Workflow Templates

Categories: Business Automation, Customer Support, AI, Knowledge Management This comprehensive workflow enables businesses to build and deploy a custom-trained AI Chatbot in minutes. By combining a sophisticated data scraping engine with a RAG-based (Retrieval-Augmented Generation) chat interface, it allows you to transform website content into a high-performance support agent. Powered by Google Gemini and Pinecone, this system ensures your chatbot provides accurate, real-time answers based exclusively on your business data. Benefits Instant Knowledge Sync** - Automatically crawls sitemaps and URLs to keep your AI up-to-date with your latest website content. Embeddable Anywhere** - Features a ready-to-use chat trigger that can be integrated into the bottom-right of any website via a simple script. High-Fidelity Retrieval** - Uses vector embeddings to ensure the AI "searches" your documentation before answering, reducing hallucinations. Smart Conversational Memory** - Equipped with a 10-message window buffer, allowing the bot to handle complex follow-up questions naturally. Cost-Efficient Scaling** - Leverages Gemini’s efficient API and Pinecone’s high-speed indexing to manage thousands of customer queries at a low cost. How It Works Dual-Path Ingestion: The process begins with an n8n Form where you provide a sitemap or individual URLs. The workflow automatically handles XML parsing and URL cleaning to prepare a list of pages for processing. Clean Content Extraction: Using Decodo, the workflow fetches the HTML of each page and uses a specialized extraction node to strip away code, ads, and navigation, leaving only the high-value text content. SignUp using: dashboard.decodo.com/register?referral_code=55543bbdb96ffd8cf45c2605147641ee017e7900. Vectorization & Storage: The cleaned text is passed to the Gemini Embedding model, which converts the information into 3076-dimensional vectors. These are stored in a Pinecone "supportbot" index for instant retrieval. RAG-Powered Chat Agent: When a user sends a message through the chat widget, an AI Agent takes over. It uses the user's query to search the Pinecone database for relevant business facts. Intelligent Response Generation: The AI Agent passes the retrieved facts and the current chat history to Google Gemini, which generates a polite, accurate, and contextually relevant response for the user. Requirements n8n Instance:** A self-hosted or cloud instance of n8n. Google Gemini API Key:** For text embeddings and chat generation. Pinecone Account:** An API key and a "supportbot" index to store your knowledge base. Decodo Access:** For high-quality website content extraction. How to Use Initialize the Knowledge Base: Use the Form Trigger to input your website URL or Sitemap. Run the ingestion flow to populate your Pinecone index. Configure Credentials: Authenticate your Google Gemini and Pinecone accounts within n8n. Deploy the Chatbot: Enable the Chat Trigger node. Use the provided webhook URL to connect the backend to your website's frontend chat widget. Test & Refine: Interact with the bot to ensure it retrieves the correct data, and update your knowledge base by re-running the ingestion flow whenever your website content changes. Business Use Cases Customer Support Teams** - Automate answers to 80% of common FAQs using your existing documentation. E-commerce Sites** - Help customers find product details, shipping policies, and return information instantly. SaaS Providers** - Build an interactive technical documentation assistant to help users navigate your software. Marketing Agencies** - Offer "AI-powered site search" as an add-on service for client websites. Efficiency Gains Reduce Ticket Volume** by providing instant self-service options. Eliminate Manual Data Entry** by scraping content directly from the live website. Improve UX** with 24/7 availability and zero wait times for customers. Difficulty Level: Intermediate Estimated Setup Time: 30 min Monthly Operating Cost: Low (variable based on AI usage and Pinecone tier)

Generate AI voice receptionist agents for local businesses using VAPI Automate the creation of personalized AI phone receptionists for local businesses by scraping Google Maps, analyzing websites, and deploying voice agents to VAPI. Who is this for? Agencies** offering AI voice solutions to local businesses Consultants** helping SMBs modernize their phone systems Developers** building lead generation tools for voice AI services Entrepreneurs** launching AI receptionist services at scale What this workflow does This workflow automates the entire process of creating customized AI voice agents: Collects business criteria through a form (city, keywords, quantity) Scrapes Google Maps for matching local businesses using Apify Fetches and analyzes each business website Generates tailored voice agent prompts using Claude AI Automatically provisions voice assistants via VAPI API Logs all created agents to Google Sheets for tracking The AI adapts prompts based on business type (salon, restaurant, dentist, spa) with appropriate tone, services, and booking workflows. Setup requirements Apify account** with Google Maps Scraper actor access Anthropic API key** for prompt generation OpenRouter API key** for website analysis VAPI account** with API access Google Sheets** connected via OAuth How to set up Import the workflow template Add your Apify credentials to the scraping node Configure Anthropic and OpenRouter API keys Replace YOUR_VAPI_API_KEY in the HTTP Request node header Connect your Google Sheets account Create a Google Sheet with columns: Business Name, Category, Address, Phone, Agent ID, Agent URL Update the Sheet URL in both Google Sheets nodes Activate the workflow and submit the form Customization options Business templates**: Edit the prompt in "Generate Agent Messages" to add new business categories Voice settings**: Modify ElevenLabs voice parameters (stability, similarity boost) LLM model**: Switch between GPT-4, Claude, or other models via OpenRouter Output format**: Customize the results page HTML in the final Form node

