Workflow Templates

Bulk Archival Decryption: AI-Powered Compliance Audit & Unlocking Engine 🎯 Description This is a high-security enterprise solution for LegalOps and Compliance departments. It automates the high-stakes process of decrypting batches of legacy, password-protected PDF assets to facilitate forensic indexing and search discovery. By utilizing the HTML to PDF (Unlock) node, it bridges the gap between inaccessible "dark data" and searchable compliance archives. ✨ The Sovereign Lifecycle Phase 1: Secure Intake & Forensic Fingerprinting - Fetches legacy binaries and generates an initial SHA-256 hash. This establishes a strict "Chain of Custody" before any modification occurs. Phase 2: Key Vault Orchestration - Integrates with a PostgreSQL Key Vault to automatically retrieve historical passwords based on file metadata, enabling the decryption of thousands of documents without manual intervention. Phase 3: Atomic Unlock sovereign - Utilizes the HTML to PDF (Unlock) node to strip owner and user passwords with cryptographic precision, restoring full text-extraction and print capabilities. Phase 4: Integrity Guard & Validation - A dedicated Code Node compares the original and unlocked file hashes to verify successful decryption. It calculates processing latency and logs comprehensive integrity statuses. Phase 5: Compliance-Ready Archival - Unlocked files are vaulted in a secure Google Drive "Audit Vault." Forensic metadata, including custody IDs and timestamps, are permanently logged to PostgreSQL. Phase 6: Incident Management - Features a robust error-handling "Quarantine Zone." Failed decryptions or invalid keys trigger immediate Slack alerts and moves the problematic files to a restricted folder for manual legal review. 💡 Key Technical Features Forensic Audit Trail:** Every processing event is recorded with non-repudiation tokens for regulatory auditors. Bi-Directional Governance:** Simultaneously updates PostgreSQL audit logs while managing physical file movements in cloud storage. Fail-Safe Validation:** Implements strict MIME-type and file-size validation to prevent the processing of malicious or corrupted binaries. 🚀 Benefits ✅ Zero Audit Gaps - Ensures 100% of legacy data is accessible for regulatory search and indexing. ✅ Reduced Legal Risk - Provides an immutable record of the decryption process for internal and external discovery. ✅ Instant Discovery - Drastically reduces the time required to "open" legacy archives for litigation or audits. Tags: #legalops #compliance #pdf-unlock #audit #governance #enterprise #cybersecurity Category: Legal & Compliance | Difficulty: Advanced

Name: AI Chatbot for Max Messenger with Voice Recognition (GigaChat + Sber) Description: How it works This workflow powers an intelligent, conversational AI bot for Max messenger that can understand and respond to both text and voice messages. The bot uses GigaChat AI with built-in memory, allowing it to remember the conversation history for each unique user and answer follow-up questions. Voice messages are transcribed using Sber SmartSpeech. It's a complete solution for creating an engaging, automated assistant within your Max bot, using Russian AI services. Step-by-step Max Trigger:* The workflow starts when the *Max Trigger** node receives a new message sent to your Max bot. Access Control:* The *Check User** node verifies the sender's user ID against an allowed list. This prevents unauthorized users from accessing your bot. Access Denied Response:* If the user is not authorized, the *Access Denied** node sends a polite rejection message. Message Type Routing:* The *Text/Attachment** (Switch) node checks if the message contains plain text or has attachments (voice, photo, file). Attachment Processing:* If an attachment is detected, the *Download Attachment* (HTTP Request) node retrieves it, and the *Attachment Router** (Switch) node determines its type (voice, photo, or file). Voice Transcription:* For voice messages, the workflow gets a Sber access token via *Get Access Token* (HTTP Request), merges it with the audio file, and sends it to *Get Response** (HTTP Request) which uses Sber SmartSpeech API to transcribe the audio to text. Input Unification:* The *Voice to Prompt* node converts transcribed text into a prompt, while *Text to Prompt* does the same for plain text messages. Both paths merge at the *Combine** node. AI Agent Processing:* The unified prompt is passed to the *AI Agent, powered by **GigaChat Model and using Simple Memory to retain the last 10 messages per user (using Max user_id as the session key). Response Delivery:* The AI-generated response is sent back to the user via the *Send Message** node. Set up steps Estimated set up time: 15 minutes Get Max bot credentials: Visit https://business.max.ru/ to create a bot and obtain API credentials. Add these credentials to Max Trigger, Send Message, and Access Denied nodes. Add GigaChat credentials: Register for GigaChat API access and add your credentials to the GigaChat Model node. Add Sber credentials: Obtain Sber SmartSpeech API credentials and add them to Get Access Token and Get Response nodes (HTTP Header Auth). Configure access control: Open the Check User node and change the user_id value (currently 50488534) to your own Max user ID. This ensures only you can use the bot during testing. Customize bot personality: Open the AI Agent node and edit the system message to change the bot's name, behavior, and add your own contact information or links. Test the bot: Activate the workflow and send a text or voice message to your Max bot to verify it responds correctly. Notes This workflow is specifically designed for Russian-speaking users and uses Russian AI services (GigaChat and Sber SmartSpeech) as alternatives to OpenAI. Make sure you have valid API access to both services before setting up this workflow.

