categories: For a Fashion Store: const categories = [ { pattern: /(shirt|tshirt|top)/i, category: 'tops' }, { pattern: /(jeans|pants|trousers)/i, category: 'bottoms' }, { pattern: /(dress|gown|kurti)/i, category: 'dresses' }, { pattern: /(shoe|footwear|heels)/i, category: 'shoes' }, ]; For a Grocery Store: const categories = [ { pattern: /(vegetable|veggies)/i, category: 'vegetables' }, { pattern: /(fruit|fruits)/i, category: 'fruits' }, { pattern: /(dairy|milk|cheese)/i, category: 'dairy' }, { pattern: /(snack|chips|biscuit)/i, category: 'snacks' }, ]; For a Beauty/Cosmetics Store: const categories = [ { pattern: /(skincare|cream|serum)/i, category: 'skincare' }, { pattern: /(makeup|lipstick|foundation)/i, category: 'makeup' }, { pattern: /(perfume|fragrance)/i, category: 'perfumes' }, { pattern: /(hair|shampoo|conditioner)/i, category: 'haircare' }, ]; For a Home Furniture Store: const categories = [ { pattern: /(sofa|couch)/i, category: 'sofas' }, { pattern: /(bed|mattress)/i, category: 'bedroom' }, { pattern: /(table|desk|dining)/i, category: 'tables' }, { pattern: /(chair|seating)/i, category: 'chairs' }, ]; For a Restaurant: const categories = [ { pattern: /(pizza|italian)/i, category: 'italian' }, { pattern: /(burger|sandwich)/i, category: 'fast_food' }, { pattern: /(biryani|curry|indian)/i, category: 'indian' }, { pattern: /(dessert|sweet|ice cream)/i, category: 'desserts' }, ]; STEP 2: Customize Product Responses Update the "Generate Product Response" node with YOUR actual products: Example for Fashion Store: if (category === 'tops') { response = Hi ${userName}! Check out our TOPS collection:\\n\\n; response += π Cotton Casual T-Shirt\\n π° βΉ499\\n π¨ 5 colors available\\n π yourstore.com/tshirts\\n\\n; response += π Formal Shirt\\n π° βΉ899\\n π Buy 2 Get 20% OFF\\n π yourstore.com/shirts\\n\\n; } Example for Grocery Store: if (category === 'vegetables') { response = Fresh VEGETABLES available, ${userName}:\\n\\n; response += π₯ Fresh Carrots (1kg)\\n π° βΉ40\\n π± Organic\\n\\n; response += π Tomatoes (1kg)\\n π° βΉ30\\n β Farm Fresh\\n\\n; } Example for Restaurant: if (category === 'italian') { response = Delicious ITALIAN dishes, ${userName}:\\n\\n; response += π Margherita Pizza\\n π° βΉ299\\n π¨βπ³ Chef's Special\\n\\n; response += π Creamy Alfredo Pasta\\n π° βΉ349\\n π₯ Bestseller\\n\\n; } STEP 3: Update Company Information Edit the "Build AI System Prompt" node: For a Boutique: const systemPrompt = `You are a customer service assistant for Elegant Threads Boutique. COMPANY INFORMATION: Business: Women's Designer Clothing Boutique Products: Ethnic wear, western wear, accessories Price Range: βΉ1,500 - βΉ15,000 Speciality: Custom tailoring available Store Address: Shop 12, Fashion Street, Mumbai Phone: +91 98XXXXXXXX Delivery: Pan-Mumbai, 2-3 days Returns: 7-day no-questions-asked return policy `; For a Tech Store: const systemPrompt = `You are customer support for TechHub Electronics. COMPANY INFORMATION: Business: Consumer Electronics Retailer Products: Smartphones, laptops, accessories, home appliances Price Range: βΉ500 - βΉ2,00,000 Speciality: Same-day delivery in Delhi NCR Warranty: Extended warranty on all electronics Store: Connaught Place, New Delhi Phone: +91 11-XXXXXXXX `; For a Bakery: const systemPrompt = `You are the assistant for Sweet Delights Bakery. COMPANY INFORMATION: Business: Fresh Baked Goods & Custom Cakes Products: Cakes, pastries, cookies, bread Price Range: βΉ50 - βΉ3,000 Speciality: Custom cakes for all occasions (24hrs notice) Store: Baker Street, Bangalore Phone: +91 80-XXXXXXXX Delivery: Free above βΉ500 within 5km `; Additional Customization Options Change AI Model Replace Google Gemini with other LLM providers: OpenAI GPT-4**: Best for nuanced understanding Anthropic Claude**: Strong at following instructions Llama** (self-hosted): Cost-effective for high volume Simply swap the "Google Gemini Chat Model" node with your preferred model. Add More Intents Extend the intent classification in the "Classify User Intent" node: // Add order tracking if (/track.order|order.status|where.*order/i.test(text)) { intent = 'order_tracking'; } // Add complaint handling if (/complaint|unhappy|problem|issue|refund/i.test(text)) { intent = 'complaint'; } // Add shipping questions if (/shipping|delivery|courier|when.