Intelligent Invoice Processing with AI Classification and XML Export Summary Automated invoice processing pipeline that extracts data from PDF invoices, uses AI Agent for intelligent expense categorization, generates XML for accounting systems, and routes high-value invoices for approval. Detailed Description A comprehensive accounts payable automation workflow that monitors for new PDF invoices, extracts text content, uses AI to classify expenses and detect anomalies, converts to XML format for accounting system integration, and implements approval workflows for high-value or unusual invoices. Key Features PDF Text Extraction**: Extract from File node parses invoice PDFs automatically AI-Powered Classification**: AI Agent categorizes expenses, suggests GL codes, detects anomalies XML Export**: Convert structured data to accounting-compatible XML format Approval Workflow**: Route invoices over $5,000 or low confidence for human review Multi-Trigger Support**: Google Drive monitoring or manual webhook upload Comprehensive Logging**: Archive all processed invoices to Google Sheets Use Cases Accounts payable automation Expense report processing Vendor invoice management Financial document digitization Audit trail generation Required Credentials Google Drive OAuth (for PDF source folder) OpenAI API key Slack Bot Token Gmail OAuth Google Sheets OAuth Node Count: 24 (19 functional + 5 sticky notes) Unique Aspects Uses Extract from File node for PDF text extraction (rarely used) Uses XML node for JSON to XML conversion (very rare) Uses AI Agent node for intelligent classification Uses Google Drive Trigger for file monitoring Implements approval workflow with conditional routing Webhook response** mode for API integration Workflow Architecture [Google Drive Trigger] [Manual Webhook] | | +----------+-----------+ | v [Filter PDF Files] | v [Download Invoice PDF] | v [Extract PDF Text] | v [Parse Invoice Data] (Code) | v [AI Invoice Classifier] <-- [OpenAI Chat Model] | v [Parse AI Classification] | v [Convert to XML] | v [Format XML Output] | v [Needs Approval?] (If) / \ Yes (>$5000) No (Auto) | | [Email Approval] [Slack Notify] | | +------+-------+ | v [Archive to Google Sheets] | v [Respond to Webhook] Configuration Guide Google Drive: Set folder ID to monitor in Drive Trigger node Approval Threshold: Default $5,000, adjust in "Needs Approval?" node Email Recipients: Configure finance-approvers@example.com Slack Channel: Set #finance-notifications for updates GL Codes: AI suggests codes; customize in AI prompt if needed Google Sheets: Configure document for invoice archive

Overview Automatically generate viral short-form health videos using AI and publish them to social platforms with n8n and Veo 3. This workflow collects viral ideas, analyzes engagement patterns, generates AI video scripts, renders videos with Veo 3, and handles publishing and tracking fully automated, with no manual editing. Who is this for? This template is ideal for: Content creators building faceless health channels (Shorts, Reels, TikTok) Affiliate marketers promoting health products with video content AI marketers running high-volume short-form content funnels Automation builders combining LLMs, video AI, and n8n Teams that want a scalable, repeatable system for viral AI video production If you want to create health niche videos at scale without manually scripting, rendering, and uploading each video, this workflow is for you. What problem is this workflow solving? Creating viral short-form health videos usually involves many manual steps and disconnected tools, such as: Manually collecting and validating viral content ideas Writing hooks and scripts for each video Switching between AI tools for analysis and video generation Waiting for videos to render and checking status manually Uploading videos and tracking what has been published This workflow connects all these steps into a single automated pipeline and removes repetitive manual work. What this workflow does This automated AI health video workflow: Runs on a defined schedule Collects viral health content ideas from external sources Normalizes and stores ideas in Google Sheets Loads pending viral ideas for processing Analyzes each idea and generates AI-optimized video scripts Creates AI videos automatically using the Veo 3 API Waits for video rendering and checks completion status Retrieves the final rendered videos Optionally aggregates or merges video assets Publishes videos to social platforms Updates Google Sheets with processing and publishing results The entire process runs end-to-end with minimal human intervention. Setup 1. Prepare Google Sheets Create a Google Sheet to manage your content pipeline with columns such as: idea / topic – Viral idea or source content analysis – AI analysis or hook summary script – Generated video script status – pending / processing / completed / failed video_url – Final rendered video link publish_result – Publishing status or notes Only rows marked as pending will be processed by the workflow. 2. Connect Google Sheets Authenticate your Google Sheets account in n8n Select the spreadsheet in the load and update nodes Ensure the workflow can write status updates back to the same sheet 3. Configure AI & Veo 3 Add credentials for your AI model (e.g. Gemini or similar) Configure prompt logic for health niche content Add your Veo 3 API credentials Test video creation with a small number of ideas before scaling 4. Configure Publishing & Schedule Set up publishing credentials for your target social platforms Open the Schedule triggers and define how often the workflow runs The schedule controls how frequently new AI health videos are created and published How to customize this workflow to your needs You can adapt this workflow without changing the core structure: Replace viral idea sources with your own research or internal data Adjust AI prompts for different health sub-niches Add manual approval steps before video creation Disable publishing and use the workflow only for video generation Add retry logic for failed renders or API errors Extend the workflow with analytics or performance tracking Best practices Start with a small batch of test ideas Keep status values consistent in Google Sheets Focus on strong hooks for health-related content Monitor rendering and publishing nodes during early runs Adjust schedule frequency based on API limits Documentation For a full walkthrough and advanced customization ideas, see the Video Guide.