👗 Virtual Try-On Telegram Bot — AI Outfit Try-On via Telegram with Async Job Polling Try on any outfit virtually - right inside Telegram. A user sends a person photo, then a garment photo (captioned garment), and the bot replies with an AI-generated try-on result image using a dedicated Virtual Try-On API. No app. No frontend. Just a Telegram chat. 🎯 Who Is This For? Fashion e-commerce brands** wanting to offer a try-before-you-buy experience via a simple chat interface Telegram bot developers** looking for a real-world async API polling pattern with state management AI product builders** who want a plug-and-play virtual fitting room for their audience Boutiques & clothing stores** wanting to let customers preview outfits before ordering ⚙️ What This Workflow Does Receives a Telegram message and checks if it contains a photo Person photo (sent without caption) → saves chat_id + file_id to Google Sheets as session state Garment photo (sent with caption garment) → looks up the saved person photo, resolves both Telegram file URLs, downloads both images, and submits a try-on job to the API Async polling loop → waits 15 seconds, checks job status, loops until completed or failed Sends the result back to the user as a photo in Telegram, then cleans up the sheet row 🔑 Credentials & APIs Required | Service | Purpose | Where to Get It | |---|---|---| | Telegram Bot Token | Receive messages & send results | Create a bot via @BotFather on Telegram → /newbot | | Virtual Try-On API Key | Submit and poll try-on jobs | Sign up at your Try-On API provider (e.g. try-on api or your own deployment) | | Google Sheets OAuth2 | Store per-user session state between messages | Connect via n8n's built-in Google Sheets OAuth2 credential | > 💡 Why Google Sheets for state? Each Telegram message triggers a separate workflow execution - there's no shared memory between runs. Google Sheets acts as a lightweight session store using chat_id as the unique key. 🛠️ Setup Instructions Step 1 - Telegram Bot Open Telegram and message @BotFather Run /newbot, follow the prompts, copy your Bot Token Paste the token into the ⚙️ Config node under botToken Step 2 - Google Sheet Create a new Google Sheet Add a tab named exactly tryon-state Add two column headers in row 1: chat_id and person_file_id Copy the Sheet ID from the URL (docs.google.com/spreadsheets/d/{THIS_PART}/edit) Paste it into the ⚙️ Config node under sheetId Step 3 - Try-On API Obtain your API key from your Virtual Try-On provider Set tryonApiKey in the ⚙️ Config node If your API base URL differs, also update tryonApiBase Verify the API accepts POST /api/v1/tryon with multipart/form-data fields person_images and garment_images, and returns { jobId, statusUrl } - adjust the Submit Try-On Job and Check Job Status nodes if your provider's schema differs Step 4 - n8n Credentials Assign your Telegram credential to all Telegram nodes (Trigger + all send nodes) Assign your Google Sheets OAuth2 credential to all Google Sheets nodes Activate** the workflow - the Telegram Trigger only works when the workflow is active 🔄 How the Polling Loop Works The Try-On API is asynchronous - submitting a job returns a jobId, not the result immediately. The workflow: Submits the job → gets jobId Waits 15 seconds Calls the status endpoint: GET /api/v1/tryon/status/{jobId} If status === "completed" → downloads and sends the result image If status === "failed" → sends an error message to the user Otherwise → loops back to the wait step Typical total wait: 15–60 seconds depending on the API and queue load. 