*arrive/i.test(text)) { intent = 'shipping_inquiry'; } Then add corresponding response nodes in the routing switch. Integrate with CRM Connect to HubSpot: Add HubSpot node after intent classification Log every conversation as a ticket Create contacts automatically Track customer journey Connect to Salesforce: Use Salesforce node to create leads Update opportunity stages based on intent Log interactions in Activity History Connect to Airtable: Store conversations in Airtable database Analyze common questions Build knowledge base from real conversations Add Multi-Language Support Method 1: Google Translate API Detect message language Translate to English for processing Translate response back to user's language Method 2: Multilingual AI Add language preference to AI prompt Train AI on multilingual responses Support major languages natively Rich Media Responses Send images: return [{ chatId: chatId, image: 'https://yoursite.com/product.jpg', caption: 'Check out this product!' }]; Send documents: Product catalogs (PDF) Warranty cards Invoice copies Installation guides Send location pins: Store locations Delivery tracking Service centers Human Handoff Logic Add escalation for complex issues: // Check if AI can't help if (complexityScore > 8 || sentiment === 'angry') { // Notify human agent // Transfer conversation // Set status: 'awaiting_agent' } Integrate with: Intercom for live chat handoff Slack for agent notifications Zendesk for ticket creation Connect to Inventory Real-time stock checking: Query your database for availability Show "In Stock" / "Out of Stock" status Suggest alternatives for unavailable products Notify customers when items are restocked Dynamic pricing: Pull current prices from database Apply promotional discounts automatically Show time-sensitive offers Add Analytics Track metrics: Messages per day/week/month Most common intents AI usage vs. pre-built responses Average response time Customer satisfaction scores Integration options: Google Analytics for website tracking Mixpanel for event tracking Custom dashboard in Grafana Google Sheets for simple logging Business Hours Management Add business hours logic: const now = new Date(); const hour = now.getHours(); const isBusinessHours = (hour >= 10 && hour < 20); // 10 AM - 8 PM if (!isBusinessHours) { return [{ response: "We're currently closed. Our hours are 10 AM - 8 PM. We'll respond when we open!" }]; } A/B Testing Responses Test different response styles: Formal vs. casual tone With/without emojis Short vs. detailed answers Different CTAs Track which versions lead to more sales/conversions. Tips for best results 1. Start Simple Begin with 3-5 main intents Add more as you see common patterns Don't over-complicate the initial setup 2. Monitor and Iterate Review conversations weekly Identify missed intents Refine pattern matching Update product information regularly 3. Balance Pre-Built vs. AI Use pre-built for: FAQs, product lists, contact info (fast, cheap) Use AI for: Comparisons, complex queries, personalization (slower, costs money) Aim for 70-80% pre-built, 20-30% AI 4. Optimize Response Times Pre-built responses are instant AI responses take 2-5 seconds Set user expectations ("Let me check that for you...") 5. Test Different Scenarios Happy path (normal inquiries) Edge cases (misspellings, slang) Multi-turn conversations Multiple topics in one message 6. Keep Responses Concise WhatsApp users prefer short messages Use formatting (bold, bullets) for readability Break long responses into multiple messages 7. Maintain Brand Voice Customize AI system prompt with your brand personality Use consistent tone across all responses Include brand-appropriate emojis 8. Handle Failures Gracefully Add error handling for API failures Have fallback responses ready Always offer human contact option 9. Respect Privacy Don't store sensitive information Comply with GDPR/local privacy laws Allow users to delete their data 10. Monitor Costs Track Gemini API usage Set spending alerts Optimize prompt length to reduce token usage Common use cases across industries Fashion & Apparel Store Answer size and fit questions Share new collection arrivals Check stock availability by size/color Process exchange requests Share styling tips Electronics & Tech Store Provide product specifications Compare different models Check warranty information Share installation guides Handle technical support queries Grocery & Food Store Check product availability Share daily fresh stock updates Take bulk