🧠 Overview Discover and analyze the most valuable community-built n8n workflows on GitHub. This automation searches public repositories, analyzes JSON workflows using AI, and saves a ranked report to Google Sheets — including summaries, use cases, difficulty, stars, node count, and repository links. ⚙️ How It Works Search GitHub Code API — queries for extension:json n8n and splits results Fetch & Parse — downloads each candidate file’s raw JSON and safely parses it Extract Metadata — detects AI-powered flows and collects key node information AI Analysis — evaluates the top N workflows (description, use case, difficulty) Merge Insights — combines AI analysis with GitHub data Save to Google Sheets — appends or updates by workflow name 🧩 Setup Instructions (5–10 min) Open Config node and set: search_query — e.g., "openai" extension:json n8n max_results — number of results to fetch (1–100) ai_analysis_top — number of workflows analyzed with AI SPREADSHEET_ID, SHEET_NAME — Google Sheets target Add GitHub PAT via HTTP Header Credential: Authorization: Bearer <YOUR_TOKEN> Connect OpenAI Credential to OpenAI Chat Model Connect Google Sheets (OAuth2) to Save to Google Sheets (Optional) Enable Schedule Trigger to run weekly for automatic updates > 💡 Tip: If you need to show literal brackets, use backticks like `<example>` (no HTML entities needed). 📚 Use Cases 1) Trend Tracking for AI Automations Goal:** Identify the fastest-growing AI-powered n8n workflows on GitHub. Output:** Sorted list by stars and AI detection, updated weekly. 2) Internal Workflow Benchmarking Goal:** Compare your organization’s workflows against top public examples. Output:** Difficulty, node count, and AI usage metrics in Google Sheets. 3) Market Research for Automation Agencies Goal:** Discover trending integrations and tool combinations (e.g., OpenAI + Slack). Output:** Data-driven insights for client projects and content planning. 🧪 Notes & Best Practices 🔐 No hardcoded secrets — use n8n Credentials 🧱 Works with self-hosted or cloud n8n 🧪 Start small (max_results = 10) before scaling 🧭 Use “AI Powered” + “Stars” columns in Sheets to identify top templates 🧩 Uses only Markdown sticky notes — no HTML formatting required 🔗 Resources GitHub (template JSON):** github-workflow-finder-ai.json

Overview This workflow implements a self-healing Retrieval-Augmented Generation (RAG) maintenance system that automatically updates document embeddings, evaluates retrieval quality, detects embedding drift, and safely promotes or rolls back embedding updates. Maintaining high-quality embeddings in production RAG systems is difficult. When source documents change or embedding models evolve, updates can accidentally degrade retrieval quality or introduce semantic drift. This workflow solves that problem by introducing an automated evaluation and rollback pipeline for embeddings. It periodically checks for document changes, regenerates embeddings for updated content, evaluates the new embeddings against a set of predefined golden test questions, and compares the results with the currently active embeddings. Quality metrics such as Recall@K, keyword similarity, and answer variance are calculated, while embedding vectors are also analyzed for semantic drift using cosine distance. If the new embeddings outperform the current ones and remain within acceptable drift limits, they are automatically promoted to production. Otherwise, the system safely rolls back or flags the update for manual review. This creates a robust, production-safe RAG lifecycle automation system. How It Works 1. Workflow Trigger The workflow can start in two ways: Scheduled trigger** running daily Webhook trigger** when source documents change Both paths lead to a centralized configuration node that defines parameters such as chunk size, thresholds, and notification settings. 2. Document Retrieval & Change Detection Documents are fetched from the configured source (GitHub, Drive, Confluence, or other APIs). The workflow then: Splits documents into deterministic chunks Computes SHA-256 hashes for each chunk Compares them with previously stored hashes in Postgres Only new or modified chunks proceed for embedding generation, which significantly reduces processing cost. 3. Embedding Generation Changed chunks are processed through: Recursive text splitting Document loading OpenAI embedding generation These embeddings are stored as a candidate vector store rather than immediately replacing the production embeddings. Metadata about the embedding version is stored in Postgres. 4. Golden Question Evaluation A set of golden test questions stored in the database is used to evaluate retrieval quality. Two AI agents are used: One queries the candidate embeddings One queries the current production embeddings Both generate answers using retrieved context. 5. Quality Metrics Calculation The workflow calculates several evaluation metrics: Recall@K** to measure retrieval effectiveness Keyword similarity** between generated answers and expected answers Answer length variance** to detect inconsistencies These are combined into a weighted quality score. 6. Embedding Drift Detection The workflow compares embedding vectors between versions using cosine distance. This identifies semantic drift, which may occur due to: embedding model updates chunking changes document structure changes 7. Promotion or Rollback The workflow checks two conditions: Quality score exceeds the configured threshold Embedding drift remains below the drift threshold If both conditions pass: The candidate embeddings are promoted to active If not: The system rolls back to the previous embeddings Or flags the update for human review 8. Notifications A webhook notification is sent with: update status quality score drift score timestamp This allows teams to monitor embedding health automatically. Setup Instructions Configure Document Source Edit the Workflow Configuration node and set: documentSourceUrl API endpoint or file source containing your documents. Examples include: GitHub repository API Google Drive export API Confluence REST API Configure Postgres Database Create the following tables in your Postgres database: document_chunks embeddings embedding_versions golden_questions These tables store chunk hashes, embedding vectors, version metadata, and evaluation questions. Connect the Postgres nodes using your database credentials. Add OpenAI Credentials Configure credentials for: OpenAI Embeddings** OpenAI Chat Model** These are used for generating embeddings and answering evaluation questions. Populate Golden Questions Insert evaluation questions into the golden_questions table. Each record should include: question_text expected passages expected answer keywords These questions represent critical queries your RAG system must answer correctly. Configure Notification Webhook Add a Slack or Teams webhook URL in the configuration node. Notifications will be sent whenever: embeddings are promoted embeddings are rolled back manual review is required Adjust Quality Thresholds In the configuration node you can modify: qualityThreshold driftThreshold chunkSize chunkOverlap These parameters control the sensitivity of the evaluation system. Use Cases Production RAG Monitoring Automatically evaluate and update embeddings in production knowledge systems without risking degraded results. Continuous Knowledge Base Updates Keep embeddings synchronized with frequently changing documentation, repositories, or internal knowledge bases. Safe Embedding Model Upgrades Test new embedding models against production data before promoting them. AI System Reliability Detect retrieval regressions before they affect end users. Enterprise AI Governance Provide automated evaluation and rollback capabilities for mission-critical RAG deployments. Requirements This workflow requires the following services: n8n** Postgres Database** OpenAI API** Recommended integrations: Slack or Microsoft Teams (for notifications) Required nodes include: Schedule Trigger Webhook HTTP Request Postgres Compare Datasets Code nodes OpenAI Embeddings OpenAI Chat Model Vector Store nodes AI Agent nodes Summary This workflow provides a fully automated self-healing RAG infrastructure for maintaining embedding quality in production systems. By combining change detection, golden-question evaluation, embedding drift analysis, and automatic rollback, it ensures that retrieval performance improves safely over time. It is ideal for teams running production AI assistants, knowledge bases, or internal search systems that depend on high-quality vector embeddings.

