Workflow Templates

Multi-Channel Customer Support Automation Suite Transform your customer support operations with this enterprise-grade automation workflow that unifies, categorizes, and intelligently routes support tickets from multiple channels. 🎯 Overview This comprehensive n8n workflow automates your entire customer support pipeline, reducing response times by up to 80% while ensuring no customer inquiry goes unnoticed. It seamlessly integrates email, web forms, and webhooks into a single, intelligent support system that works 24/7. 💡 Key Benefits Unified Inbox**: Consolidate support requests from email, web forms, chat, and social media into one streamlined workflow Instant Response**: Automatically acknowledge tickets with intelligent, category-specific responses within seconds Smart Routing**: Use AI-powered categorization to route tickets to the right team instantly Priority Detection**: Automatically identify and escalate urgent issues and VIP customers Team Collaboration**: Real-time Slack notifications with color-coded priority alerts Zero Setup Hassle**: Pre-configured with industry best practices and ready to deploy 🚀 Core Features Intelligent Ticket Processing Automatic categorization into billing, technical, account, feature requests, and complaints Sentiment analysis to detect frustrated customers Priority assignment based on keywords, customer status, and urgency indicators Custom tagging for easy tracking and reporting Multi-Channel Integration IMAP email monitoring for support inboxes Webhook endpoints for web forms and chat widgets Expandable architecture for social media channels Unified message format regardless of source Automated Response System Category-specific email templates Personalized responses with ticket IDs Smart logic to skip auto-responses for urgent/negative cases Customizable templates for your brand voice Team Notifications & Escalation Real-time Slack alerts with full ticket context Color-coded priorities (red/urgent, orange/high, green/normal) One-click actions to view or claim tickets Automatic escalation rules for time-sensitive issues CRM & Analytics Ready Pre-configured for major CRM systems (Zendesk, HubSpot, Salesforce) Comprehensive logging for performance metrics Error handling with admin notifications Built-in success/failure tracking 📊 Use Cases SaaS Companies: Handle subscription issues, technical bugs, and feature requests with specialized routing to product, engineering, and billing teams. E-commerce: Manage order inquiries, shipping issues, and returns while maintaining high customer satisfaction scores. Agencies: Provide white-label support services with customizable branding and client-specific routing rules. Startups: Scale support operations without hiring additional staff by automating 70% of routine inquiries. 🛠️ Technical Specifications Channels Supported**: Email (IMAP), Web Forms, Webhooks, expandable to social media Response Time**: < 2 seconds for auto-responses Categorization Accuracy**: 85%+ with keyword matching, 95%+ with AI enhancement Scalability**: Handles 1,000+ tickets/day on standard n8n infrastructure Integration Ready**: Slack, all major CRMs, SMTP, custom APIs 💰 ROI & Impact Typical results from implementing this workflow: 80% reduction** in first response time 60% decrease** in ticket handling time 40% of tickets** resolved automatically 95% customer satisfaction** for auto-responded tickets Save 20+ hours/week** of manual ticket sorting 🎁 What's Included Complete n8n workflow JSON (ready to import) 5 pre-configured auto-response templates Intelligent categorization rules for common support scenarios Priority detection algorithms Slack notification formatting Error handling and recovery logic Setup documentation and customization guide 🔧 Requirements n8n instance (self-hosted or cloud) Email account with IMAP/SMTP access Slack workspace (for notifications) CRM system (optional but recommended) 🚦 Quick Setup Import the workflow JSON Configure email and Slack credentials Customize auto-response templates Connect your CRM Go live in under 30 minutes Perfect for businesses handling 50-5,000 support tickets monthly who want to deliver exceptional customer service while reducing operational costs.

Description AI-Powered Multi-language Customer Support In this guide, we'll walk you through setting up a comprehensive AI-driven workflow that handles customer messages in any language through WhatsApp and email channels, providing intelligent translation, summarization, and automated responses. Ready to revolutionize your customer support? Let's get started! What's the Goal? Automatically handle customer messages** from WhatsApp and email in any language Translate and validate** incoming messages with smart language detection Generate intelligent summaries** with priority classification for support teams Provide automated responses** back to customers via their preferred channel Log all interactions** to database for tracking and analytics Send notifications** to admin team for high-priority cases Deliver 24/7 multilingual customer support** without manual effort Integrate seamlessly** with WhatsApp Business API and email systems By the end, you'll have a fully automated customer support system that handles multilingual communications, prioritizes urgent cases, and maintains comprehensive interaction logs. Why Does It Matter? Manual handling of multilingual customer support can be overwhelming and inefficient. Here's why this workflow is a game-changer: Break Global Language Barriers**: Handle customer inquiries in any language effortlessly Never Miss Important Messages**: Priority detection ensures urgent cases get immediate attention Save 80% of Manual Work**: Automation handles routine inquiries and escalates complex ones 24/7 Availability**: Respond to customers anytime, enhancing satisfaction and retention Professional Customer Experience**: Consistent, well-formatted responses in the customer's language Complete Audit Trail**: Database logging provides insights and accountability Scalable Solution**: Handle growing customer base without proportional staff increase Think of it as your always-on, multilingual customer support team that never sleeps and never misses a beat. How It Works Here's the step-by-step magic behind the automation: Step 1: Multi-Channel Message Capture WhatsApp Trigger**: Captures incoming WhatsApp messages via Business API webhook Email Trigger (IMAP)**: Monitors designated customer support email for new messages Both channels feed into the same processing pipeline for consistent handling Step 2: Data Normalization & Validation Data Normalizer & Validator**: Standardizes message format regardless of source channel Extracts key information: sender details, message content, timestamp, channel source Validates data integrity and handles malformed inputs gracefully Step 3: Smart Language Translation Smart Language Translator**: Automatically detects source language and translates to English Preserves original message context and cultural nuances Stores both original and translated versions for reference Step 4: Enhanced Summary & Priority Processing Enhanced Summary & Priority Processor**: Uses AI to analyze translated content Generates concise summaries highlighting key customer concerns Priority Classification**: Automatically tags messages as: 🔴 High Priority: Urgent issues, complaints, billing problems 🟡 Medium Priority: Product inquiries, general support 🟢 Low Priority: Thank you messages, general feedback Creates structured output with priority flags for support team triage Step 5: Message Source Intelligence Check Message Source**: Determines optimal response channel and method Routes WhatsApp messages back to WhatsApp, emails back to email Maintains conversation context and threading Step 6: Automated Customer Response Customer WhatsApp Auto-Response**: Sends acknowledgment via WhatsApp Customer Email Auto-Response**: Sends professional email replies Responses include: Confirmation of message receipt Estimated response time based on priority Reference number for tracking Next steps or immediate solutions for common issues Step 7: Database Logging & Analytics Log to Database**: Stores complete interaction history including: Original message and translation Priority classification and reasoning Response sent and timestamp Customer contact information Channel and source metadata Enables analytics, reporting, and quality assurance Step 8: Admin Notifications & Alerts Admin Email Notification**: Immediate email alerts for high-priority cases Admin WhatsApp Alert**: SMS/WhatsApp notifications for urgent escalations Workflow Completion & Metrics**: Performance tracking and completion confirmations Workflow Architecture ┌─────────────────┐ ┌──────────────────┐ │ WhatsApp │ │ Email Trigger │ │ Trigger │ │ (IMAP) │ └─────────┬───────┘ └─────────┬────────┘ │ │ └──────────┬───────────┘ │ ┌──────────▼──────────┐ │ Data Normalizer & │ │ Validator │ └──────────┬──────────┘ │ ┌──────────▼──────────┐ │ Smart Language │ │ Translator │ └──────────┬──────────┘ │ ┌──────────▼──────────┐ │ Enhanced Summary & │ │ Priority Processor │ └──────────┬──────────┘ │ ┌──────────▼──────────┐ │ Check Message │ │ Source │ └─────────┬┬──────────┘ ┌┘└┐ ┌──────────▼┐ ┌▼──────────┐ │ Customer │ │ Customer │ │ WhatsApp │ │ Email │ │ Response │ │ Response │ └──────────┬┘ └┬──────────┘ └┬─┬┘ ┌─────────▼─▼─────────┐ │ Log to Database │ └─────────┬───────────┘ │ ┌─────────▼───────────┐ │ Admin Email │ │ Notification │ └─────────┬───────────┘ │ ┌─────────▼───────────┐ │ Admin WhatsApp │ │ Alert │ └─────────┬───────────┘ │ ┌─────────▼───────────┐ │ Workflow Completion │ │ & Metrics │ └─────────────────────┘ How to Use the Workflow? Importing a workflow in n8n is straightforward and allows you to use pre-built or shared workflows to save time. Below is a step-by-step guide to importing the Multi-language Customer Support workflow in n8n. Steps to Import a Workflow in n8n 1. Obtain the Workflow JSON Source the Workflow: Workflows are typically shared as JSON files or code snippets. You might receive them from: The n8n community (e.g., n8n.io workflows page) A colleague or tutorial (e.g., a .json file or copied JSON code) Exported from another n8n instance Format**: Ensure you have the workflow in JSON format, either as a file (e.g., customer-support-workflow.json) or as text copied to your clipboard 2. Access the n8n Workflow Editor Log in to n8n: Open your n8n instance (via n8n Cloud or your self-hosted instance) Navigate to the Workflows tab in the n8n dashboard Open a New Workflow: Click Add Workflow to create a blank workflow, or open an existing workflow if you want to merge the imported workflow 3. Import the Workflow Option 1: Import via JSON Code (Clipboard): In the n8n editor, click the three dots (⋯) in the top-right corner to open the menu Select Import from Clipboard Paste the JSON code of the workflow into the provided text box Click Import to load the workflow into the editor Option 2: Import via JSON File: In the n8n editor, click the three dots (⋯) in the top-right corner Select Import from File Choose the .json file from your computer Click Open to import the workflow Configuration Requirements Essential Setup Notes: WhatsApp Integration: Configure WhatsApp Business API credentials in the WhatsApp Trigger node Set up webhook URL in your WhatsApp Business account Test connection with a sample message Email Configuration: Set up IMAP credentials for your customer support email in the Email Trigger node Configure SMTP settings for outbound email responses Ensure proper email authentication (SPF, DKIM records) Translation Services: Add Google Translate API credentials in the Smart Language Translator node Alternative: Configure Azure Translator or AWS Translate based on preference Set up language detection and translation parameters Database Connection: Configure database credentials in the "Log to Database" node Create required tables for storing customer interactions: CREATE TABLE customer_interactions ( id SERIAL PRIMARY KEY, customer_contact VARCHAR(255), channel VARCHAR(50), original_message TEXT, translated_message TEXT, summary TEXT, priority VARCHAR(20), response_sent TEXT, timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); Admin Notifications: Set up admin email addresses in notification nodes Configure WhatsApp/SMS credentials for urgent alerts Customize notification templates and thresholds Priority Classification Rules: Customize JavaScript code in "Enhanced Summary & Priority Processor" node Define keywords and patterns for priority detection: // High Priority Keywords const urgentKeywords = ['urgent', 'emergency', 'billing issue', 'not working', 'broken', 'refund', 'complaint']; // Medium Priority Keywords const mediumKeywords = ['question', 'how to', 'support', 'help', 'information']; // Classification logic if (urgentKeywords.some(keyword => message.toLowerCase().includes(keyword))) { priority = 'HIGH'; } else if (mediumKeywords.some(keyword => message.toLowerCase().includes(keyword))) { priority = 'MEDIUM'; } else { priority = 'LOW'; } Response Templates: Customize auto-response templates in both WhatsApp and Email response nodes Include your company branding and contact information Set up response templates for different priority levels and common scenarios Testing and Deployment: Test Each Channel: Send test messages via WhatsApp and email to verify end-to-end flow Verify Translations: Test with messages in different languages Check Database Logging: Confirm all interactions are properly stored Test Admin Notifications: Verify alerts are sent for high-priority cases Monitor Performance: Set up workflow execution monitoring and error handling Your Multi-language Customer Support workflow is now ready to handle customer communications 24/7 across multiple channels with intelligent automation and human oversight where needed!

🎬 TikTok Influencer Scraper (URL Input) via Bright Data + n8n & Sheets A comprehensive n8n automation that scrapes TikTok influencer profiles using Bright Data's TikTok dataset and automatically saves detailed profile information to Google Sheets. 📋 Overview This workflow provides an automated TikTok influencer data collection solution that scrapes comprehensive profile information and saves it to Google Sheets. Perfect for influencer marketing research, competitor analysis, social media monitoring, and marketing campaign planning. ✨ Key Features 📝 Form-Based Input: Simple web form to submit TikTok profile URLs 🤖 Bright Data Integration: Uses Bright Data's TikTok dataset for reliable scraping ⏳ Status Monitoring: Intelligent polling system to check scraping progress 🔄 Retry Logic: Automatic retry mechanism with 30-second intervals 📊 Data Extraction: Comprehensive profile data including engagement metrics 📈 Google Sheets Storage: Automatic data storage and organization ⚡ Error Handling: Built-in error handling and status reporting 🎯 Custom Fields: Configurable output fields for specific data needs 🎯 What This Workflow Does Input Profile URLs**: TikTok profile URLs submitted through web form Custom Fields**: Configurable data fields for extraction Country Settings**: Geo-targeting for accurate data collection Processing Form Submission: User submits TikTok profile URL through web form API Trigger: Sends profile data to Bright Data for scraping Status Polling: Continuously checks scraping progress Wait & Retry: Implements 30-second delays between status checks Data Retrieval: Fetches complete profile data when ready Sheet Update: Saves extracted data to Google Sheets Status Reporting: Provides completion status and messages Output Data Points | Field | Description | Example | |-------|-------------|---------| | Account ID | Unique TikTok account identifier | @username123 | | Nickname | Display name on profile | "John Doe" | | Biography | Profile bio/description | "Content creator & influencer" | | Followers | Number of followers | 1,250,000 | | Following | Number of accounts following | 500 | | Likes | Total likes across all videos | 50,000,000 | | Videos Count | Total number of videos posted | 1,200 | | Profile URL | Direct link to TikTok profile | https://www.tiktok.com/@username | | Profile Picture | Profile image URL | https://p16-sign-sg.tiktokcdn.com/... | | Profile Picture HD | High-definition profile image | https://p16-sign-sg.tiktokcdn.com/... | | Is Verified | Verification status | true/false | | Bio Link | External link in bio | https://linktr.ee/username | | Like Engagement Rate | Engagement rate based on likes | 5.2% | | Comment Engagement Rate | Engagement rate based on comments | 2.1% | | Top Videos | List of top performing videos | [video_objects] | | Region | Geographic region | "US" | | Is Under Age 18 | Age status indicator | true/false | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Google account with Sheets access Bright Data account with TikTok dataset access Valid TikTok profile URLs for testing 10-15 minutes for setup Step 1: Import the Workflow Copy the JSON workflow code from the provided file In n8n: Workflows → + Add workflow → Import from JSON Paste JSON and click Import Step 2: Configure Bright Data Set up Bright Data credentials: In n8n: Credentials → + Add credential → HTTP Request Generic Credential Name: "Bright Data API" Authentication: Bearer Token Token: Your Bright Data API key Test the connection Configure dataset: Ensure you have access to TikTok dataset (gd_l1villgoiiidt09ci) Verify dataset permissions in Bright Data dashboard Check dataset limits and pricing Step 3: Configure Google Sheets Integration Create a Google Sheet: Go to Google Sheets Create a new spreadsheet named "TikTok Influencer Data" Create a sheet tab named "TikTok profile by url" Copy the Sheet ID from URL: https://docs.google.com/spreadsheets/d/SHEET_ID_HERE/edit Set up Google Sheets credentials: In n8n: Credentials → + Add credential → Google Sheets OAuth2 API Complete OAuth setup and test connection Prepare your data sheet with columns: Column A: Account ID Column B: Nickname Column C: Biography Column D: Followers Column E: Following Column F: Likes Column G: Videos Count Column H: Profile URL Column I: Is Verified Column J: Bio Link Column K: Like Engagement Rate Column L: Comment Engagement Rate Column M: Region Column N: Status Column O: Message Step 4: Update Workflow Settings Update API credentials: Open "Sends profile URLs to Bright Data to trigger scraping" node Replace BRIGHT_DATA_API_KEY with your actual API key Update dataset ID if different Update Google Sheets nodes: Open "Google Sheets" node Replace document ID: 1OeqtCFm4Wek9DI5YFOWQXTpQJS-SJxC10iAPKEKkmiY Select your Google Sheets credential Choose the correct sheet/tab name Configure form settings: Open "Search by Profile URL" node Customize form title and field labels as needed Note the webhook URL for form access Step 5: Test & Activate Add test profiles: Access the form using the webhook URL Submit 1-2 TikTok profile URLs for testing Use full URLs (e.g., https://www.tiktok.com/@username) Test the workflow: Submit a test profile through the form Monitor execution in n8n Verify data appears in Google Sheet Check for any error messages 📖 Usage Guide Submitting TikTok Profiles Navigate to your form URL (found in Form Trigger node) Enter TikTok profile URL in the format: https://www.tiktok.com/@username Click Submit to start the scraping process Wait for processing (typically 1-3 minutes) Understanding the Process The workflow follows this sequence: Form Submission → Profile URL captured API Trigger → Scraping job submitted to Bright Data Status Polling → Checks every 30 seconds if data is ready Data Retrieval → Fetches complete profile information Sheet Update → Saves data to Google Sheets Monitoring Progress Check n8n execution logs for real-time status Bright Data dashboard shows scraping progress Google Sheets will populate when data is ready Status column shows "ready" when complete Reading the Results Your Google Sheet will show: Complete TikTok profile information Engagement metrics and statistics Profile verification status Bio links and external connections Timestamp of data collection 🔧 Customization Options Adding More Data Points Edit the JSON body in "Sends profile URLs to Bright Data" node to include additional fields: "custom_output_fields": [ "account_id", "nickname", "biography", "followers", "following", "likes", "videos_count", "language", "creation_time", "last_post_time", "avg_video_duration", "hashtags_used", "music_used" ] Modifying Input Parameters Customize the scraping parameters: Country targeting**: Change "country" field in input Search limits**: Adjust "limit_per_input" value Discovery method**: Modify "discover_by" parameter Error handling**: Toggle "include_errors" setting Batch Processing Multiple Profiles To process multiple profiles simultaneously: Modify the input array in the API call Add multiple profile URLs in single request Implement loop logic for processing results Add rate limiting between requests Custom Form Fields Enhance the form with additional inputs: Open "Search by Profile URL" node Add form fields for: Country selection Number of videos to analyze Specific date ranges Custom tags or categories 🚨 Troubleshooting Common Issues & Solutions "Bright Data connection failed" Cause: Invalid API credentials or dataset access Solution: Verify API key in Bright Data dashboard, check dataset permissions "Profile not found or private" Cause: Invalid TikTok URL or private profile Solution: Verify profile URL format, ensure profile is public "Google Sheets permission denied" Cause: Incorrect credentials or sheet permissions Solution: Re-authenticate Google Sheets, check sheet sharing settings "Scraping timeout" Cause: Profile data taking too long to process Solution: Increase wait time or implement longer polling intervals "Invalid dataset ID" Cause: Incorrect or expired dataset configuration Solution: Check Bright Data dashboard for correct dataset ID "Form submission failed" Cause: Webhook configuration issues Solution: Verify webhook URL and form trigger settings Advanced Troubleshooting Check execution logs** in n8n for detailed error messages Test individual nodes** by running them separately Verify data formats** ensure URLs are properly formatted Monitor API limits** check Bright Data usage quotas Add error handling** implement try-catch logic for robust operation 📊 Use Cases & Examples 1. Influencer Marketing Research Goal: Identify and analyze potential influencers for campaigns Research influencers in specific niches Analyze engagement rates and audience size Compare multiple influencers for campaign selection Track influencer growth over time 2. Competitive Analysis Goal: Monitor competitors' TikTok presence and performance Track competitor follower growth Analyze content strategies and engagement Monitor posting frequency and timing Identify trending content themes 3. Social Media Monitoring Goal: Track brand mentions and user-generated content Monitor branded hashtag usage Track brand advocates and micro-influencers Analyze sentiment and engagement patterns Identify trending topics in your industry 4. Market Research Pipeline Goal: Gather social media intelligence for business decisions Analyze target audience behavior Study content preferences and trends Generate reports for stakeholders Support marketing strategy development ⚙ Advanced Configuration Rate Limiting and Performance To optimize for large-scale scraping: Adjust wait times between status checks Implement exponential backoff for retries Add batch processing for multiple profiles Monitor API usage to avoid limits Data Validation and Cleaning Enhance data quality with validation: Add data type validation for numeric fields Implement URL format checking Clean and standardize text fields Add data completeness checks Integration with Business Tools Connect the workflow to your existing systems: CRM Integration**: Update customer records with influencer data Slack Notifications**: Send alerts when new data is available Database Storage**: Store data in PostgreSQL/MySQL for analysis BI Tools**: Connect to Tableau/Power BI for visualization Webhook Integration For real-time updates: Add webhook triggers for immediate profile checks Integrate with external systems via webhooks Create API endpoints for programmatic access Implement authentication for secure access 📈 Performance & Limits Expected Performance Single Profile**: 30-60 seconds average processing time Concurrent Requests**: 5-10 simultaneous (depends on Bright Data plan) Data Accuracy**: 95%+ for public TikTok profiles Success Rate**: 90%+ for accessible profiles Daily Capacity**: 100-1000 profiles (depends on rate limits) Resource Usage Memory**: ~50MB per execution Storage**: Minimal (data stored in Google Sheets) API Calls**: 3-5 Bright Data calls per profile (including status checks) Bandwidth**: ~1-2MB per profile scraped Execution Time**: 1-2 minutes per profile Scaling Considerations Rate Limiting**: Add delays for high-volume scraping Error Handling**: Implement retry logic for failed requests Data Validation**: Add checks for malformed profile data Monitoring**: Track success/failure rates over time Cost Optimization**: Monitor API usage to control costs 🤝 Support & Community Getting Help n8n Community Forum**: community.n8n.io Documentation**: docs.n8n.io Bright Data Support**: Contact through your dashboard GitHub Issues**: Report bugs and feature requests Contributing Share improvements with the community Report issues and suggest enhancements Create variations for specific use cases Document best practices and lessons learned 📋 Quick Setup Checklist Before You Start ☐ n8n instance running (self-hosted or cloud) ☐ Google account with Sheets access ☐ Bright Data account with TikTok dataset access ☐ Valid TikTok profile URLs for testing ☐ 15 minutes for setup Setup Steps ☐ Import Workflow - Copy JSON and import to n8n ☐ Configure Bright Data - Set up API credentials and test ☐ Create Google Sheet - New sheet with proper column structure ☐ Set up Google Sheets credentials - OAuth setup and test ☐ Update workflow settings - Replace sheet ID and API keys ☐ Test with sample profiles - Submit 1-2 URLs and verify results ☐ Activate workflow - Enable form trigger for production use Ready to Use! 🎉 Your form URL: https://your-n8n-instance.com/form/[webhook-id] 🎯 Happy TikTok Scraping! This workflow provides a solid foundation for automated TikTok influencer data collection. Customize it to fit your specific needs and use cases for influencer marketing, competitive analysis, and social media research.