orders Provide recipe suggestions Handle delivery slot bookings Beauty & Cosmetics Recommend products for skin types Share ingredient information Explain usage instructions Handle shade/color queries Process return for wrong products Home Furniture Store Share dimensions and specifications Check delivery timelines Provide assembly instructions Schedule store visits Custom furniture inquiries Restaurant & Cafe Share menu and prices Take table reservations Handle takeaway orders Answer dietary restriction questions Share daily specials Jewelry Store Share designs and prices Book appointments for trials Check customization options Verify metal purity/certifications Handle repair inquiries Bookstore Check book availability Take pre-orders for new releases Recommend books by genre Share reading lists Handle exchange requests Important Notes: This workflow requires WhatsApp Business API (not regular WhatsApp Business app) WhatsApp Business API typically requires business verification Message rates and limits vary by provider Test thoroughly before deploying to customers Always provide a way to reach human support Getting Started Tip: Start with just contact info and product inquiries. Once that works smoothly, add AI responses for complex queries. Gradually expand based on actual customer needs you observe in conversations.
by WeblineIndia
Automated Social Media Lead Processing with AI Summaries, Slack Alerts & Jira Ticketing This workflow automatically collects new lead messages from social media platforms, LinkedIn or web forms, filters relevant marketing inquiries using keywords, classifies and summarizes the lead with AI, logs it to Google Sheets, creates a Jira task and sends Slack notifications. Additionally, it generates weekly lead reports for team insights. It reduces manual triage, ensures no valid inquiry is missed and keeps your team updated with both immediate notifications and summary reports. Quick Start β Implementation Steps Connect your webhook to your social media inbox, LinkedIn, Twitter or web form. Add your OpenAI, Google Sheets, Jira and Slack credentials. Enable the workflow. Send a test message to confirm Google Sheets logging, Slack notification and Jira task creation. Activate the scheduler for weekly reports to track lead performance. What It Does This workflow performs the following key tasks: Filters incoming messages for marketing-related keywords like ad request, promo request, collaboration, partnership or social media inquiry. Uses OpenAI GPT to classify the lead into categories such as Sales, Support, Partnership, Influencer Inquiry or General Lead. Generates a short AI summary of the message. Logs structured lead data to Google Sheets, including username, source, category, summary and timestamp. Creates a Jira task automatically with summary, description, category and received time. Sends a Slack notification to alert the team instantly. Runs a scheduled workflow that aggregates weekly leads and sends a weekly report to Slack. This ensures a structured, automated pipeline for capturing, summarizing and assigning leads efficiently. Whoβs It For Marketing and sales teams managing leads from social media and web forms. Agencies handling client campaigns and inquiries. Businesses that want automated notifications and ticketing. Teams using Slack and Jira for daily operations. Requirements to Use This Workflow n8n account or self-hosted instance. Webhook-enabled social media inbox or lead form. OpenAI API Key. Slack Bot Token with channel posting permission. Jira Software Cloud API credentials. Google Sheets credentials. Predefined keyword list for filtering messages. How It Works & Setup Steps 1. Get DM (Webhook Trigger) Receives new messages from social media or web forms and starts the workflow. 2. Lead Keyword Filter (Code Node) Filters incoming messages for predefined marketing keywords and removes irrelevant or spam messages. 3. AI Lead Classifier (OpenAI Node) Classifies the lead into categories (Sales, Support, Partnership, Influencer Inquiry, General Lead) and generates a one-line summary using GPT-4.1. 4. AI Output Parser (Code Node) Parses AI JSON output and merges it with original message data, adding timestamp and structured fields. 5. Store Lead (Google Sheets Node) Logs structured lead data to Google Sheets including username, source, category, summary and timestamp. 6. Create Task (Jira Node) Automatically creates a Jira story or task in your selected project with the AI summary, category and timestamp. 7. Send a Summary (Slack Node) Sends a formatted message to your selected Slack channel, alerting your team of the new lead. 8. Weekly Reporting Schedule Trigger** β triggers the weekly reporting workflow. Extract Lead Data** β fetches all logged leads from Google Sheets. Weekly Lead Filter** β filters data to include leads from the last week. Report Data Formatter** β calculates total leads, category counts, source counts and example leads. Weekly Report Slack** β sends a formatted weekly lead summary to Slack. How to Customize Nodes Keyword Filter Add or remove keywords in the JavaScript code to match your specific lead types or campaigns. AI Classification Update the OpenAI prompt for different summary lengths, tones, or lead categories. Google Sheets Logging Map additional columns like email, phone or campaign source as needed. Jira Fields Customize summary, description, labels, priority or assignees based on your project requirements. Slack Message Format Modify emojis, line breaks and formatting to suit your teamβs Slack notifications. Add-Ons (Extend the Workflow) Send email alerts for high-priority leads. Trigger WhatsApp replies using an API provider. Integrate with CRMs like HubSpot, Zoho or Salesforce. Add sentiment analysis to detect frustrated or VIP users. Automate daily or weekly analytics reports to Slack. Use Case Examples Collecting Instagram, LinkedIn and Twitter DMs and logging them to Google Sheets. Creating automated Jira tickets for marketing inquiries. Sending instant Slack notifications for new leads. Filtering out irrelevant messages and only processing valid marketing leads. Generating weekly lead summary reports for team review. Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|----------| | No leads appearing | Webhook not receiving messages | Check webhook URL and ensure messages are sent correctly | | AI summary empty | OpenAI API key invalid or model limit reached | Regenerate API key / check usage | | Jira task not created | Missing required Jira fields or incorrect project ID | Add required fields or update Jira project settings | | Slack message not sent | Wrong channel ID or missing permissions | Reconnect Slack credentials | | Filter passes 0 items | Keywords do not match | Update or expand keyword list in filter node | Need Help? If you need assistance setting up this workflow, customizing nodes, building add-ons or automating more processes, our n8n workflow development team at WeblineIndia is happy to help. We can guide you through integrations, scaling or building end-to-end automation systems tailored to your business.
by phil
This workflow is designed for B2B professionals to automatically identify and summarize business opportunities from a company's website. By leveraging Bright Data's Web Unblocker and advanced AI models from OpenRouter, it scrapes relevant company pages ("About Us", "Team", "Contact"), analyzes the content for potential pain points and needs, and synthesizes a concise, actionable report. The final output is formatted for direct use in documents, making it an ideal tool for sales, marketing, and business development teams to prepare for prospecting calls or personalize outreach. Who's it for This template is ideal for: B2B Sales Teams:** Quickly find and qualify leads by identifying specific business needs before a cold call. Marketing Agencies:** Develop personalized content and value propositions based on a prospect's public website information. Business Development Professionals:** Efficiently research potential partners or clients and discover collaboration opportunities. Entrepreneurs:** Gain a competitive edge by understanding a competitor's strategy or a potential client's operations. How it works The workflow is triggered by a chat message, typically a URL from an n8n chat application. It uses Bright Data to scrape the website's sitemap and extract all anchor links from the homepage. An AI agent analyzes the extracted URLs to filter for pages relevant to company information (e.g., "about-us," "team," "contact"). The workflow then scrapes the content of these specific pages. A second AI agent summarizes the content of each page, looking for business opportunities related to AI-powered automation. The summaries are merged and a final AI agent synthesizes them into a single, cohesive report, formatted for easy reading in a Google Doc. How to set up Bright Data Credentials: Sign up for a Bright Data account and create a Web Unblocker zone. In n8n, create new Bright Data API credentials and copy your API key. OpenRouter Credentials: Create an account on OpenRouter and get your API key. In n8n, create new OpenRouter API credentials and paste your key. Chat Trigger Node: Configure the "When chat message received" node. Copy the production webhook URL to integrate with your preferred chat platform. Requirements An active n8n instance. A Bright Data account with a Web Unblocker zone. An OpenRouter account with API access. How to customize this workflow AI Prompting:** Edit the "systemMessage" parameters in the "AI Agent", "AI Agent1", and "AI Agent2" nodes to change the focus of the opportunity analysis. For example, modify the prompts to search for specific technologies, industry jargon, or different types of business challenges. Model Selection:** The workflow uses openai/o4-mini and openai/gpt-5. You can change these to other models available on OpenRouter by editing the model parameter in the OpenRouter Chat Model nodes. Scraping Logic:** The extract url node uses a regular expression to find `` tags. This can be modified or replaced with an HTML Extraction node to target different elements or content on a website. Output Format:** The final output is designed for Google Docs. You can modify the last "AI Agent2" node's prompt to generate the output in a different format, such as a simple JSON object or a markdown list. Phil | Inforeole π«π· Contactez nous pour automatiser vos processus
by SpaGreen Creative
WhatsApp Number Verify & Confirmation System with Rapiwa API and Google Sheets Who is this for? This n8n workflow makes it easy to verify WhatsApp numbers submitted through a form. When someone fills out the form, the automation kicks inβcapturing the data via a webhook, checking the WhatsApp number using the Rapiwa API, and sending a confirmation message if the number is valid. All submissions, whether verified or not, are logged into a Google Sheet with a clear status. Itβs a great solution for businesses, marketers, or developers who need a reliable way to verify leads, manage event signups, or onboard customers using WhatsApp. How it works? This n8n automation listens for form submissions via a webhook, validates the provided WhatsApp number using the Rapiwa API, sends a confirmation message if the number is verified, and then appends the submission data to a Google Sheet, marking each entry as verified or unverified. Features Webhook Trigger**: Captures form submissions via HTTP POST Data Cleaning**: Formats and sanitizes the WhatsApp number Rapiwa API Integration**: Checks if the number is registered on WhatsApp Conditional Messaging**: Sends confirmation messages only to verified WhatsApp users Google Sheets Integration**: Appends all submissions with a validity status Auto Timestamping**: Adds the submission date in YYYY-MM-DD format Throttling Support**: Built-in delay to avoid hitting API or sheet rate limits Separation of Verified/Unverified**: Distinct handling for both types of entries Nodes Used in the Workflow Webhook** Format Webhook Response Data** (Code) Loop Over Items** (Split In Batches) Cleane Number** (Code) check valid whatsapp number** (HTTP Request) If** (Conditional) Send Message Using Rapiwa** verified append row in sheet** (Google Sheets) unverified append row in sheet** (Google Sheets) Wait1** How to set up? Webhook Add a Webhook node to the canvas. Set HTTP Method to POST. Copy the Webhook URL path (/a9b6a936-e5f2-4xxxxxxxxxe0a970d5). In your frontend form or app, make a POST request to: The request body should include: { "business_name": "ABC Corp", "location": "New York", "whatsapp": "+1 234-567-8901", "email": "user@example.com", "name": "John Doe" } Format Webhook Response Data Add a Code node after the Webhook node. Use this JavaScript code: const result = $input.all().map(item => { const body = item.json.body || {}; const submitted_date = new Date().toISOString().split('T')[0]; return { business_name: body.business_name, location: body.location, whatsapp: body.whatsapp, email: body.email, name: body.name, submitted_date: submitted_date }; }); return result; Loop Over Items Insert a SplitInBatches node after the data formatting. Set the Batch Size to a reasonable number (e.g. 1 or 10). This is useful for processing multiple submissions at once, especially if your webhook receives arrays of entries. Note: If you expect only one submission at a time, it still helps future-proof your workflow. Cleane Number Add a Code node named Cleane Number. Paste the following JavaScript: const items = $input.all(); const updatedItems = items.map((item) => { const waNo = item?.json["whatsapp"]; const waNoStr = typeof waNo === 'string' ? waNo : (waNo !== undefined && waNo !== null ? String(waNo) : ""); const cleanedNumber = waNoStr.replace(/\D/g, ""); item.json["whatsapp"] = cleanedNumber; return item; }); return updatedItems; Check WhatsApp Number using Rapiwa Add an HTTP Request node. Set: Method: POST URL: https://app.rapiwa.com/api/verify-whatsapp Add authentication: Type: HTTP Bearer Credentials: Select or create Rapiwa token In Body Parameters, add: number: ={{ $json.whatsapp }} This API call checks if the WhatsApp number exists and is valid. Expected Output: { "success": true, "data": { "number": "+88017XXXXXXXX", "exists": true, "jid": "88017XXXXXXXXXXXXX", "message": "β Number is on WhatsApp" } } Conditional If Check Add an If node after the Rapiwa validation. Configure the condition: Left Value: ={{ $json.data.exists }} Operation: true If true β valid number β go to messaging and append as "verified". If false β go to unverified sheet directly. Note: This step branches the flow based on the WhatsApp verification result. Send WhatsApp Message (Rapiwa) Add an HTTP Request node under the TRUE branch of the If node. Set: Method: POST URL: https://app.rapiwa.com/api/send-message Authentication: Type: HTTP Bearer Use same Rapiwa token Body Parameters: number: ={{ $json.data.phone }} message_type: text message: Hi {{ $('Cleane Number').item.json.name }}, Thanks! Your form has been submitted successfully. This sends a confirmation message via WhatsApp to the verified number. Google Sheets β Verified Data Add a Google Sheets node under the TRUE branch (after the message is sent). Set: Operation: Append Document ID: Choose your connected Google Sheet Sheet Name: Set to your active sheet (e.g., Sheet1) Column Mapping: Business Name: ={{ $('Cleane Number').item.json.business_name }} Location: ={{ $('Cleane Number').item.json.location }} WhatsApp Number: ={{ $('Cleane Number').item.json.whatsapp }} Email : ={{ $('Cleane Number').item.json.email }} Name: ={{ $('Cleane Number').item.json.name }} Date: ={{ $('Cleane Number').item.json.submitted_date }} validity: verified Use OAuth2 Google Sheets credentials for access. Note: Make sure the sheet has matching column headers. Google Sheets β Unverified Data Add a Google Sheets node under the FALSE branch of the If node. Use the same settings as the verified node, but set: validity: unverified This stores entries with unverified WhatsApp numbers in the same Google Sheet. Wait Node Add a Wait node after both Google Sheets nodes. Set Wait Time: Value: 2 seconds This delay prevents API throttling and adds buffer time before processing the next item in the batch. Google Sheet Column Reference A Google Sheet formatted like this β€ Sample Sheet | Business Name | Location | WhatsApp Number | Email | Name | validity | Date | |---------------------|--------------------|------------------|----------------------|------------------|------------|------------| | SpaGreen Creative | Dhaka, Bangladesh | 8801322827799| contact@spagreen.net | Abdul Mannan | unverified | 2025-09-14 | | SpaGreen Creative | Bagladesh | 8801322827799| contact@spagreen.net| Abdul Mannan | verified | 2025-09-14 | > Note: The Email column includes a trailing space. Ensure your column headers match exactly to prevent data misalignment. How to customize the workflow Modify confirmation message with your brand tone Add input validation for missing or malformed fields Route unverified submissions to a separate spreadsheet or alert channel Add Slack or email notifications on new verified entries Notes & Warnings Ensure your Google Sheets credential has access to the target sheet Rapiwa requires an active subscription for API access Monitor Rapiwa API limits and adjust wait time as needed Keep your webhook URL protected to avoid misuse Support & Community WhatsApp Support: Chat Now Discord: Join SpaGreen Community Facebook Group: SpaGreen Support Website: spagreen.net Developer Portfolio: Codecanyon SpaGreen