A secure, scalable enterprise AI orchestration layer built on the Model Context Protocol (MCP). This workflow standardizes tool access across all business systems, enforces permission-based data handling, applies contextual reasoning via Claude AI, and provides a single governance plane for multi-agent AI deployments. How it works Receive AI Agent Request - Unified MCP webhook accepts tool or context requests from any agent Enterprise Auth & RBAC - Validates JWT, resolves role-based access controls, enforces tenant isolation Context Assembly - Builds full enterprise context: user profile, org policies, active sessions, prior tool calls Claude AI Orchestration - Reasons over context to select optimal tool chain, validate intent, plan execution Policy Enforcement Engine - Applies data classification, DLP rules, and geo/time-based restrictions Multi-System Tool Dispatch - Routes to CRM, ERP, HRMS, Data Warehouse, or custom APIs in parallel Response Aggregation - Merges multi-tool results, applies post-processing and redaction rules Compliance Logging - SOC2/ISO27001-ready audit trail with data lineage tracking Return Enriched Context - Delivers MCP-compliant response with reasoning trace back to agent Setup Steps Import workflow into n8n Configure credentials: Anthropic API - Claude AI for orchestration and contextual reasoning Google Sheets - RBAC policy store, session registry, audit log SMTP / Slack - Security and compliance notifications JWT Secret - For enterprise token validation Populate the RBAC policy sheet with roles, permissions, and data classifications Configure your enterprise system endpoints in the tool dispatch nodes Set your tenant IDs and org-level data policies Activate workflow and register the webhook URL with your AI agent platform Sample Enterprise MCP Request { "mcpVersion": "1.1", "agentId": "sales-agent-prod-007", "jwtToken": "eyJhbGciOiJIUzI1NiJ9...", "tenantId": "ORG-ACME-001", "userId": "john.doe@acme.com", "userRole": "sales_manager", "toolRequests": [ { "toolName": "crm.get_pipeline", "parameters": { "region": "APAC" } }, { "toolName": "erp.get_inventory", "parameters": { "sku": "PROD-001" } } ], "agentGoal": "Prepare a quarterly sales brief for the APAC team meeting", "dataClassification": "INTERNAL", "sessionId": "sess-xyz-001" } Enterprise Features Multi-tenant isolation** — strict org boundary enforcement RBAC + ABAC** — role and attribute-based access control per tool per data class Data Loss Prevention (DLP)** — redacts PII/secrets before returning to agent Contextual AI reasoning** — Claude plans the optimal tool chain for agent goals SOC2 / ISO 27001** audit trail with data lineage Geo & time-based policy** — restrict tool access by region or business hours Explore More LinkedIn & Social Automation: Contact us to design AI-powered lead nurturing, content engagement, and multi-platform reply workflows tailored to your growth strategy.

🔧 Setup Guide - Hiring Bot Workflow 📋 Prerequisites Before importing this workflow, make sure you have: ✅ n8n Instance (cloud or self-hosted) ✅ Telegram Bot Token (from @BotFather) ✅ OpenAI API Key (with GPT-4 Vision access) ✅ Gmail Account (with OAuth setup) ✅ Google Drive (to store your resume) ✅ Redis Instance (free tier available at Redis Cloud) 🚀 Step-by-Step Setup 1️⃣ Upload Your Resume to OpenAI First, you need to upload your resume to OpenAI's Files API: Upload your resume to OpenAI curl https://api.openai.com/v1/files \ -H "Authorization: Bearer YOUR_OPENAI_API_KEY" \ -F purpose="assistants" \ -F file="@/path/to/your/resume.pdf" Important: Save the file_id from the response (looks like file-xxxxxxxxxxxxx) Alternative: Use OpenAI Playground or Python: from openai import OpenAI client = OpenAI(api_key="YOUR_API_KEY") with open("resume.pdf", "rb") as file: response = client.files.create(file=file, purpose="assistants") print(f"File ID: {response.id}") 2️⃣ Upload Your Resume to Google Drive Go to Google Drive Upload your resume PDF Right-click → "Get link" → Copy the file ID from URL URL format: https://drive.google.com/file/d/FILE_ID_HERE/view Example ID: 1h79U8IFtI2dp_OBtnyhdGaarWpKb9qq9 3️⃣ Create a Telegram Bot Open Telegram and message @BotFather Send /newbot Choose a name and username Save the Bot Token (looks like 123456789:ABCdefGHIjklMNOpqrsTUVwxyz) (Optional) Set bot commands: /start - Start the bot /help - Get help 4️⃣ Set Up Redis Option A: Redis Cloud (Recommended - Free) Go to Redis Cloud Create a free account Create a database Note: Host, Port, Password Option B: Local Redis Docker docker run -d -p 6379:6379 redis:latest Or via package manager sudo apt-get install redis-server 5️⃣ Import the Workflow to n8n Open n8n Click "+" → "Import from File" Select Hiring_Bot_Anonymized.json Workflow will import with placeholder values 6️⃣ Configure Credentials A. Telegram Bot Credentials In n8n, go to Credentials → Create New Select "Telegram API" Enter your Bot Token from Step 3 Test & Save B. OpenAI API Credentials Go to Credentials → Create New Select "OpenAI API" Enter your OpenAI API Key Test & Save C. Redis Credentials Go to Credentials → Create New Select "Redis" Enter: Host: Your Redis host Port: 6379 (default) Password: Your Redis password Test & Save D. Gmail Credentials Go to Credentials → Create New Select "Gmail OAuth2 API" Follow OAuth setup flow Authorize n8n to access Gmail Test & Save E. Google Drive Credentials Go to Credentials → Create New Select "Google Drive OAuth2 API" Follow OAuth setup flow Authorize n8n to access Drive Test & Save 7️⃣ Update Node Values A. Update OpenAI File ID in "PayloadForReply" Node Double-click the "PayloadForReply" node Find this line in the code: const resumeFileId = "YOUR_OPENAI_FILE_ID_HERE"; Replace with your actual OpenAI file ID from Step 1: const resumeFileId = "file-xxxxxxxxxxxxx"; Save the node B. Update Google Drive File ID (Both "Download Resume" Nodes) There are TWO nodes that need updating: Node 1: "Download Resume" Double-click the node In the "File ID" field, click "Expression" Replace YOUR_GOOGLE_DRIVE_FILE_ID with your actual ID Update "Cached Result Name" to your resume filename Save Node 2: "Download Resume1" (same process) Double-click the node Update File ID Update filename Save 8️⃣ Assign Credentials to Nodes After importing, you need to assign your credentials to each node: Nodes that need credentials: | Node Name | Credential Type | |-----------|----------------| | Telegram