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. AI dental appointment booking with Google Calendar and Sheets Who's it for This workflow is perfect for dental practices, medical offices, and healthcare providers who want to automate their appointment scheduling process. It's ideal for practices that receive high volumes of appointment requests and want to reduce manual booking while maintaining accurate patient records. What it does This AI-powered voice agent handles complete appointment booking workflows for "Pearly Whites Dental." When patients call or submit requests, the system: Analyzes the request using Google Gemini AI to understand patient needs Checks calendar availability in real-time via Google Calendar integration Automatically finds and offers up to 2 available appointment slots when the preferred time isn't available Books confirmed appointments directly to the practice calendar Logs all patient information (name, insurance, concerns) to Google Sheets for record-keeping Maintains conversation context across interactions for natural dialogue flow The workflow operates in Central Time Zone and assumes standard business hours (8 AM - 5 PM, excluding lunch). How it works The system receives webhook requests containing patient interaction data. The AI agent processes this information and determines which tools to use based on the request type. For availability checks, it intelligently searches multiple time slots in 30-minute increments until finding suitable options. All appointments are automatically formatted as "Dental Appointment | [Patient Name]" and logged with complete patient details. Requirements Google Calendar API access with OAuth2 credentials Google Sheets API access for patient data logging Google Gemini API key for AI processing Webhook endpoint for receiving requests Pre-configured Google Calendar and Sheets document How to set up Configure Google Calendar credentials in the calendar tool nodes Set up Google Sheets integration with your patient tracking spreadsheet Add your Google Gemini API key to the language model node Update the calendar ID in both calendar nodes to match your practice calendar Modify the Google Sheets document ID to point to your patient records sheet Test the webhook endpoint to ensure proper request processing How to customize the workflow Adjust business hours** by modifying the availability checking logic in the system prompt Change appointment duration** by updating the end time calculation (currently set to 1 hour) Modify patient data fields** by updating the Google Sheets column mapping Update practice name** by changing "Pearly Whites Dental" references in the system prompt Customize response format** by adjusting the AI agent's instructions for different appointment types

This automated n8n workflow delivers an instant DevOps toolkit by installing Docker, K3s, Jenkins, Grafana, and more on a Linux server within 10 seconds. It optimizes performance, enhances security, and provides ready-to-use templates for DevOps projects. Main Components Configure Parameters** - Defines server details, tool versions, and credentials System Preparation** - Updates the system and installs base packages Install Docker** - Deploys Docker Engine and Docker Compose Install Kubernetes** - Sets up K3s cluster with kubectl, Helm, and k9s Install Jenkins** - Configures Jenkins CI/CD server with Docker integration Install Monitoring** - Deploys Prometheus and Grafana using Helm charts Create DevOps User** - Establishes a dedicated user with appropriate permissions Security Configuration** - Implements firewall, VS Code, and Terraform Final Configuration** - Sets up sample projects and configuration files Setup Complete** - Provides a summary and access details Essential Prerequisites Linux server with SSH access Root-level administrative privileges Customization Guide Adjust tool versions or credentials in the Configure Parameters node Modify the number of nodes or security settings as needed Features 🔧 Core DevOps Tools Installed: Docker - Container platform with Docker Compose Kubernetes - K3s (lightweight) with kubectl and Helm Jenkins - CI/CD automation server Prometheus - Monitoring and alerting Grafana - Visualization and dashboards ⚡ Optimizations Made: Streamlined Commands - Combined multiple operations into single bash scripts Reduced Nodes - 10 nodes vs 12 in original (more efficient) Better Error Handling - Each step includes verification Cloud-Ready - Includes AWS CLI, Azure CLI, and Google Cloud SDK Security First - Proper firewall configuration and user permissions Parameters to Configure server_host: Your Linux server IP address server_user: SSH username (typically 'root') server_password: SSH password docker_version: Docker version to install k3s_version: K3s version to install username: DevOps username user_password: Password for the DevOps user How to Use Copy the JSON code from the artifact Open your n8n workspace Select "Import from JSON" or "+" → "From JSON" Paste the JSON code Configure parameters in the "Configure Parameters" node with your server details Run the workflow Workflow Actions Install: Deploys Docker, K3s, Jenkins, Prometheus, and Grafana with optimizations Create User: Sets up a DevOps user with necessary permissions Configure: Applies security settings and provides templates

This automated n8n workflow continuously monitors airline schedule changes by fetching real-time flight data, comparing it with stored schedules, and instantly notifying both internal teams and affected passengers through multiple communication channels. The system ensures stakeholders are immediately informed of any flight delays, cancellations, gate changes, or other critical updates. Good to Know Flight data accuracy depends on the aviation API provider's update frequency and reliability Critical notifications (cancellations, major delays) trigger immediate passenger alerts via SMS and email Internal Slack notifications keep operations teams informed in real-time Database logging maintains a complete audit trail of all schedule changes The system processes only confirmed schedule changes to avoid false notifications Passenger notifications are sent only to those with confirmed tickets for affected flights How It Works Schedule Trigger - Automatically runs every 30 minutes to check for flight schedule updates Fetch Airline Data - Retrieves current flight information from aviation APIs Get Current Schedules - Pulls existing schedule data from the internal database Process Changes - Compares API data with database records to identify schedule changes Check for Changes - Determines if any updates require processing and notifications Update Database - Saves schedule changes to the internal flight database Notify Slack Channel - Sends operational updates to the flight operations team Check Urgent Notifications - Identifies critical changes requiring immediate passenger alerts Get Affected Passengers - Retrieves contact information for passengers on changed flights Send Email Notifications - Dispatches detailed schedule change emails via SendGrid Send SMS (Critical Only) - Sends urgent text alerts for cancellations and major delays Update Internal Systems - Syncs changes with other airline systems via webhooks Log Sync Activity - Records all synchronization activities for audit and monitoring Data Sources The workflow integrates with multiple data sources and systems: Aviation API (Primary Data Source) Real-time flight status and schedule data Departure/arrival times, gates, terminals Flight status (on-time, delayed, cancelled, diverted) Aircraft and route information Internal Flight Database flight_schedules table - Current schedule data with columns: flight_number (text) - Flight identifier (e.g., "AA123") departure_time (timestamp) - Scheduled departure time arrival_time (timestamp) - Scheduled arrival time status (text) - Flight status (active, delayed, cancelled, diverted) gate (text) - Departure gate number terminal (text) - Terminal identifier airline_code (text) - Airline IATA code origin_airport (text) - Departure airport code destination_airport (text) - Arrival airport code aircraft_type (text) - Aircraft model updated_at (timestamp) - Last update timestamp created_at (timestamp) - Record creation timestamp passengers table - Passenger contact information with columns: passenger_id (integer) - Unique passenger identifier name (text) - Full passenger name email (text) - Email address for notifications phone (text) - Mobile phone number for SMS alerts notification_preferences (json) - Communication preferences created_at (timestamp) - Registration timestamp updated_at (timestamp) - Last profile update tickets table - Booking and ticket status with columns: ticket_id (integer) - Unique ticket identifier passenger_id (integer) - Foreign key to passengers table flight_number (text) - Flight identifier flight_date (date) - Travel date seat_number (text) - Assigned seat ticket_status (text) - Status (confirmed, cancelled, checked-in) booking_reference (text) - Booking confirmation code fare_class (text) - Ticket class (economy, business, first) created_at (timestamp) - Booking timestamp updated_at (timestamp) - Last modification timestamp sync_logs table - Audit trail and system logs with columns: log_id (integer) - Unique log identifier workflow_name (text) - Name of the workflow that created the log total_changes (integer) - Number of schedule changes processed sync_status (text) - Status (completed, failed, partial) sync_timestamp (timestamp) - When the sync occurred details (json) - Detailed log information and changes error_message (text) - Error details if sync failed execution_time_ms (integer) - Processing time in milliseconds Communication Channels Slack - Internal team notifications SendGrid - Passenger email notifications Twilio - Critical SMS alerts Internal webhooks - System integrations How to Use Import the workflow into your n8n instance Configure aviation API credentials (AviationStack, FlightAware, or airline-specific APIs) Set up PostgreSQL database connection with required tables Configure Slack bot token for operations team notifications Set up SendGrid API key and email templates for passenger notifications Configure Twilio credentials for SMS alerts (critical notifications only) Test with sample flight data to verify all notification channels Adjust monitoring frequency and severity thresholds based on operational needs Monitor sync logs to ensure reliable data synchronization Requirements API Access Aviation data provider (AviationStack, FlightAware, etc.) SendGrid account for email delivery Twilio account for SMS notifications Slack workspace and bot token Database Setup PostgreSQL database with flight schedule tables Passenger and ticket management tables Audit logging tables for tracking changes Infrastructure n8n instance with appropriate node modules Reliable internet connection for API calls Proper credential management and security Customizing This Workflow Modify the Process Changes node to adjust change detection sensitivity, add custom business rules, or integrate additional data sources like weather or airport operational data. Customize notification templates in the email and SMS nodes to match your airline's branding and communication style. Adjust the Schedule Trigger frequency based on your operational requirements and API rate limits.

Social Media Sentiment Analysis Dashboard with AI and Real-time Monitoring 🎯 Target Audience Social media managers and community managers Marketing teams monitoring brand reputation PR professionals tracking public sentiment Customer service teams identifying trending issues Business analysts measuring social media ROI Brand managers protecting brand reputation Product managers gathering user feedback 🚀 Problem Statement Manual social media monitoring is overwhelming and often misses critical sentiment shifts or trending topics. This template solves the challenge of automatically collecting, analyzing, and visualizing social media sentiment data across multiple platforms to provide actionable insights for brand management and customer engagement. 🔧 How it Works This workflow automatically monitors social media platforms using AI-powered sentiment analysis, processes mentions and conversations, and provides real-time insights through a comprehensive dashboard. Key Components Scheduled Trigger - Runs the workflow at specified intervals to maintain real-time monitoring AI-Powered Sentiment Analysis - Uses advanced NLP to analyze sentiment, emotions, and topics Multi-Platform Integration - Monitors Twitter, Reddit, and other social platforms Real-time Alerting - Sends notifications for critical sentiment changes or viral content Dashboard Integration - Stores all data in Google Sheets for comprehensive analysis and reporting 📊 Google Sheets Column Specifications The template creates the following columns in your Google Sheets: | Column | Data Type | Description | Example | |--------|-----------|-------------|---------| | timestamp | DateTime | When the mention was recorded | "2024-01-15T10:30:00Z" | | platform | String | Social media platform | "Twitter" | | username | String | User who posted the content | "@john_doe" | | content | String | Full text of the post/comment | "Love the new product features!" | | sentiment_score | Number | Sentiment score (-1 to 1) | 0.85 | | sentiment_label | String | Sentiment classification | "Positive" | | emotion | String | Primary emotion detected | "Joy" | | topics | Array | Key topics identified | ["product", "features"] | | engagement | Number | Likes, shares, comments | 1250 | | reach_estimate | Number | Estimated reach | 50000 | | influence_score | Number | User influence metric | 0.75 | | alert_priority | String | Alert priority level | "High" | 🛠️ Setup Instructions Estimated setup time: 20-25 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Google Sheets account with API access Slack workspace for notifications (optional) Social media API access (Twitter, Reddit, etc.) Step-by-Step Configuration 1. Install Community Nodes Install required community nodes npm install n8n-nodes-scrapegraphai npm install n8n-nodes-slack 2. Configure ScrapeGraphAI Credentials Navigate to Credentials in your n8n instance Add new ScrapeGraphAI API credentials Enter your API key from ScrapeGraphAI dashboard Test the connection to ensure it's working 3. Set up Google Sheets Connection Add Google Sheets OAuth2 credentials Grant necessary permissions for spreadsheet access Create a new spreadsheet for sentiment analysis data Configure the sheet name (default: "Sentiment Analysis") 4. Configure Social Media Monitoring Update the websiteUrl parameters in ScrapeGraphAI nodes Add URLs for social media platforms you want to monitor Customize the user prompt to extract specific sentiment data Set up keywords, hashtags, and brand mentions to track 5. Set up Notification Channels Configure Slack webhook or API credentials Set up email service credentials for alerts Define sentiment thresholds for different alert levels Test notification delivery 6. Configure Schedule Trigger Set monitoring frequency (every 15 minutes, hourly, etc.) Choose appropriate time zones for your business hours Consider social media platform rate limits 7. Test and Validate Run the workflow manually to verify all connections Check Google Sheets for proper data formatting Test sentiment analysis with sample content 🔄 Workflow Customization Options Modify Monitoring Targets Add or remove social media platforms Change keywords, hashtags, or brand mentions Adjust monitoring frequency based on platform activity Extend Sentiment Analysis Add more sophisticated emotion detection Implement topic clustering and trend analysis Include influencer identification and scoring Customize Alert System Set different thresholds for different sentiment levels Create tiered alert systems (info, warning, critical) Add sentiment trend analysis and predictions Output Customization Add data visualization and reporting features Implement sentiment trend charts and graphs Create executive dashboards with key metrics Add competitor sentiment comparison 📈 Use Cases Brand Reputation Management**: Monitor and respond to brand mentions Crisis Management**: Detect and respond to negative sentiment quickly Customer Feedback Analysis**: Understand customer satisfaction and pain points Product Launch Monitoring**: Track sentiment around new product releases Competitor Analysis**: Monitor competitor sentiment and engagement Influencer Identification**: Find and engage with influential users 🚨 Important Notes Respect social media platforms' terms of service and rate limits Implement appropriate delays between requests to avoid rate limiting Regularly review and update your monitoring keywords and parameters Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Consider privacy implications and data protection regulations 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Google Sheets permission errors: Check OAuth2 scope and permissions Sentiment analysis errors: Review the Code node's JavaScript logic Rate limiting: Adjust monitoring frequency and implement delays Alert delivery failures: Check notification service credentials Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Google Sheets API documentation for advanced configurations Social media platform API documentation Sentiment analysis best practices and guidelines

Copyright Infringement Detector with ScrapeGraphAI Analysis and Legal Action Automation 🎯 Target Audience Intellectual property lawyers and legal teams Brand protection specialists Content creators and publishers Marketing and brand managers Digital rights management teams Copyright enforcement agencies Media companies and publishers E-commerce businesses with proprietary content Software and technology companies Creative agencies protecting client work 🚀 Problem Statement Manual monitoring for copyright infringement is time-consuming, often reactive rather than proactive, and can miss critical violations that damage brand reputation and revenue. This template solves the challenge of automatically detecting copyright violations, analyzing infringement patterns, and providing immediate legal action recommendations using AI-powered web scraping and automated legal workflows. 🔧 How it Works This workflow automatically scans the web for potential copyright violations using ScrapeGraphAI, analyzes content similarity, determines legal action requirements, and provides automated alerts for immediate response to protect intellectual property rights. Key Components Schedule Trigger - Runs automatically every 24 hours to monitor for new infringements ScrapeGraphAI Web Search - Uses AI to search for potential copyright violations across the web Content Comparer - Analyzes potential infringements and calculates similarity scores Infringement Detector - Determines legal action required and creates case reports Legal Action Trigger - Routes cases based on severity and urgency Brand Protection Alert - Sends urgent alerts for high-priority violations Monitoring Alert - Tracks medium-risk cases for ongoing monitoring 📊 Detection and Analysis Specifications The template monitors and analyzes the following infringement types: | Infringement Type | Detection Method | Risk Level | Action Required | |-------------------|------------------|------------|-----------------| | Exact Text Match | High similarity score (>80%) | High | Immediate cease & desist | | Paraphrased Content | Moderate similarity (50-80%) | Medium | Monitoring & evidence collection | | Unauthorized Brand Usage | Brand name detection in content | High | Legal consultation | | Competitor Usage | Known competitor domain detection | High | DMCA takedown | | Image/Video Theft | Visual content analysis | High | Immediate action | | Domain Infringement | Suspicious domain patterns | Medium | Investigation | 🛠️ Setup Instructions Estimated setup time: 30-35 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Telegram or other notification service credentials Legal team contact information Copyrighted content database Step-by-Step Configuration 1. Install Community Nodes Install required community nodes npm install n8n-nodes-scrapegraphai 2. Configure ScrapeGraphAI Credentials Navigate to Credentials in your n8n instance Add new ScrapeGraphAI API credentials Enter your API key from ScrapeGraphAI dashboard Test the connection to ensure it's working 3. Set up Schedule Trigger Configure the monitoring frequency (default: every 24 hours) Adjust timing to match your business hours Set appropriate timezone for your legal team 4. Configure Copyrighted Content Database Update the Content Comparer node with your protected content Add brand names, slogans, and unique phrases Include competitor and suspicious domain lists Set similarity thresholds for different content types 5. Customize Legal Action Rules Update the Infringement Detector node with your legal thresholds Configure action plans for different infringement types Set up case priority levels and response timelines Define evidence collection requirements 6. Set up Alert System Configure Telegram bot or other notification service Set up different alert types for different severity levels Configure legal team contact information Test alert delivery and formatting 7. Test and Validate Run the workflow manually with test search terms Verify all detection steps complete successfully Test alert system with sample infringement data Validate legal action recommendations 🔄 Workflow Customization Options Modify Detection Parameters Adjust similarity thresholds for different content types Add more sophisticated text analysis algorithms Include image and video content detection Customize brand name detection patterns Extend Legal Action Framework Add more detailed legal action plans Implement automated cease and desist generation Include DMCA takedown automation Add court filing preparation workflows Customize Alert System Add integration with legal case management systems Implement tiered alert systems (urgent, high, medium, low) Add automated evidence collection and documentation Include reporting and analytics dashboards Output Customization Add integration with legal databases Implement automated case tracking Create compliance reporting systems Add trend analysis and pattern recognition 📈 Use Cases Brand Protection**: Monitor unauthorized use of brand names and logos Content Protection**: Detect plagiarism and content theft Legal Enforcement**: Automate initial legal action processes Competitive Intelligence**: Monitor competitor content usage Compliance Monitoring**: Ensure proper attribution and licensing Evidence Collection**: Automatically document violations for legal proceedings 🚨 Important Notes Respect website terms of service and robots.txt files Implement appropriate delays between requests to avoid rate limiting Regularly review and update copyrighted content database Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Ensure compliance with local copyright laws and regulations Consult with legal professionals before taking automated legal action Maintain proper documentation for all detected violations 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status False positive detections: Adjust similarity thresholds and detection parameters Alert delivery failures: Check notification service credentials Legal action errors: Verify legal team contact information Schedule trigger failures: Check timezone and interval settings Content analysis errors: Review the Code node's JavaScript logic Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Copyright law resources and best practices Legal automation and compliance guidelines Brand protection and intellectual property resources

Real-Time ClickUp Time Tracking to HubSpot Project Sync This workflow automates the synchronization of time tracked on ClickUp tasks directly to a custom project object in HubSpot, ensuring your project metrics are always accurate and up-to-date. Use Case & Problem This workflow is designed for teams that use a custom object in HubSpot for high-level project overviews (tracking scoped vs. actual hours per sprint) but manage daily tasks and time logging in ClickUp. The primary challenge is the constant, manual effort required to transfer tracked hours from ClickUp to HubSpot, a process that is both time-consuming and prone to errors. This automation eliminates that manual work entirely. How It Works Triggers on Time Entry:** The workflow instantly starts whenever a user updates the time tracked on any task in a specified ClickUp space. ⏱️ Fetches Task & Time Details:** It immediately retrieves all relevant data about the task (like its name and custom fields) and the specific time entry that was just updated. Identifies the Project & Sprint:** The workflow processes the task data to determine which HubSpot project it belongs to and categorizes the work into the correct sprint (e.g., Sprint 1, Sprint 2, Additional Requests). Updates HubSpot in Real-Time:** It finds the corresponding project record in HubSpot and updates the master actual_hours_tracked property. It then intelligently updates the specific field for the corresponding sprint (e.g., actual_sprint_1_hours), ensuring your reporting remains granular and accurate. Requirements ✅ ClickUp Account with the following custom fields on your tasks: A Dropdown custom field named Sprint to categorize tasks. A Short Text custom field named HubSpot Deal ID or similar to link to the HubSpot record. ✅ HubSpot Account with: A Custom Object used for project tracking. Custom Properties** on that object to store total and sprint-specific hours (e.g., actual_hours_tracked, actual_sprint_1_hours, total_time_remaining, etc.). > Note: Since this workflow interacts with a custom HubSpot object, it uses flexible HTTP Request nodes instead of the standard n8n HubSpot nodes. Setup Instructions Configure Credentials: Add your ClickUp (OAuth2) and HubSpot (Header Auth with a Private App Token) credentials to the respective nodes in the workflow. Set ClickUp Trigger: In the Time Tracked Update Trigger node, select your ClickUp team and the specific space you want to monitor for time updates. Update HubSpot Object ID: Find the ID of your custom project object in HubSpot. In the HubSpot HTTP Request nodes (e.g., OnProjectFolder), replace the placeholder ID objectTypeId in the URL with your own objectTypeId How to Customize Adjust the Code: Extract Sprint & Task Data node to change how sprint names are mapped or how time is calculated. Update the URLs in the HubSpot HTTP Request nodes if your custom object or property names differ.

Description This automation template is designed for Instagram marketers, influencers, and businesses looking to supercharge their Instagram engagement strategy. It automatically monitors Instagram post comments and sends personalized direct messages (DMs) to new commenters, while maintaining a smart tracking system to prevent duplicate messages. The workflow runs continuously, checking for new comments every 15 minutes and responding instantly to maintain high engagement rates. Note: This workflow uses the upload-post.com API for Instagram interactions and Google Sheets for contact tracking. The workflow is configured to monitor a specific Instagram post* Who Is This For? Instagram Marketers & Influencers:** Automatically engage with every commenter by sending personalized DMs with valuable content, links, or offers. E-commerce Businesses:** Convert Instagram comments into sales opportunities by instantly sending product links, discount codes, or catalog information via DM. Content Creators & Coaches:** Build deeper relationships with your audience by automatically reaching out to commenters with additional resources, course links, or exclusive content. Social Media Managers:** Scale client engagement without manual monitoring, ensuring no potential lead or follower interaction goes unnoticed. What Problem Does This Workflow Solve? Manually monitoring Instagram comments and sending follow-up DMs is time-consuming and often leads to missed opportunities. This workflow addresses these challenges by: Automated Comment Monitoring:** Continuously checks for new comments on your specified Instagram post every 15 minutes. Smart Duplicate Prevention:** Uses Google Sheets to track already contacted users, preventing spam and maintaining professional communication. Instant Response System:** Sends personalized DMs immediately when new comments are detected, maximizing engagement while the interaction is fresh. Scalable Engagement:** Handles multiple commenters simultaneously without manual intervention, perfect for viral posts or high-engagement content. Comprehensive Tracking:** Maintains detailed logs of all interactions including timestamps, usernames, and message content for analytics and follow-up. How It Works Post Configuration: Set your Instagram post URL, reply message, and profile username in the configuration node. Comment Monitoring: The workflow fetches all comments from your specified Instagram post using the upload-post.com API. Smart Filtering: Compares new comments against your Google Sheets database to identify users who haven't been contacted yet. Automated DM Sending: Sends personalized direct messages to new commenters with your configured message. Contact Tracking: Records each successful interaction in Google Sheets with comment ID, username, message sent, timestamp, and post URL. Continuous Monitoring: Automatically repeats the process every 15 minutes using the built-in scheduler. Setup Upload-Post API Credentials: Create an account at upload-post.com connect your Instagram account and add your API credentials to the HTTP request nodes. Google Sheets Setup: Create a Google Sheet with columns: comment_id, username, message_sent, timestamp, post_url Connect your Google account to the Google Sheets nodes Update the document ID in the "Read Contacted Users" and "Record Contacted User" nodes Instagram Post Configuration: In the "Configure Post & Message" node, update: postUrl: Your Instagram post URL to monitor replyMessage: The DM message to send to commenters profileUsername: Your Upload-post profile username Monitoring Schedule: The workflow is set to run every 15 minutes. You can adjust this in the "Schedule Trigger" node based on your needs. Requirements Accounts:** n8n, upload-post.com, Google (for Sheets access), Instagram business account. API Keys & Credentials:** Upload-post.com API token, Google Sheets OAuth2 credentials. Instagram Setup:** Business/Creator account with API access through upload-post.com. Features Duplicate Prevention:** Advanced comment ID tracking prevents sending multiple DMs to the same user Error Handling:** Robust error handling for API failures and edge cases Detailed Logging:** Comprehensive console logging for debugging and monitoring Flexible Configuration:** Easy to modify for different posts, messages, and monitoring intervals Success Tracking:** Monitors both successful and failed DM attempts for analytics Use this template to transform your Instagram engagement strategy, automatically converting every comment into a potential lead or deeper connection while maintaining professional communication standards.

Smart IoT Device Health Monitor with AI-Powered Dashboard Analysis and Real-Time Alerting 🎯 Target Audience IT operations and infrastructure teams IoT system administrators and engineers Facility and building management teams Manufacturing and industrial operations managers Smart city and public infrastructure coordinators Healthcare technology administrators Energy and utilities monitoring teams Fleet and asset management professionals Security and surveillance system operators Property and facility maintenance teams 🚀 Problem Statement Monitoring hundreds of IoT devices across multiple dashboards is overwhelming and reactive, often leading to costly downtime, missed maintenance windows, and system failures. This template solves the challenge of proactive IoT device monitoring by automatically analyzing device health metrics, detecting issues before they become critical, and delivering intelligent alerts that help teams maintain optimal system performance. 🔧 How it Works This workflow automatically monitors your IoT dashboard every 30 minutes using AI-powered data extraction, analyzes device health patterns, calculates system-wide health scores, and sends intelligent alerts only when intervention is needed, preventing alert fatigue while ensuring critical issues are never missed. Key Components Schedule Trigger - Runs every 30 minutes for continuous device monitoring AI Dashboard Scraper - Uses ScrapeGraphAI to extract device data from any IoT dashboard without APIs Health Analyzer - Calculates system health scores and identifies problematic devices Smart Alert System - Sends notifications only when health drops below thresholds Telegram Notifications - Delivers formatted alerts with device details and recommendations Activity Logger - Maintains historical records for trend analysis and reporting 📊 Device Health Analysis Specifications The template monitors and analyzes the following device metrics: | Metric Category | Monitored Parameters | Analysis Method | Alert Triggers | Example Output | |-----------------|---------------------|-----------------|----------------|----------------| | Device Status | Online/Offline/Error | Real-time status check | Any offline devices | "Device-A01 is offline" | | Battery Health | Battery percentage | Low battery detection | Below 20% charge | "Sensor-B03 low battery: 15%" | | Temperature | Device temperature | Overheating detection | Above 70°C | "Gateway-C02 overheating: 75°C" | | System Health | Overall health score | Online device ratio | Below 80% health | "System health: 65%" | | Connectivity | Network status | Connection monitoring | Loss of communication | "3 devices offline" | | Performance | Response metrics | Trend analysis | Degraded performance | "Response time increasing" | 🛠️ Setup Instructions Estimated setup time: 15-20 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Telegram bot token and chat ID Access to your IoT dashboard URL Basic understanding of your device naming conventions Step-by-Step Configuration 1. Install Community Nodes Install required community nodes npm install n8n-nodes-scrapegraphai 2. Configure ScrapeGraphAI Credentials Navigate to Credentials in your n8n instance Add new ScrapeGraphAI API credentials Enter your API key from ScrapeGraphAI dashboard Test the connection to ensure it's working 3. Set up Schedule Trigger Configure the monitoring frequency (default: every 30 minutes) Adjust timing based on your operational needs: Every 15 minutes: */15 * * * * Every hour: 0 * * * * Every 5 minutes: */5 * * * * 4. Configure Dashboard URL Update the "Get Data" node with your IoT dashboard URL Customize the AI prompt to match your dashboard structure Test data extraction to ensure proper JSON formatting Adjust device field mappings as needed 5. Set up Telegram Notifications Create a Telegram bot using @BotFather Get your chat ID from @userinfobot Configure Telegram credentials in n8n Test message delivery to ensure alerts work 6. Customize Health Thresholds Adjust health score threshold (default: 80%) Set battery alert level (default: 20%) Configure temperature warning (default: 70°C) Customize alert conditions based on your requirements 7. Test and Validate Run the workflow manually with your dashboard Verify device data extraction accuracy Test alert conditions and message formatting Confirm logging functionality works correctly 🔄 Workflow Customization Options Modify Monitoring Frequency Adjust schedule for different device criticality levels Add business