🎨 How to Customize Change the garment trigger keyword* → Edit the caption check in *Is Garment Photo?** node from garment to anything you like (e.g. outfit, try, wear) Add a category/garment type input** → Extend the caption parsing to accept values like top, dress, jacket and pass them as an extra field to your Try-On API if supported Use a different state store* → Swap Google Sheets for Airtable, Supabase, or Redis if you have higher traffic needs; just replace the *Save Person to Sheet* and *Lookup Person from Sheet** nodes Add a paid/subscription gate** → Before saving the person photo, check a users sheet or database to see if the chat_id has remaining credits Support multiple garments in one session** → Instead of deleting the person row after each try-on, keep it for 24 hours so users can try multiple garments without re-uploading the person photo Add a result watermark* → Insert an n8n Code node after *HTTP Request** (result download) to overlay your brand logo before sending 📋 Workflow Nodes Overview | Node | Role | |---|---| | Telegram Trigger | Listens for incoming messages | | Extract Message Info | Pulls out chat_id, caption, hasPhoto, fileId | | ⚙️ Config | Single place for all configurable values — edit this first | | Has Photo? | Routes messages that contain a photo | | Is Garment Photo? | Checks if caption equals garment | | Save Person to Sheet | Stores person file_id keyed by chat_id | | Lookup Person from Sheet | Retrieves saved person photo when garment arrives | | Has Person Saved? | Guards against out-of-order photos | | Collect IDs | Bundles all required IDs for downstream nodes | | Get Person/Garment File Path | Resolves Telegram file_id → file_path via getFile API | | Download Person/Garment Image | Downloads actual image binary from Telegram CDN | | Submit Try-On Job | POSTs both images to the Try-On API | | Wait 15 Seconds | Gives the API time before first status check | | Check Job Status | Polls job status endpoint | | Is Job Complete? / Is Job Failed? | Routes to success or error path | | HTTP Request (result download) | Downloads the result image as binary | | Send Result Photo | Sends the AI try-on image back to the user | | Delete Row from Sheet | Cleans up session state after successful result | ⚠️ Notes & Gotchas Telegram file URLs expire** - the workflow resolves and downloads images immediately; never store Telegram download URLs for later use The bot must be activated** for the Telegram webhook to register - n8n does not receive messages while the workflow is inactive Google Sheets appendOrUpdate** uses chat_id as the matching key, so a user can retake their person photo any time and it will overwrite the old entry Pinned test data** is included in Submit Try-On Job and Merge State nodes for local testing — remove or disable these pins before going live 🔗 Useful Links Telegram BotFather — Create your bot n8n Telegram node docs n8n Google Sheets node docs n8n HTTP Request node docs n8n Wait node docs n8n Community Forum

Automatically turn top performing Instagram reels into 7 new ready to use content scripts. This workflow scrapes high performing posts from a chosen Instagram profile, downloads and transcribes the reel audio, analyzes the transcript patterns with AI, and generates 7 fresh script ideas based on what is already working. The final scripts are written into a Google Doc with clean formatting and clear headings, so you can easily review, refine, and publish them. Ideal for creators, social media managers, and marketing teams looking to scale short form content production.

This workflow lets you scrape and convert youtube video transcript to twitter thread using Google sheets, Apify and RapidAPI Workflow summary Get unprocessed youtube video link from google sheet Use youtube trascript scraper to extract transcript from youtube video Feed the youtube transcript to OpenAI model with advanced prompt to convert the transcript to twitter friendly posts/thread Post the thread to 𝕏 from your account using your cookies for authentication Mark the video as Processed so that it won't be processed again Features Fast and cheap (mostly free) APIs Advanced prompts to avoid tweets sound like AI converted from youtube Customizable AI Model Prompt used System: You are an expert Twitter thread creator. Your task is to convert YouTube video transcripts into engaging, viral-worthy Twitter threads. Each tweet must be under 280 characters, punchy, and valuable on its own. Use emojis sparingly for emphasis and include line breaks for readability. Always create a hook tweet, 5-10 insight tweets, and a call-to-action tweet. User: Convert this YouTube video transcript into a Twitter thread. Make sure it doesn't sound like it is direct conversion from youtube. For example do not make sentences that indicates you are perforing an activity in presense tense. Create a thread with: A hook tweet that grabs attention. Show results first with numbers 5-10 tweets breaking down key insights A final call-to-action tweet Return as structured JSON with the thread array. Video Title: {{Title}} Transcript: {{Transcript}}

> This n8n workflow template uses a community node and is only compatible with the self-hosted version of n8n. Who's it for DevOps teams, platform engineers, and ops leads who use Slack bots for infrastructure tasks but need to control who can trigger what. Ideal for teams that have outgrown hardcoded IF-node permission checks. How it works A team member @mentions the bot in Slack with a DevOps request like "restart staging" or "deploy to production." OpenAI classifies the message into a structured action and resource. Permit.io then checks if that Slack user's role allows the action. If permitted, the workflow executes the action and posts the result. If denied, the bot responds with the user's current permissions and suggests who can help. How to set up Install the @permitio/n8n-nodes-permitio community node Create a Slack app with app_mentions:read, chat:write, channels:read, users:read scopes Add your OpenAI API key In Permit.io, create resources (logs, staging, production, secrets) with actions (view, deploy, restart, rotate) and roles (viewer, developer, sre, admin) Sync your team's Slack user IDs as users in Permit.io and assign roles Requirements Self-hosted n8n instance Slack workspace with a bot app OpenAI API key Permit.io account (free tier works) How to customize Replace the mock HTTP Request node with your actual infrastructure endpoints (GitHub Actions, ArgoCD, Jenkins, etc.) Add ABAC conditions in Permit.io for time-based or context-based rules without changing the workflow Extend with additional resources and actions to match your team's operations

This n8n template implements an AI-Powered Chatbot for Automated WHMCS Support Ticket Creation Who's it for This template is designed for web hosting companies, domain registrars, and IT service providers who want to automate their customer support ticket creation process. It's perfect for businesses looking to streamline support operations by automatically converting customer chat conversations into structured WHMCS support tickets while maintaining professional, empathetic customer interactions. How it works / What it does This workflow creates an AI-powered chatbot that automatically converts customer chat messages into structured support tickets within the WHMCS system. The AI agent automatically: Receives customer queries through a webhook endpoint Processes natural language requests using Google Gemini AI Extracts key information from customer conversations: Customer name and email Issue description and subject Appropriate support department Priority level (Low, Medium, High) Fetches valid support departments from WHMCS using the GetSupportDepartments API Creates structured support tickets via WHMCS OpenTicket API Maintains conversation context with session-based memory Provides professional responses while gathering necessary information The system ensures 100% accuracy by always mapping to valid WHMCS departments and never inventing ticket fields, maintaining data integrity and proper ticket routing. How to set up 1. Configure Google Gemini API Set up your Google Gemini API credentials in the Google Gemini Chat Model node Ensure you have sufficient API quota for your expected usage 2. Configure WHMCS API Update the WHMCS API credentials in both HTTP Request Tool nodes Replace https://WHMCS_URL.com/includes/api.php with your actual WHMCS API endpoint Ensure your WHMCS API has the necessary permissions for: GetSupportDepartments action OpenTicket action 3. Customize AI Agent Behavior Modify the system message in the AI Agent node to match your company's tone and policies Adjust the agent's response style and ticket creation workflow Customize department mapping and priority assignment logic 4. Set up the Webhook The workflow creates a unique webhook endpoint for receiving customer queries Use this endpoint URL in your customer-facing chat interface Ensure proper security measures for webhook access 5. Test Department Integration Verify that the GetSupportDepartments API call returns your actual support departments Test ticket creation with various customer scenarios Ensure proper error handling for API failures Requirements Google Gemini API account** with appropriate credentials n8n instance** (self-hosted or cloud) WHMCS installation** with API access enabled Support department structure** already configured in WHMCS Customer chat interface** or messaging system How to customize the workflow Modify AI Agent Behavior Edit the system message in the AI Agent node to change the bot's personality and response style Adjust ticket creation logic and required field validation Customize priority assignment algorithms based on keywords or urgency indicators Enhance Ticket Creation Add custom fields to the ticket creation process Implement ticket categorization based on conversation content Add automatic assignment to specific support staff members Improve Customer Experience Add ticket confirmation and tracking information Implement follow-up message scheduling Add customer satisfaction surveys after ticket resolution Security Enhancements Implement API key rotation and monitoring Add request validation and sanitization Set up usage analytics and abuse prevention Key Features Automatic ticket creation** from natural language conversations Intelligent department mapping** using WHMCS API Professional customer interaction** with empathetic responses Session-based memory** for contextual conversations Structured ticket data** with proper validation Priority assignment** based on conversation analysis Scalable webhook architecture** for high-volume usage Direct WHMCS integration** for seamless ticket management Use Cases 24/7 automated support ticket creation** for web hosting companies Customer service automation** with human-like interaction Support team efficiency** by reducing manual ticket entry Consistent ticket formatting** across all customer interactions Improved response times** through immediate ticket creation Customer self-service** with professional guidance Chat Session Management The workflow automatically manages chat sessions with the following features: Unique Session IDs** for each customer conversation Automatic information extraction** from customer messages Conversation history tracking** with chronological message storage Session persistence** across multiple interactions Contextual responses** based on conversation history Example Customer Interactions The AI agent can handle various customer scenarios: Technical Issues**: "My website is down" → Creates ticket in Technical Support department Billing Questions**: "I need help with my invoice" → Creates ticket in Billing department Domain Services**: "I want to transfer my domain" → Creates ticket in Domain Services department General Support**: "I have a question about my hosting plan" → Creates ticket in General Support department Ticket Creation Process The workflow follows a structured approach: Information Gathering: The AI agent identifies missing required information (email, name, etc.) Department Selection: Fetches available departments from WHMCS and maps customer needs appropriately Priority Assessment: Determines ticket priority based on urgency indicators in the conversation Ticket Creation: Generates a well-structured ticket with clear subject and detailed message Confirmation: Provides customer with ticket creation confirmation and next steps This template transforms your web hosting business by providing instant, automated support ticket creation while maintaining the personal touch that customers expect from professional service providers. The AI agent becomes an extension of your support team, handling routine inquiries and ensuring no customer request goes unaddressed.

Send VAPI voice requests into n8n with memory and OpenAI for conversational automation This template shows how to capture voice interactions from VAPI (Voice AI Platform), send them into n8n via a webhook, process them with OpenAI, and maintain context with memory. The result is a conversational AI agent that responds back to VAPI with short, business-focused answers. ✅ What this template does Listens for POST requests from VAPI containing the session ID and user query Extracts session ID and query for consistent conversation context Uses OpenAI (GPT-4.1-mini) to generate conversational replies Adds Memory Buffer Window so each VAPI session maintains history Returns results to VAPI in the correct JSON response format 👤 Who’s it for Developers and consultants building voice-driven assistants Businesses wanting to connect VAPI calls into automation workflows Anyone who needs a scalable voice → AI → automation pipeline ⚙️ How it works Webhook node catches incoming VAPI requests Set node extracts session_id and user_query from the request body OpenAI Agent generates short, conversational replies with your business context Memory node keeps conversation history across turns Respond to Webhook sends results back to VAPI in the required JSON schema 🔧 Setup instructions Step 1: Create Function Tool in VAPI In your VAPI dashboard, create a new Function Tool Name: send_to_n8n Description: Send user query and session data to n8n workflow Parameters: session_id (string, required) – Unique session identifier user_query (string, required) – The user’s question Server URL: https://your-n8n-instance.com/webhook/vapi-endpoint Step 2: Configure Webhook in n8n Add a Webhook node Set HTTP method to POST Path: /webhook/vapi-endpoint Save, activate, and copy the webhook URL Use this URL in your VAPI Function Tool configuration Step 3: Create VAPI Assistant In VAPI, create a new Assistant Add the send_to_n8n Function Tool Configure the assistant to call this tool on user requests Test by making a voice query — you should see n8n respond 📦 Requirements An OpenAI API key stored in n8n credentials A VAPI account with access to Function Tools A self-hosted or cloud n8n instance with webhook access 🎛 Customization Update the system prompt in the OpenAI Agent node to reflect your brand’s voice Swap GPT-4.1-mini for another OpenAI model if you need longer or cheaper responses Extend the workflow by connecting to CRMs, Slack, or databases 📬 Contact Need help customizing this (e.g., filtering by campaign, connecting to CRMs, or formatting reports)? 📧 rbreen@ynteractive.com 🔗 https://www.linkedin.com/in/robert-breen-29429625/ 🌐 https://ynteractive.com

This n8n template implements a Customer Support Chat Agent for Web Hosting Companies with Google Gemini, Google Sheets Knowledge base and WHMCS API to Check Domain Name Availability Who's it for This template is designed for web hosting companies, domain registrars, and IT service providers who want to automate their customer support with an AI-powered chatbot. It's perfect for businesses looking to provide 24/7 customer assistance for hosting plans, domain services, and technical support while maintaining a professional, human-like interaction experience. How it works / What it does This workflow creates an AI-powered customer support chatbot that provides comprehensive assistance for web hosting and domain services. The AI agent (named Matt) automatically: Receives customer queries through a webhook endpoint Captures customer information (name and email) at the start of each session Processes natural language requests using Google Gemini AI Accesses real-time information from multiple Google Sheets knowledge bases: Shared Hosting Plans (pricing, features, specifications) Domain Prices (registration, transfer, renewal costs) Hosting Features (technical capabilities and specifications) FAQs (common questions and answers) Payment Method Details (accepted payment options) Company Offerings (available products and services) Checks domain availability via WHMCS API integration Provides accurate, contextual responses based on the knowledge base Maintains conversation history with session-based memory Stores complete chat sessions in Google Sheets for analysis and follow-up The system ensures 100% accuracy by only providing information that exists in the knowledge base, eliminating guesswork and maintaining brand consistency. How to set up 1. Configure Google Sheets Knowledge Base Set up a Google Sheets document with the following sheets: Shared_Hosting_Plans: Hosting plan details, pricing, and specifications Domain_Prices: Domain registration and renewal pricing Hosting_Features: Technical features and capabilities FAQs: Frequently asked questions and answers Payment_Method_Details: Payment options and instructions Offerings: Available products and services Update the Google Sheets credentials in each tool node 2. Set up Google Gemini API Configure your Google Gemini API credentials in the Google Gemini Chat Model node Ensure you have sufficient API quota for your expected usage 3. Configure WHMCS API (Optional) Replace Your_WHMCS_Identifier with your actual WHMCS API identifier Replace Your_WHMCS_Secret with your actual WHMCS API secret Update https://your_whmcs_url.com/includes/api.php with your WHMCS domain This enables domain availability checking for customers 4. Set up Chat Storage Create a Google Sheet for storing chat inquiries Update the document ID and credentials in the Chat_Inquiries node This will automatically store all customer conversations for analysis 5. Deploy the Webhook The workflow creates a unique webhook endpoint for receiving customer queries Use this endpoint URL in your customer-facing application or chat interface Requirements Google Sheets account** with the knowledge base set up Google Gemini API account** with appropriate credentials n8n instance** (self-hosted or cloud) WHMCS installation** (optional, for domain availability checking) Web hosting or domain services business** How to customize the workflow Modify AI Agent Behavior Edit the system message in the AI Agent node to change the bot's personality and response style Adjust response length and tone to match your brand voice Customize the agent's name (currently "Matt") Enhance Knowledge Base Add more Google Sheets tools for additional information sources Include product catalogs, pricing tables, or technical documentation Add multi-language support for international customers Improve Customer Experience Add domain suggestion algorithms based on customer input Integrate with your existing customer database for personalized recommendations Add notification systems (email, Slack, SMS) for high-value inquiries Security Enhancements Implement API key rotation and monitoring Add request validation and sanitization Set up usage analytics and abuse prevention Key Features Real-time information access** from Google Sheets knowledge base AI-powered natural language processing** for customer queries Session-based memory** for contextual conversations Automatic domain availability checking** via WHMCS API Professional, customer-focused responses** that maintain brand standards Complete chat history storage** for analysis and follow-up Scalable webhook architecture** for high-volume usage Multi-tool integration** for comprehensive customer support Use Cases 24/7 customer support automation** for web hosting companies Sales team assistance** with real-time product information Customer self-service portals** with intelligent assistance Lead generation** through proactive service recommendations Customer retention** via improved support experience Support ticket reduction** by handling common queries automatically Chat Session Management The workflow automatically manages chat sessions with the following features: Unique Session IDs** for each customer conversation Automatic customer information capture** (name and email) Conversation history tracking** with chronological message storage Session persistence** across multiple interactions Data export** to Google Sheets for analysis and follow-up Example Customer Interactions The AI agent can handle various customer scenarios: Hosting Plan Inquiries**: Detailed information about shared hosting plans, features, and pricing Domain Services**: Domain availability checking, pricing, and registration guidance Technical Support**: Feature explanations, setup guidance, and troubleshooting Payment Information**: Accepted payment methods and transaction processes General Support**: Company information, service offerings, and FAQ responses This template transforms your web hosting business by providing instant, accurate customer support while maintaining the personal touch that customers expect from professional service providers. The AI agent becomes an extension of your support team, handling routine inquiries and allowing human agents to focus on complex technical issues.

Automated Helpdesk Ticket Alerts to Microsoft Teams from Easy Redmine Intro/Overview This workflow automatically posts a Microsoft Teams message whenever a new helpdesk ticket is created in Easy Redmine. It’s perfect for IT teams who want real-time visibility into new issues without constantly checking ticket queues or inboxes. By integrating Easy Redmine with Teams, this setup ensures tickets are discussed and resolved faster, improving both response and resolution times. How it works Catch Easy Webhook – New Issue Created Triggers whenever Easy Redmine sends a webhook for a newly created ticket. Uses the webhook URL generated from Easy Redmine’s webhook settings. Get a new ticket by ID Fetches full ticket details (subject, priority, description) via the Easy Redmine API using the ticket ID from the webhook payload. Pick Description & Create URL to Issue Extracts the ticket description. Builds a direct link to the ticket in Easy Redmine for quick access. AI Agent – Description Processing Uses an AI model to summarize the ticket and suggest possible solutions based on the issue description. MS Teams Message to Support Channel Formats and sends the ticket details, priority, summary, and issue link into a designated Microsoft Teams channel. Uses the Teams message layout for clarity and quick scanning. How to Use Import the workflow into your n8n instance. Set up credentials: Easy Redmine API credentials with permission to read helpdesk tickets. Microsoft Teams credentials for posting messages to a channel. Configure Easy Redmine webhook To trigger on ticket creation events. Insert n8n webhook URL to your active Easy Redmine Webhook which can be created at https://easy-redmine-application.com/easy_web_hooks Adjust node settings: In the webhook node, use your Easy Redmine webhook URL. In the “Get a new ticket by ID” node, insert your API endpoint and authentication. In the Teams message node, select the correct Teams channel. Adjust timezone or scheduling if your team works across different time zones. Test the workflow by creating a sample ticket in Easy Redmine and confirming that it posts to Teams. Example Use Cases IT Helpdesk**: Notify the support team immediately when new issues are logged. Customer Support Teams**: Keep the entire team updated on urgent tickets in real time. Project Teams**: Ensure critical bug reports are shared instantly with the right stakeholders. Requirements Easy Redmine application Easy Redmine technical user for API calls with “read” permissions on tickets Microsoft Teams technical user for API calls with “post message” permissions Active n8n instance Customization Change the AI prompt to adjust how summaries and solutions are generated. Modify the Teams message format (e.g., bold priority, add emojis for urgency). Add filters so only high-priority or specific project tickets trigger notifications. Send alerts to multiple Teams channels based on ticket type or project. Workflow Improvement Suggestions: Rename nodes for clarity (e.g., “Fetch Ticket Details” instead of “get-one-issue”). Ensure no private ticket data is exposed beyond intended recipients. Add error handling for failed API calls to avoid missing ticket alerts.

Description This workflow automates a personalized pre-arrival guest experience for hotels by combining Google Sheets, OpenAI, Email, and Slack. It detects upcoming check-ins, maintains unified guest profiles for new and returning guests, and sends warm, AI-generated welcome messages exactly 2 days before arrival, ensuring a premium, consistent experience without manual effort. ⚠️ Deployment Disclaimer This workflow is intended for self-hosted n8n instances. ⚙️ What This Workflow Does (Step-by-Step) ▶️ Scheduled Daily Trigger Runs automatically each day to process upcoming arrivals. 📄 Fetch Guest Profiles (Google Sheets) Loads guest reservations and existing guest history from the source of truth. 👤 Guest Profile Handling Merges data for returning guests or creates a new profile for first-time visitors. ⏰ Pre-Arrival Window Check Calculates days until check-in and filters guests arriving within 2 days only. 🤖 AI Welcome Message Generation Uses OpenAI to generate short, personalized pre-arrival messages based on preferences, allergies, and special occasions. 📬 Multi-Channel Delivery Sends the message via Slack (if available) or Email and logs all messages to Google Sheets. 🗂 Profile & Message Logging Updates guest profiles and stores sent messages for tracking and audit. 🚨 Error Monitoring Any workflow failure triggers an immediate Slack alert. 🧩 Prerequisites • Google Sheets OAuth2 • OpenAI API key • Slack API access • SMTP email credentials • Self-hosted n8n recommended 💡 Key Benefits ✔ Personalized pre-arrival communication ✔ Zero manual guest follow-ups ✔ Unified guest profiles (new + returning) ✔ Timed, non-spammy outreach ✔ Full logging and error visibility 👥 Perfect For Hotels & resorts Boutique hospitality brands Property managers Guest experience teams

WP Category Toolkit automates mapping content topics to your WordPress category IDs using your WordPress REST API and a GPT-5mini model. It replaces manual copy-paste and guesswork when assigning categories, speeds up publishing, and reduces tagging errors so your site stays organized and search friendly without you learning new dev magic (just a bit of prompt craft). 💡 Why Use WP Category Toolkit? Saves time:** Cuts hours of manual category lookup and mapping—deploy batches of posts in minutes, not coffee breaks. Stops messy tagging:** Eliminates inconsistent category assignments so your archive and SEO behave themselves. Measurable improvement:** Expect faster publish cycles and fewer category fixes (reduce manual mapping errors by ~90%). Competitive edge:** Uses an LLM to interpret topic intent, so your categories align with content meaning, not guesswork—like having a librarian who understands your jokes. ⚡ Perfect For Content Managers:** Keep large WordPress catalogs neat without the spreadsheet gymnastics. Agencies:** Onboard client sites faster by automating taxonomy mapping across projects. Developers & Automators:** Add an LLM-powered mapping step to content pipelines without building custom classifiers. 🔧 How It Works ⏱ Trigger: Manual start (kick it off when you’re ready to map categories). 📎 Process: Pull all site categories from your WordPress REST endpoint, aggregate the list, and feed the source content + current topic into the mapping step. 🤖 Smart Logic: A Chain LLM node (Category-Mapping) uses a small prompt to decide which WordPress category IDs match the content topic (GPT5-mini handles the reasoning). 💌 Output: A clean category ID mapping you can paste into your Body Post WordPress node or use to patch posts automatically. 🔐 Quick Setup Import JSON file to your n8n instances → n8n Import Link Add credentials: WordPress API credential & Azure OpenAI (GPT5-mini) credential or you can use usual open AI node Update: Replace the WP endpoint URLand any post-body endpoints you’ll write back to Test: Run with a sample post and copy the output mapping into your Body Post WordPress node to confirm IDs match expected categories 🧩 You'll Need Active n8n instances → n8n Partner Link WordPress REST API access and credentials (wp-json access) Azure OpenAI account with access to GPT5-mini Integrations: WordPress API node, Chain LLM / Azure OpenAI node (Optional) Staging WordPress site to test mappings safely 🛠️ Level Up Ideas Auto-write category descriptions based on mapped content using the LLM. (It’ll sound smarter than your coffee.) Patch posts automatically after mapping so mapping becomes truly zero-touch. Add fallback heuristics: if the LLM is uncertain, route to a Slack/Microsoft Teams approval step. Made by: Khairul Tags: WordPress, Categories, AI, n8n Category: WordPress Need custom work? Contact me