Trigger | Telegram API | | Generating Reply | OpenAI API | | Store AI Reply | Redis | | GetValues | Redis | | Download Resume | Google Drive OAuth2 | | Download Resume1 | Google Drive OAuth2 | | Schedule Email | Gmail OAuth2 | | SendConfirmation | Telegram API | | Send a message | Telegram API | | Edit a text message | Telegram API | | Send a text message | Telegram API | | Send a chat action | Telegram API | How to assign: Click on each node In the "Credentials" section, select your saved credential Save the node 🧪 Testing the Workflow 1️⃣ Activate the Workflow Click the "Active" toggle in the top-right Workflow should now be listening for Telegram messages 2️⃣ Test with a Job Post Find a job post online (LinkedIn, Indeed, etc.) Take a screenshot Send it to your Telegram bot Bot should respond with: "Analyzing job post..." (typing indicator) Full email draft with confirmation button 3️⃣ Test Email Sending Click "Send The Email" button Check Gmail to verify email was sent Check if resume was attached 🐛 Troubleshooting Issue: "No binary image found" Solution:** Make sure you're sending an image file, not a document Issue: "Invalid resume file_id" Solution:** Check OpenAI file_id format (starts with file-) Verify file was uploaded successfully Make sure you updated the code in PayloadForReply node Issue: "Failed to parse model JSON" Solution:** Check OpenAI API quota/limits Verify model name is correct (gpt-5.2) Check if image is readable Issue: Gmail not sending Solution:** Re-authenticate Gmail OAuth Check Gmail permissions Verify "attachments" field is set to "Resume" Issue: Redis connection failed Solution:** Test Redis connection in credentials Check firewall rules Verify host/port/password Issue: Telegram webhook not working Solution:** Deactivate and reactivate workflow Check Telegram bot token is valid Make sure bot is not blocked 🔐 Security Best Practices Never share your credentials - Keep API keys private Use environment variables in n8n for sensitive data Set up Redis password - Don't use default settings Limit OAuth scopes - Only grant necessary permissions Rotate API keys regularly Monitor usage - Check for unexpected API calls 🎨 Customization Ideas Change AI Model In the PayloadForReply node, update: const MODEL = "gpt-5.2"; // Change to gpt-4, claude-3-opus, etc. Adjust Email Length Modify the system prompt: // From: Email body: ~120–180 words unless INSIGHTS specify otherwise. // To: Email body: ~100–150 words for concise applications. Add More Languages Update language detection logic in the system prompt to support more languages. Custom Job Filtering Edit the system prompt to target specific roles: // From: Only pick ONE job offer to process — the one most clearly related to Data roles // To: Only pick ONE job offer to process — the one most clearly related to [YOUR FIELD] Add Follow-up Reminders Add a "Wait" node after email sends to schedule a reminder after 7 days. 📊 Workflow Structure Telegram Input ↓ Switch (Route by type) ↓ ├─ New Job Post │ ↓ │ Send Chat Action (typing...) │ ↓ │ PayloadForReply (Build AI request) │ ↓ │ Generating Reply (Call OpenAI) │ ↓ │ FormatAiReply (Parse JSON) │ ↓ │ Store AI Reply (Redis cache) │ ↓ │ SendConfirmation (Show preview) │ └─ Callback (User clicked "Send") ↓ GetValues (Fetch from Redis) ↓ Format Response ↓ Download Resume (from Drive) ↓ ├─ Path A: Immediate Send │ ↓ │ Send Confirmation Message │ ↓ │ Edit Message (update status) │ └─ Path B: Scheduled Send ↓ Wait (10 seconds) ↓ Download Resume Again ↓ Schedule Email (Gmail) ↓ Send Success Message 💡 Tips for Best Results High-Quality Resume: Upload a well-formatted PDF resume Clear Screenshots: Take clear, readable job post screenshots Use Captions: Add instructions via Telegram captions Example: "make it more casual" Example: "send to recruiter@company.com" Review Before Sending: Always read the draft before clicking send Update Resume Regularly: Keep your Google Drive resume current Test First: Try with a few test jobs before mass applying 🆘 Need Help? 📚 n8n Documentation 💬 n8n Community Forum 📺 n8n YouTube Channel 🤖 OpenAI Documentation 📱 Telegram Bot API Docs 📝 Checklist Use this checklist to verify your setup: [ ] OpenAI resume file uploaded (got file_id) [ ] Google Drive resume uploaded (got file ID) [ ] Telegram bot created (got bot token) [ ] Redis instance created (got credentials) [ ] All n8n credentials created and tested [ ] PayloadForReply node updated with OpenAI file_id [ ] Both Download Resume nodes updated with Drive file_id [ ] All nodes have credentials assigned [ ] Workflow activated [ ] Test message sent successfully [ ] Test email received successfully 🎉 You're all set! 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WhatsApp Booking Flow | Consultation Scheduling This n8n template automates appointment booking via WhatsApp Flows with real-time calendar availability, AI-powered intent classification, and CRM synchronization. It transforms manual booking conversations into a seamless self-service experience directly within WhatsApp. Who is this for? Service businesses wanting WhatsApp-based appointment booking Consultants and agencies offering scheduled calls or consultations Teams using Airtable for CRM and Google Calendar for scheduling Businesses looking to reduce booking friction with conversational commerce What problem does this workflow solve? Appointment booking typically involves back-and-forth messaging to find available times, collect customer details, and confirm bookings. This workflow eliminates that friction by providing an interactive booking flow within WhatsApp that checks real-time calendar availability, collects customer information, and automatically syncs bookings to your CRM and calendar. How it works Webhook Entry Point: A single webhook handles both GET (Meta verification) and POST (messages/flows) requests - required due to Meta's single-webhook-per-app restriction. Message Routing: Incoming requests are classified as either regular WhatsApp messages or encrypted Flow requests, then routed accordingly. WhatsApp Flow Handling: Flow requests are decrypted using RSA/AES-GCM encryption. The workflow handles multiple Flow actions: INIT: Returns consultation types and date constraints SERVICE_SELECTION: Processes service and customer details DATE_TIME_SELECTION: Queries calendar availability for selected date CONFIRMATION: Displays booking summary COMPLETE_BOOKING: Finalizes the booking AI Agent: For regular text messages, an OpenAI-powered agent classifies user intent. When booking intent is detected, it triggers the consultation template with the WhatsApp Flow. Booking Process: On completion, the workflow: Creates or updates customer in Airtable Creates booking record with event details Creates Google Calendar event Sends WhatsApp confirmation message Good to Know Verify Token: The WHATSAPP_VERIFY_TOKEN environment variable is required for Meta webhook verification. Set this to any secure string and use the same value in Meta Developer Portal. Cloud vs Self-hosted: Cloud n8n instances are easier to configure as they have public URLs. Self-hosted instances require additional setup for public accessibility. Hostinger/Docker Setup: For self-hosted instances, configure public webhook access in your docker-compose.yml or reverse proxy configuration. Meta Prerequisites: You'll need: Facebook Account Meta Developer App WhatsApp Business Account (linked to Developer App) WhatsApp Business Phone Number Health Checks: Meta sends periodic ping requests to verify webhook availability. The workflow handles these automatically with a 200 response. Single Webhook Restriction: Meta only allows one webhook URL per WhatsApp app, which is why all message types flow through a single endpoint with routing logic. Encryption: WhatsApp Flows use end-to-end encryption. The workflow handles RSA decryption (for AES key exchange) and AES-128-GCM encryption/decryption for Flow data. Requirements Meta Business Account with WhatsApp Business API access WhatsApp Business App in Meta Developer Portal n8n instance (cloud or self-hosted with public URL) OpenAI API key (for intent classification with GPT-4o) Airtable account with base containing: Customers, Services, Bookings tables Google Calendar with OAuth credentials Environment Variables | Variable | Description | |----------|-------------| | WHATSAPP_VERIFY_TOKEN | Webhook verification token (match in Meta Developer Portal) | | WHATSAPP_PRIVATE_KEY | RSA private key for Flow encryption (PEM format) | | WHATSAPP_PRIVATE_KEY_PASSPHRASE | Passphrase for the RSA private key | | GOOGLE_CALENDAR_ID | Calendar ID for availability checks and event creation | Setup Meta Business Setup Create a Meta Developer App at developers.facebook.com Add WhatsApp product to your app Set up a WhatsApp Business Account Generate a permanent access token Import Workflow Import personal-booking-whatsapp-flow.json into n8n Replace placeholder credential IDs with your actual credentials Configure Credentials WhatsApp: HTTP Bearer Auth with your access token OpenAI: API key for GPT-4o Airtable: OAuth2 authentication Google Calendar: OAuth2 authentication Set Environment Variables Configure all variables listed above in n8n settings Configure Webhook in Meta Navigate to WhatsApp > Configuration in Developer Portal Set webhook URL to your n8n webhook endpoint Enter your verify token Subscribe to messages webhook field Create WhatsApp Flow In WhatsApp Manager, create a new Flow Use the JSON from whatsapp-flow.json as your Flow definition Publish the Flow and note the Flow ID Create Message Template Create a template with a Flow button component Link it to your published Flow Submit for approval Airtable Schema Customers Table: customer_name (text) customer_email (email) phone_number (text) created_at (date) Services Table: service_name (text) service_key (text) - e.g., "30_min", "60_min" duration_minutes (number) Bookings Table: customer_email (text) service_type (linked to Services) event_date (date) event_time (text) booking_status (select: Pending, Confirmed, Cancelled) calendar_event_id (text) created_at (date) Customizing this workflow Consultation Types: Modify the INIT handler code node to add/change consultation options and durations. Business Hours: Adjust the calendar availability logic in the date refresh handler to match your working hours. AI Agent Prompts: Customize the system prompt in the AI Agent node to match your business context and available services. Messaging Templates: Create additional WhatsApp templates for different services (quotes, information requests) and add corresponding tools to the AI agent. CRM Fields: Extend the Airtable schema and update the booking creation nodes to capture additional customer data. Made by www.fenrirlabs.nl

This workflow automates the complete blog publishing process. It removes manual work from content creation, image generation, category management, and WordPress publishing by using AI and n8n. It helps agencies, SEO teams, and content creators manage blogs at scale. Key Features Scheduled or manual blog publishing Automated topic research and content writing AI-generated featured and in-content images using Ideogram Dynamic WordPress category detection and creation Automatic media upload with SEO-friendly alt text Internal linking using sitemap data Google Sheets logging for published URLs Error notifications for failed executions What This Workflow Does Input Blog topics or keywords stored in Google Sheets Target WordPress site details Publishing rules and schedule Processing Triggers the workflow on a schedule or manual run Fetches blog posting data from Google Sheets Validates active projects or websites Performs topic and SEO research Writes long-form, SEO-optimized blog content Generates image prompts and creates images using Ideogram Uploads images to WordPress with alt text Detects or creates blog categories dynamically Publishes the blog post to WordPress Output Live published blog post URL Updated Google Sheet with publishing details Notification alerts if any step fails Setup Instructions Prerequisites n8n instance (cloud or self-hosted) WordPress site with REST API access Google Sheets access AI model credentials (Google Gemini, OpenAI, or DeepSeek) Ideogram API access Notification service (Discord or Slack) Step 1: Import the Workflow Download or copy the workflow JSON In n8n, go to Workflows → Import from file / JSON Import the workflow Step 2: Configure Credentials Set up the required credentials inside n8n's credential manager: Google Sheets OAuth**: For reading posting data and saving URLs WordPress API**: For publishing posts and uploading media AI Model**: Connect Google Gemini, OpenAI, or DeepSeek Ideogram API**: For AI image generation Discord/Slack Webhook**: For error notifications Important: No