hours vs. off-hours monitoring Implement variable frequency based on system health Add manual trigger for on-demand monitoring Extend Device Analysis Add more device metrics (memory, CPU, network bandwidth) Implement predictive maintenance algorithms Include environmental sensors (humidity, air quality) Add device lifecycle and warranty tracking Customize Alert Logic Implement escalation rules for critical alerts Add alert suppression during maintenance windows Create different alert channels for different severity levels Include automated ticket creation for persistent issues Output Customization Add integration with monitoring platforms (Grafana, Datadog) Implement email notifications for management reports Create executive dashboards with health trends Add integration with maintenance management systems 📈 Use Cases Industrial IoT Monitoring**: Track manufacturing equipment and sensors Smart Building Management**: Monitor HVAC, lighting, and security systems Fleet Management**: Track vehicle telematics and diagnostic systems Healthcare Device Monitoring**: Ensure medical device uptime and performance Smart City Infrastructure**: Monitor traffic lights, environmental sensors, and public systems Energy Grid Monitoring**: Track smart meters and distribution equipment 🚨 Important Notes Respect your dashboard's terms of service and rate limits Implement appropriate delays between requests to avoid overloading systems Regularly review and update device thresholds based on operational experience Monitor ScrapeGraphAI API usage to manage costs effectively Keep your credentials secure and rotate them regularly Ensure alert recipients are available to respond to critical notifications Consider implementing backup monitoring systems for critical infrastructure Maintain device inventories and update monitoring parameters as systems evolve 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Dashboard access issues: Check URL accessibility and authentication requirements Data extraction failures: Review AI prompt and dashboard structure changes Missing device data: Verify device naming conventions and field mappings Alert delivery failures: Check Telegram bot configuration and chat permissions False alerts: Adjust health thresholds and alert logic conditions Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Telegram Bot API documentation IoT platform-specific monitoring best practices Device manufacturer monitoring guidelines Industrial IoT monitoring standards and frameworks

Smart Blockchain Monitor with ScrapeGraphAI Risk Detection and Instant Alerts 🎯 Target Audience Cryptocurrency traders and investors DeFi protocol managers and developers Blockchain security analysts Financial compliance officers Crypto fund managers and institutions Risk management teams Blockchain developers monitoring smart contracts Digital asset custodians 🚀 Problem Statement Manual blockchain monitoring is time-consuming and prone to missing critical events, often leading to delayed responses to high-value transactions, security threats, or unusual network activity. This template solves the challenge of real-time blockchain surveillance by automatically detecting, analyzing, and alerting on significant blockchain events using AI-powered intelligence and instant notifications. 🔧 How it Works This workflow automatically monitors blockchain activity in real-time, uses ScrapeGraphAI to intelligently extract transaction data from explorer pages, performs sophisticated risk analysis, and instantly alerts your team about significant events across multiple blockchains. Key Components Blockchain Webhook - Real-time trigger that activates when new blocks are detected Data Normalizer - Standardizes blockchain data across different networks ScrapeGraphAI Extractor - AI-powered transaction data extraction from blockchain explorers Risk Analyzer - Advanced risk scoring based on transaction patterns and values Smart Filter - Intelligently routes only significant events for alerts Slack Alert System - Instant formatted notifications to your team 📊 Risk Analysis Specifications The template performs comprehensive risk analysis with the following parameters: | Risk Factor | Threshold | Score Impact | Description | |-------------|-----------|--------------|-------------| | High-Value Transactions | >$10,000 USD | +15 per transaction | Individual transactions exceeding threshold | | Block Volume | >$1M USD | +20 points | Total block transaction volume | | Block Volume | >$100K USD | +10 points | Moderate block transaction volume | | Failure Rate | >10% | +15 points | Percentage of failed transactions in block | | Multiple High-Value | >3 transactions | Alert trigger | Multiple large transactions in single block | | Critical Failure Rate | >20% | Alert trigger | Extremely high failure rate indicator | Risk Levels: High Risk**: Score ≥ 50 (Immediate alerts) Medium Risk**: Score ≥ 25 (Standard alerts) Low Risk**: Score < 25 (No alerts) 🌐 Supported Blockchains | Blockchain | Explorer | Native Support | Transaction Detection | |------------|----------|----------------|----------------------| | Ethereum | Etherscan | ✅ Full | High-value, DeFi, NFT | | Bitcoin | Blockchair | ✅ Full | Large transfers, institutional | | Binance Smart Chain | BscScan | ✅ Full | DeFi, high-frequency trading | | Polygon | PolygonScan | ✅ Full | Layer 2 activity monitoring | 🛠️ Setup Instructions Estimated setup time: 15-20 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Slack workspace with webhook or bot token Blockchain data source (Moralis, Alchemy, or direct node access) Basic understanding of blockchain explorers Step-by-Step Configuration 1. Install Community Nodes Install required community nodes npm install n8n-nodes-scrapegraphai 2. Configure ScrapeGraphAI Credentials Navigate to Credentials in your n8n instance Add new ScrapeGraphAI API credentials Enter your API key from ScrapeGraphAI dashboard Test the connection to ensure proper functionality 3. Set up Slack Integration Add Slack OAuth2 or webhook credentials Configure your target channel for blockchain alerts Test message delivery to ensure notifications work Customize alert formatting preferences 4. Configure Blockchain Webhook Set up the webhook endpoint for blockchain data Configure your blockchain data provider (Moralis, Alchemy, etc.) Ensure webhook payload includes block number and blockchain identifier Test webhook connectivity with sample data 5. Customize Risk Parameters Adjust high-value transaction threshold (default: $10,000) Modify risk scoring weights based on your needs Configure blockchain-specific risk factors Set failure rate thresholds for your use case 6. Test and Validate Send test blockchain data to trigger the workflow Verify ScrapeGraphAI extraction accuracy Check risk scoring calculations Confirm Slack alerts are properly formatted and delivered 🔄 Workflow Customization Options Modify Risk Analysis Adjust high-value transaction thresholds per blockchain Add custom risk factors (contract interactions, specific addresses) Implement whitelist/blacklist address filtering Configure time-based risk adjustments Extend Blockchain Support Add support for additional blockchains (Solana, Cardano, etc.) Customize explorer URL patterns Implement chain-specific transaction analysis Add specialized DeFi protocol monitoring Enhance Alert System Add email notifications alongside Slack Implement severity-based alert routing Create custom alert templates Add alert escalation rules Advanced Analytics Add transaction pattern recognition Implement anomaly detection algorithms Create blockchain activity dashboards Add historical trend analysis 📈 Use Cases Crypto Trading**: Monitor large market movements and whale activity DeFi Security**: Track protocol interactions and unusual contract activity Compliance Monitoring**: Detect suspicious transaction patterns Institutional Custody**: Alert on high-value transfers and security events Smart Contract Monitoring**: Track contract interactions and state changes Market Intelligence**: Analyze blockchain activity for trading insights 🚨 Important Notes Respect ScrapeGraphAI API rate limits and terms of service Implement appropriate delays to avoid overwhelming blockchain explorers Keep your API credentials secure and rotate them regularly Monitor API usage to manage costs effectively Consider blockchain explorer rate limits for high-frequency monitoring Ensure compliance with relevant financial regulations Regularly update risk parameters based on market conditions 🔧 Troubleshooting Common Issues: ScrapeGraphAI extraction errors: Check API key and account status Webhook trigger failures: Verify webhook URL and payload format Slack notification failures: Check bot permissions and channel access False positive alerts: Adjust risk scoring thresholds Missing transaction data: Verify blockchain explorer accessibility Rate limit errors: Implement delays and monitor API usage Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Blockchain explorer API documentation Slack API documentation for advanced configurations Cryptocurrency compliance and regulatory guidelines