credentials are hardcoded. All must be connected via n8n's credential manager. Step 3: Configure Google Sheets Prepare a Google Sheet containing: Blog topic or keyword Target website or domain Publishing status fields Domain ID for tracking Update the Sheet ID inside the Get_Post_Data node after import. Step 4: Configure Website Access Update the PBN_Website_Access node with your WordPress site access endpoint or API. This node should return: Complete WordPress URL Basic authentication token Sitemap post URL Step 5: Configure Publishing & Schedule Adjust the Schedule Trigger if auto-publishing is required Modify publishing frequency or time zone Review WordPress post status (draft or publish) Step 6: Test & Activate Add one test row in Google Sheets Run the workflow manually Verify: Content creation Image generation WordPress publishing Sheet updates Activate the workflow Usage Guide Adding New Blog Posts Add a new row in the connected Google Sheet with the required blog topic and website details. The workflow will automatically process and publish the post on the next execution. Understanding the Output After execution, the workflow: Publishes a complete blog post on WordPress Attaches featured and in-content images Assigns the correct category Logs the live URL back to Google Sheets Workflow Node Breakdown Get_Post_Data Fetches blog posting details from Google Sheets based on the current day. It pulls keywords, landing pages, domain IDs, and posting websites. get_client_status Checks the client's project status from the project sheet. It verifies whether the client is active or inactive before proceeding further. This prevents publishing content for paused or stopped clients. PBN_Website_Access Fetches WordPress website access details such as site URL, authentication token, and sitemap URL. These details are required for publishing posts, uploading images, and managing categories. Do the Research on the Topic Performs deep SEO research on the target keyword. It analyzes search intent, content gaps, and audience needs. This ensures the generated content is informative, relevant, and SEO-optimized. sitemap_crawl (internal_linking) Crawls the website sitemap to collect internal URLs. These URLs are later used for internal linking inside the blog content. Internal links help improve SEO and site structure. write_content Uses AI to write an 800-1000 word SEO-optimized blog article based on research data. The content includes proper HTML formatting, internal links, and anchor keyword placement. extract_title_body Separates the H1 title from the blog body content for proper WordPress publishing format. classify_category Automatically determines the most suitable category for the blog post by analyzing the blog title and content context. This keeps the website's category structure clean and relevant. get_category & create_category Checks if the determined category exists in WordPress. If not, it creates a new category automatically. generate_image_prompt Analyzes the blog content and generates AI prompts for creating relevant images including thumbnail and in-content images. Thumbnail Image Generator & Blog Image Generator Generate high-quality images using Ideogram API based on AI-generated prompts. Images are created with proper resolution and rendering settings. Thumbnail Uploading & Blog Image Uploading Upload generated images to WordPress media library and retrieve media IDs for post attachment. Add Alt Text in Images Adds SEO-friendly alt text to uploaded images to improve accessibility and search engine optimization. Blog and Photo Merge Merges the generated images into the blog content at appropriate positions within the article. publish_blog Publishes the complete blog post to WordPress with title, content, category, featured image, and publish status. save_live_url Saves the live published blog URL back into Google Sheets along with keyword, website URL, and timestamp for tracking and reporting. If Error Existed Then Get Notified Sends instant Discord or Slack notifications when any error occurs during workflow execution, ensuring no failure goes unnoticed. Customization Options Change blog length or tone in the content generation node Modify image style or resolution in Ideogram nodes Add multi-site publishing using Switch nodes Replace notification channel (Discord to Slack or Email) Extend workflow to social media posting Troubleshooting Blog not published Check WordPress credentials and REST API permissions. Images not generated Verify Ideogram API credentials and prompt formatting. Sheet not updating Ensure correct Sheet ID and OAuth permissions. Workflow stopped Review execution logs and error notification messages. Use Cases SEO blog automation for agencies Content publishing for niche websites Scalable blog management AI-assisted content operations Hands-free WordPress publishing Final Notes This workflow is designed to be reusable, scalable, and creator-friendly. It follows n8n best practices, avoids hardcoded credentials, and is suitable for public sharing as a workflow template. For any questions or support, please contact: info@incrementors.com or fill out this form: https://www.incrementors.com/contact-us/

Important: This workflow uses the Autype community node and requires a self-hosted n8n instance. This workflow downloads a fillable PDF form from a URL, extracts all form field names and types using Autype, sends the field list to an AI Agent (OpenAI) together with applicant data, and uses the AI response to fill the form automatically. The AI is instructed to return raw JSON only, and a Code node validates the response before filling. The filled PDF is flattened (non-editable) and saved to Google Drive. Who is this for? Companies that regularly submit the same types of PDF form applications -- permit renewals, tax filings, compliance questionnaires, insurance claims, customs declarations, or any recurring government/regulatory paperwork. Instead of manually filling the same form fields every quarter or year, the AI reads the form structure and fills it with the correct data automatically. Concrete example: A manufacturing company must renew its operating permit every year by submitting a multi-page PDF application to the local regulatory authority. The form asks for company name, registration number, address, contact person, business type, employee count, and more. With this workflow, the company stores its data once in the AI Agent prompt, and every renewal period they simply run the workflow to get a completed, flattened PDF ready for submission. This also works as an additional skill for an AI agent. Instead of a manual trigger, connect the workflow to a webhook or chat trigger so an agent can call it when a user asks "fill out the permit renewal form for Q2 2026." What this workflow does On manual trigger, the workflow fetches a fillable PDF from a URL (e.g. a government portal, internal document server, or S3 bucket). It uploads the PDF to Autype and calls Get Form Fields to extract every field name, type (text, checkbox, dropdown, radio), current value, available options, and read-only status. The field list is passed directly to an AI Agent via an inline expression (no separate prompt-building Code node needed). The AI's system message instructs it to return only raw JSON. A Code node validates and parses the response before Autype fills the form, flattens it, and the result is saved to Google Drive. Showcase How it works Run Workflow -- Manual trigger starts the pipeline. Download PDF Form -- An HTTP Request node fetches the fillable PDF from a URL (the sample uses a registration form with 7 fields). Upload PDF Form -- Uploads the PDF binary to Autype Tools to get a file ID. Get Form Fields -- Autype extracts all form fields and returns them as metadata. Each field includes: name, type (text/checkbox/dropdown/radio/optionlist), value (current), options (for dropdowns/radio), and isReadOnly. No output file is created. AI Agent -- Receives the field list and applicant data directly in its prompt via an n8n expression. The system message instructs the AI to return only a raw JSON object mapping field names to values (strings for text/dropdown/radio, booleans for checkboxes). Prepare Fill Data -- A Code node parses and validates the AI JSON response (strips markdown fences if present), then pairs it with the Autype file ID. Fill PDF Form -- Autype fills every form field with the AI-generated values. Fields are flattened (non-editable) so the output is a clean, final PDF. Save Filled PDF to Drive -- The completed form is uploaded to Google Drive as filled-form-YYYY-MM-DD.pdf. Setup Install the Autype community node (n8n-nodes-autype) via Settings > Community Nodes. Create an Autype API credential with your API key from app.autype.com. See API Keys in Settings. Create an OpenAI API credential with your key from platform.openai.com. Create a Google Drive OAuth2 credential and connect your Google account. Import this workflow and assign your credentials to each node (including the OpenAI Chat Model sub-node). The sample form URL is pre-configured. To use your own form, replace the URL in the "Download PDF Form" node. Edit the applicant data directly in the AI Agent node prompt (the "Prompt (User Message)" field). Set YOUR_FOLDER_ID in the "Save Filled PDF to Drive" node to your target Google Drive folder. Click Test Workflow to run the pipeline. Note: This is a community node, so you need a self-hosted n8n instance to use community nodes. Requirements Self-hosted n8n instance (community nodes are not available on n8n Cloud) Autype account with API key (free tier available) n8n-nodes-autype community node installed OpenAI API key (gpt-4o-mini or any chat model) Google Drive account with OAuth2 credentials (optional, can replace with other output) How to customize Change applicant data:** Edit the prompt text directly in the "AI Agent" node. Replace the example person/company info with your own. Use a different AI model:** Swap the OpenAI Chat Model sub-node for Anthropic Claude, Google Gemini, or any LangChain-compatible chat model. Connect to an AI agent:** Replace the Manual Trigger with a Webhook or Chat Trigger so an AI agent can call this workflow as a tool (e.g. "fill the Q2 permit renewal form"). Skip flattening:** Set flatten to false in the "Fill PDF Form" node if you want the fields to remain editable after filling. Add watermark:** Insert an Autype Watermark step after Fill Form to stamp "DRAFT" or "SUBMITTED" on every page before saving. Add password protection:** Insert an Autype Protect step after filling to encrypt the PDF before uploading to Drive. Change output destination:** Replace the Google Drive node with Email (SMTP), S3, Slack, or any other n8n output node. Pull data from a database:** Instead of hardcoding data in the AI Agent prompt, query a database (Postgres, MySQL, Airtable) or CRM (HubSpot, Salesforce) to dynamically fill different forms for different entities.

This workflow provides automated access to the Settyan Flash V2.0.0 Beta.4 AI model through the Replicate API. It saves you time by eliminating the need to manually interact with AI models and provides a seamless integration for other generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete other generation process using the Settyan Flash V2.0.0 Beta.4 model. It manages API authentication, parameter configuration, request processing, and result retrieval with built-in error handling and retry logic for reliable automation. Model Description: Advanced AI model for automated processing and generation tasks. Key Capabilities Specialized AI model with unique capabilities** Advanced processing and generation features** Custom AI-powered automation tools** Tools Used n8n**: The automation platform that orchestrates the workflow Replicate API**: Access to the Settyan/flash-v2.0.0-beta.4 AI model Settyan Flash V2.0.0 Beta.4**: The core AI model for other generation Built-in Error Handling**: Automatic retry logic and comprehensive error management How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Replicate API: Add your Replicate API token to the 'Set API Token' node Customize Parameters: Adjust the model parameters in the 'Set Other Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Specialized Processing**: Handle specific AI tasks and workflows Custom Automation**: Implement unique business logic and processing Data Processing**: Transform and analyze various types of data AI Integration**: Add AI capabilities to existing systems and workflows Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Replicate API**: https://replicate.com (Sign up to access powerful AI models) #n8n #automation #ai #replicate #aiautomation #workflow #nocode #aiprocessing #dataprocessing #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation