Workflow Templates

Event Alumni Re-engager: HubSpot → Gemini → Personalized Outreach Who is this for? Event marketers and conference organizers who want to reactivate past attendees with AI-personalized emails at 3-5x ROI vs. cold leads. What problem is this workflow solving? Alumni gold is untapped: Past attendees convert 3-5x better Manual segmentation takes hours Generic emails get ignored This auto-segments + personalizes outreach from HubSpot data. What this workflow does CRM → AI Alumni Machine: Trigger**: Manual (or schedule 8-12 weeks pre-event) HubSpot Pull**: Past event_registration contacts Smart Filter**: Exclude current registrants 3-Tier Segments**: Champions (3+ events) / Returning (2) / One-timers (1) Gemini Personalization**: Unique copy per attendee Email Send**: Alumni-exclusive CTAs Slack Summary**: Campaign stats posted Main workflow required. This is a sub-workflow triggered by Event Registration with Auto-Enrichment Setup (7 minutes) HubSpot**: Header Auth (Bearer YOUR_API_KEY) Custom Property**: event_registration (comma-separated event IDs) Gemini**: Flash Lite API key Email**: SMTP + Slack OAuth2 Config**: Event details in Set Campaign Config Fully configurable—no code changes needed. How to customize to your needs Segments**: Add VIPs / Speakers / High-LTV CRM**: HubSpot → Salesforce → Sheets Copy**: Edit Gemini prompts for tone/industry Channels**: Add WhatsApp / LinkedIn Timing**: Cron for automated runs HubSpot Setup: event_registration property auto-created by companion template. ROI: 3-5x conversion** vs. cold leads 60% lower CAC** (alumni segment) 2h → 2min** campaign launch Need help customizing?: Contact me for consulting and support: LinkedIn / Message Keywords: event participant re-engagement, conference registration, HubSpot automation, personalized outreach, conference marketing

AI-Powered Financial Document Processing with Google Gemini This comprehensive workflow automates the complete financial document processing pipeline using AI. Upload invoices via chat, drop expense receipts into a folder, or add bank statements - the system automatically extracts, categorizes, and organizes all your financial data into structured Google Sheets. What this workflow does Processes three types of financial documents automatically: Invoice Processing**: Upload PDF invoices through a chat interface and get structured data extraction with automatic file organization Expense Management**: Monitor a Google Drive folder for new receipts and automatically categorize expenses using AI Bank Statement Processing**: Extract and organize transaction data from bank statements with multi-transaction support Financial Analysis**: Query all your financial data using natural language with an AI agent Key Features Multi-AI Persona System**: Four specialized AI personas (Mark, Donna, Victor, Andrew) handle different financial functions Google Gemini Integration**: Advanced document understanding and data extraction from PDFs Smart Expense Categorization**: Automatic classification into 17 business expense categories using LLM Real-time Monitoring**: Continuous folder watching for new documents with automatic processing Natural Language Queries**: Ask questions about your financial data in plain English Automatic File Management**: Intelligent file naming and organization in Google Drive Comprehensive Error Handling**: Robust processing that continues even when individual documents fail How it works Invoice Processing Flow User uploads PDF invoice via chat interface File is saved to Google Drive "Invoices" folder Google Gemini extracts structured data (vendor, amounts, line items, dates) Data is parsed and saved to "Invoice Records" Google Sheet File is renamed as "{Vendor Name} - {Invoice Number}" Confirmation message sent to user Expense Processing Flow User drops receipt PDF into "Expense Receipts" Google Drive folder System detects new file within 1 minute Google Gemini extracts expense data (merchant, amount, payment method) OpenRouter LLM categorizes expense into appropriate business category All data saved to "Expenses Recording" Google Sheet Bank Statement Processing Flow User uploads bank statement to "Bank Statements" folder Google Gemini extracts multiple transactions from statement Custom JavaScript parser handles various bank formats Individual transactions saved to "Bank Transactions Record" Google Sheet Financial Analysis Enable the analysis trigger when needed Ask questions in natural language about your financial data AI agent accesses all three spreadsheets to provide insights Get reports, summaries, and trend analysis What you need to set up Required APIs and Credentials Google Drive API** - For file storage and monitoring Google Sheets API** - For data storage and retrieval Google Gemini API** - For document processing and data extraction OpenRouter API** - For expense categorization (supports multiple LLM providers) Google Drive Folder Structure Create these folders in your Google Drive: "Invoices" - Processed invoice storage "Expense Receipts" - Drop zone for expense receipts (monitored) "Bank Statements" - Drop zone for bank statements (monitored) Google Sheets Setup Create three spreadsheets with these column headers: Invoice Records Sheet: Vendor Name, Invoice Number, Invoice Date, Due Date, Total Amount, VAT Amount, Line Item Description, Quantity, Unit Price, Total Price Expenses Recording Sheet: Merchant Name, Transaction Date, Total Amount, Tax Amount, Payment Method, Line Item Description, Quantity, Unit Price, Total Price, Category Bank Transactions Record Sheet: Transaction ID, Date, Description/Payee, Debit (-), Credit (+), Currency, Running Balance, Notes/Category Use Cases Small Business Accounting**: Automate invoice and expense tracking for bookkeeping Freelancer Financial Management**: Organize client invoices and business expenses Corporate Expense Management**: Streamline employee expense report processing Financial Data Analysis**: Generate insights from historical financial data Bank Reconciliation**: Automate transaction recording and account reconciliation Tax Preparation**: Maintain organized records with proper categorization Technical Highlights Expense Categories**: 17 predefined business expense categories (Cost of Goods Sold, Marketing, Payroll, etc.) Multi-format Support**: Handles various PDF layouts and bank statement formats Scalable Processing**: Processes multiple documents simultaneously Error Recovery**: Continues processing even when individual documents fail Natural Language Interface**: No technical knowledge required for financial queries Real-time Processing**: Documents processed within minutes of upload Benefits Time Savings**: Eliminates manual data entry from financial documents Accuracy**: AI-powered extraction reduces human error Organization**: Automatic file naming and categorization Insights**: Query financial data using natural language Compliance**: Maintains organized records for accounting and audit purposes Scalability**: Handles growing document volumes without additional overhead This workflow transforms tedious financial document processing into an automated, intelligent system that grows with your business needs.

CRM Contact Sync with Mailchimp and Pipedrive This workflow keeps your contact records perfectly aligned between your CRM (e.g. HubSpot / Salesforce / Pipedrive) and Mailchimp. Whenever a contact is created or updated in one system, the automation propagates the change to the other platform, ensuring every email address, phone number and custom field stays in sync. Pre-conditions/Requirements Prerequisites n8n instance (self-hosted or cloud) Community nodes: Pipedrive, Mailchimp A dedicated service account in each platform with permission to read & write contacts Basic understanding of how webhooks work (for CRM → n8n triggers) Required Credentials Pipedrive API Token** – Used for creating, updating and searching contacts in Pipedrive Mailchimp API Key** – Grants access to lists/audiences and contact operations CRM Webhook Secret** (optional) – If your CRM supports signing webhook payloads Specific Setup Requirements | Environment Variable | Description | Example | |----------------------|--------------------------------------|---------------------------------| | PIPEDRIVE_API_KEY | Stored in n8n credential manager | 123abc456def | | MAILCHIMP_API_KEY | Stored in n8n credential manager | us-1:abcd1234efgh5678 | | MAILCHIMP_DC | Mailchimp Datacenter (sub-domain) | us-1 | | CRM_WEBHOOK_URL | Generated by the Webhook node | https://n8n.myserver/webhook/... | How it works This workflow keeps your contact records perfectly aligned between your CRM (e.g. HubSpot / Salesforce / Pipedrive) and Mailchimp. Whenever a contact is created or updated in one system, the automation propagates the change to the other platform, ensuring every email address, phone number and custom field stays in sync. Key Steps: Inbound Webhook**: Receives contact-change events from the CRM. Split in Batches**: Processes contacts in chunks to stay within API rate limits. Mailchimp Upsert**: Adds or updates each contact in the specified Mailchimp audience. Pipedrive Upsert**: Mirrors the same change in Pipedrive (or vice-versa). Merge & IF nodes**: Decide whether to create or update a contact by checking existence. Error Trigger**: Captures any API failures and posts them to the configured alert channel. Set up steps Setup Time: 15-25 minutes Create credentials • In n8n, add new credentials for Pipedrive and Mailchimp using your API keys. • Name them clearly (e.g. “Pipedrive Main”, “Mailchimp Main”). Import the workflow • Download or paste the JSON template into n8n. • Save the workflow. Configure the Webhook node • Set HTTP Method to POST. • Copy the generated URL and register it as a webhook in your CRM’s contact-update events. Map CRM fields • Open the first Set node and match CRM field names (firstName, lastName, email, etc.) to the standard keys used later in the flow. Select Mailchimp Audience • In the Mailchimp node, choose the audience/list that should receive the contacts. Define Pipedrive Person Fields • If you have custom fields, add them in the Pipedrive node’s Additional Fields section. Enable the workflow • Turn the workflow from “Inactive” to “Active”. • Send a test update from the CRM to verify that contacts appear in both Mailchimp and Pipedrive. Node Descriptions Core Workflow Nodes: Webhook** – Accepts contact-change payloads from the CRM. Set** – Normalises field names to a common schema. SplitInBatches** – Loops through contacts in controllable group sizes. HTTP Request** – Generic calls (e.g. HubSpot/Salesforce look-ups when required). Pipedrive** – Searches for existing persons; creates or updates accordingly. Mailchimp** – Performs contact upsert into an audience. If** – Branches logic on “contact exists?”. Merge** – Re-assembles branch data back into a single execution line. Code** – Small JS snippets for complex field transformations. Error Trigger** – Listens for any node failure and routes it to alerts. StickyNote** – Documentation hints inside the workflow. Data Flow: Webhook → Set (Normalise) → SplitInBatches SplitInBatches → Mailchimp (Get) → If (Exists?) → Mailchimp (Upsert) SplitInBatches → Pipedrive (Search) → If (Exists?) → Pipedrive (Upsert) Merge → End / Success Customization Examples Add a Tag to Mailchimp contacts // Place inside a Code node before the Mailchimp Upsert item.tags = ['Synced from CRM', 'High-Value']; return item; Apply a Deal Stage in Pipedrive // Pipedrive node → Additional Fields "deal": { "title": "New Lead from Mailchimp", "stage_id": 2 } Data Output Format The workflow outputs structured JSON data: { "id": 1472, "status": "updated", "email": "alex@example.com", "source": "CRM", "synced": { "pipedrive": "success", "mailchimp": "success" }, "timestamp": "2024-04-27T10:15:00Z" } Troubleshooting Common Issues HTTP 401 Unauthorized – Verify that the API keys are still valid and have not been revoked. Webhook receives no data – Double-check that the CRM webhook URL matches exactly and that the event is enabled. Performance Tips Batch contacts in groups of 50-100 to respect Mailchimp & Pipedrive rate limits. Use Continue On Fail in non-critical nodes to prevent the entire run from stopping. Pro Tips: Map your CRM’s custom fields once in the Set node to avoid touching each downstream node. Use Merge+If pattern to keep “create vs update” logic tidy. Enable workflow execution logs only in development to reduce storage usage. *Community Template Disclaimer: This workflow is provided by the n8n community “as is”. n8n GmbH makes no warranties regarding its performance, security or compliance. Always review and test in a development environment before using it in production.*

Complete Telegram Trivia Bot with AI Question Generation Build a fully-featured Telegram trivia bot that automatically generates fresh questions daily using OpenAI and tracks user progress with NocoDB. Perfect for communities, education, or entertainment! ✨ Key Features 🤖 AI Question Generation: Automatically creates 40+ new trivia questions daily across 8 categories 📊 Smart User Management: Tracks scores, prevents question repeats, maintains leaderboards 🎮 Game Mechanics: Star-based difficulty scoring, answer history, progress tracking 🏆 Competitive Elements: Real-time leaderboards with emoji rankings and user positioning 🛡️ Robust Architecture: Error handling, state management, and data validation 🚀 Perfect For Community Engagement**: Keep Telegram groups active with daily trivia challenges Educational Content**: Create learning experiences with categorized questions Business Applications**: Employee training, customer engagement, lead generation Personal Projects**: Learn n8n automation while building something fun 📱 Supported Commands /start - Welcome new users with setup instructions /question - Get personalized trivia questions (never repeats correctly answered ones) /score - View current points and statistics /leaderboard - See top 10 players with rankings /stats - Detailed accuracy and performance metrics /help - Complete command reference 🔧 How It Works User Journey: User sends /question command to bot System checks their answer history to avoid repeats Displays fresh question with multiple choice options Processes answer, updates score based on difficulty stars Saves complete answer history for future filtering AI Content Pipeline: Daily scheduler triggers question generation OpenAI creates 5 questions per category (8 categories total) Questions automatically saved to NocoDB with difficulty ratings Content includes explanations and proper formatting 🛠️ Set Up Steps Prerequisites: n8n instance (cloud or self-hosted) NocoDB database (free tier works) OpenAI API key (Not required if you want to add questions yourself) Telegram bot token Database Setup: Create 3 NocoDB tables with the exact field specifications provided in the sticky notes. The workflow includes complete schema documentation. Configuration Time: ~15 minutes for database setup + API keys Detailed Setup Instructions: All setup steps, database schemas, and configuration details are documented in the workflow's sticky notes for easy implementation. 📈 Advanced Features Question History Tracking**: Users never see correctly answered questions again Difficulty-Based Scoring**: 1-5 star rating system with corresponding points Category Management**: 8 different trivia categories for variety State Management**: Proper game flow with idle/waiting states Error Handling**: Graceful fallbacks for all edge cases Scalable Architecture**: Supports unlimited concurrent users 🎯 Business Applications Lead Generation**: Capture user data through engaging trivia Employee Training**: Create custom questions for onboarding Customer Engagement**: Keep users active in your Telegram community Educational Tools**: Subject-specific learning with progress tracking Event Activation**: Conferences, workshops, or team building 💡 Customization Options Modify question categories for your niche Adjust scoring systems and difficulty levels Add custom commands and features Integrate with other platforms or APIs Create specialized question sets 🔗 Get Started Ready to build your own AI-powered trivia bot? Start with n8n and follow the comprehensive setup guide included in this workflow template. Next Steps: Import this workflow template Follow the database setup instructions in sticky notes Configure your API credentials Test with sample questions Launch your trivia bot! Turn your friend group into trivia champions with AI-generated questions that spark friendly competition!

Who’s it for This template is built for founders, sales teams, agencies, consultants, and growth operators who want a fully automated way to discover high-intent companies showing buying signals such as funding, hiring, launches, or expansion — without manual research. It’s ideal for outbound sales, partnership scouting, market intelligence, and lead generation. What it does / How it works This workflow automates daily business signal monitoring and opportunity detection. It runs on a daily schedule and collects data from multiple sources: LinkedIn** X** Product Hunt** CrunchBase** Google News** All incoming data is: Merged and normalized into a single unified feed Cleaned and deduplicated Passed to an AI agent powered by Google Gemini The AI agent: Filters only relevant events (Funding, Launch, Expansion, Hiring) Generates a concise summary Explains why the event represents a business opportunity Based on the AI classification: Relevant opportunities continue through the workflow Irrelevant noise is automatically discarded For each qualified opportunity: Company/contact data is enriched via an external API The opportunity is saved to Google Sheets A real-time alert is sent via Telegram All logic runs automatically end-to-end. Requirements Google News (NewsAPI) API key Twitter/X API credentials Product Hunt API credentials Crunchbase RSS feed access Google Sheets OAuth2 credentials Telegram Bot credentials Google Gemini (PaLM) API credentials (Optional) Contact enrichment API How to set up Import the workflow into n8n. Connect all required credentials: Google Gemini Google Sheets Telegram External APIs (News, X, Product Hunt) Replace placeholders: Google Sheet ID and range Telegram Chat ID Enrichment API URL (if used) Adjust search queries if needed. Run the workflow once manually to test. Enable the Daily Trigger to activate automation. How to customize the workflow Modify AI prompts to refine opportunity criteria Add new data sources (LinkedIn, Reddit, Hacker News) Change schedule frequency (hourly, weekly) Log opportunities into a CRM instead of Sheets Add email or Slack alerts Expand enrichment logic for deeper company insights This workflow transforms scattered startup news into a clean, daily stream of actionable business opportunities — fully automated.

🧑‍💼 Who is this for? This workflow is for any YouTube user who wants to bulk delete all playlists from their own channel — whether to start fresh, clean up old content, or prepare the account for a new purpose. It’s useful for: Creators reorganizing their channel People transferring content to another account Anyone who wants to avoid deleting playlists manually one by one 🧠 What problem is this workflow solving? YouTube does not offer a built-in way to delete multiple playlists at once. If you have dozens or hundreds of playlists, removing them manually is extremely time-consuming. This workflow automates the entire deletion process in seconds, saving you hours of repetitive effort. ⚙️ What this workflow does Connects to your YouTube account Fetches all playlists you’ve created (excluding system playlists) Deletes them one by one** automatically > ⚠️ This action is irreversible. Once a playlist is deleted, it cannot be recovered. Use with caution. 🛠️ Setup 🔐 Create a YouTube OAuth2 credential in n8n for your channel. 🧭 Assign the credential to both YouTube nodes. ✅ Click “Test workflow” to execute. > 🟨 By default, this workflow deletes everything. If you want to be more selective, see the customization tips below. 🧩 How to customize this workflow to your needs ✅ Add a confirmation flag Insert a Set node with a custom field like confirm_delete = true, and follow it with an IF node to prevent accidental execution. ✂️ Delete only some playlists Add a Filter node after fetching playlists — you can match by title, ID, or keyword (e.g. only delete playlists containing “old”). 🛑 Add a pause before deletion Insert a Wait or NoOp node to give you a moment to cancel before it runs. 🔁 Adapt to scheduled cleanups Use a Cron trigger if you want to periodically clear temporary playlists.

🎯 LinkedIn ICP Lead Qualification Automation Automatically identify and qualify ideal customer prospects from LinkedIn post reactions using AI-powered profile analysis and intelligent data enrichment. Perfect for sales teams and marketing professionals who want to convert LinkedIn engagement into qualified leads without manual research. This workflow transforms post reactions into actionable prospect data with AI-driven ICP classification. Good to know LinkedIn Safety**: Only use cookie-free Apify actors to avoid account detection and suspension risks Daily Processing Limits**: Scrape maximum 1 page of reactions per day (50-100 profiles) to stay under LinkedIn's radar Apify actors cost approximately $0.01-0.05 per profile scraped - budget accordingly for daily processing Includes intelligent rate limiting to prevent API restrictions and maintain LinkedIn account safety AI classification requires clear definition of your Ideal Customer Profile criteria Processing too many profiles or running too frequently will trigger LinkedIn's anti-scraping measures Always monitor your LinkedIn account health and Apify usage patterns for any warning signs How it works Scrapes LinkedIn post reactions using Apify's specialized actor to identify engaged users Extracts and cleans profile data including names, job titles, and LinkedIn URLs Checks against existing Airtable records to prevent duplicate processing and save costs Creates new prospect records with basic information for tracking purposes Enriches profiles with comprehensive LinkedIn data including company details and experience Aggregates and formats profile data for AI analysis and classification Uses AI to analyze prospects against your ICP criteria with detailed reasoning Updates records with ICP classification results and extracted email addresses Implements smart batching and delays to respect API rate limits throughout the process How to use IMPORTANT**: Select cookie-free Apify actors only to avoid LinkedIn account suspension Set up Apify API credentials in both HTTP Request nodes for safe LinkedIn scraping Configure Airtable OAuth2 authentication and select your prospect tracking base Replace the LinkedIn post URL with your target post in the initial scraper node Daily Usage**: Process only 1 page of reactions per day (typically 50-100 profiles) maximum Customize the AI classification prompt with your specific ICP criteria and job titles Test with a small batch first to verify setup and monitor both API costs and LinkedIn account health Schedule workflow to run daily rather than processing large batches to maintain account safety Requirements Apify account with API access and sufficient credits for profile scraping Airtable account with OAuth2 authentication configured OpenAI or compatible AI model credentials for prospect classification LinkedIn post URL with reactions to analyze (minimum 10+ reactions recommended) Clear definition of your Ideal Customer Profile criteria for accurate AI classification Customising this workflow Safety First**: Always verify Apify actors are cookie-free before configuring to protect your LinkedIn account Modify ICP classification criteria in the AI prompt to match your specific target customer profile Set up daily scheduling (not hourly/frequent) to respect LinkedIn's usage patterns and avoid detection Adjust rate limiting delays based on your comfort level with LinkedIn scraping frequency Add additional data fields to Airtable schema for storing custom prospect information Integrate with CRM systems like HubSpot or Salesforce for automatic lead import Set up Slack notifications for new qualified prospects or daily summary reports Create email marketing sequences in tools like Mailchimp for nurturing qualified leads Add lead scoring based on company size, industry, or engagement level for prioritization Consider rotating between different LinkedIn posts to diversify your prospect sources while maintaining daily limits

This workflow manages subscription billing reminders and data updates via Telegram. It runs daily at 8:00 AM to check for upcoming due subscriptions, formats relevant information, and sends reminders to users. It also processes user messages for subscription management—adding, updating, or retrieving billing info—using AI-powered natural language understanding. Main outcomes include automated subscription tracking, timely reminders, and conversational interaction through Telegram, reducing manual tracking efforts and improving billing accuracy. Automation Benefits Time & Cost Savings Manual Process: Several hours/week spent managing subscriptions and reminders manually. Automated Process: Workflow completes checks, reminders, and data updates in under a minute. Time Savings: Saves approximately 5 hours weekly, translating to significant productivity gains and cost reduction. ROI: Automation pays for itself within the first month due to saved labor. Error Reduction: Minimized manual entry errors, ensuring accurate billing records and timely reminders. Business Impact Solves the problem of manual subscription tracking and reminders. Scales effortlessly as subscription list grows. Opens new opportunities for proactive customer engagement, personalized messaging, and integrated billing insights. Setup Guide Prerequisites Google Sheets account with subscription data sheet. OpenAI API key with access to GPT-4. Telegram bot token with messaging permissions. Email SMTP setup if email reminders are used. API Configuration Google Sheets: Generate OAuth2 credentials, enable Sheets API, and authorize access. OpenAI: Create API key, set model to GPT-4, and test connectivity. Telegram: Create bot via BotFather, retrieve token, and set webhook URL. Webhook URL: Use the provided URL in the Telegram bot settings. Node-by-Node Setup OpenAI Chat Model: Enter API credentials, select GPT-4 model. Google Sheets: Input spreadsheet ID, sheet name, and ensure correct permissions. Telegram Nodes: Insert chat ID, message parsing, and response formatting. Schedule Trigger: Confirm cron expression for daily execution. For AI nodes, test with sample messages to verify formatting and extraction. Testing & Validation Run workflow manually. Confirm data is retrieved, processed, and responses sent. Verify subscription updates in Google Sheets. Check Telegram chats for correct message flow. N8N Documentation References Google Sheets Node OpenAI Node Telegram Node Schedule Trigger Maintenance & Troubleshooting Regular Maintenance (Monthly) Check API credentials and renew tokens if expired. Monitor workflow logs for errors. Review Google Sheets data for consistency. Update API keys when new versions or permissions are granted. Verify currency conversion accuracy periodically. Common Issues & Solutions Workflow not triggering: check schedule settings and webhook URLs. Data not updating: verify Google Sheets credentials and permissions. Incorrect responses: test AI prompt inputs and outputs. API failures: regenerate API keys or check quota limits. Reconfigure nodes if external API changes. Monitoring & Alerts Set up email or Slack alerts for failures. Regularly review execution logs. Track key metrics like successful runs, error rates, and response times. Support & Escalation Check n8n logs first for errors. Export workflow for support if needed. Use n8n community forums for common issues. Contact API providers for account-specific problems. Emergency procedures: restart workflow, regenerate tokens. Updates & Improvements Review workflow performance quarterly. Optimize AI prompts for better accuracy. Backup workflow configurations before major changes. Incorporate user feedback for feature enhancements.

🚀 Channable + Google Ads + Relevance AI: Scalable AI Workflow for Automated Ad Copy Generation & Publishing 🧩 Overview This workflow automates the entire ad creation process for Google Ads by integrating product data, AI-generated copy, compliance checks, and publication into your marketing pipeline. It connects n8n, Relevance AI, Google Sheets, and optionally Channable to: Fetch product data from your catalog Generate Google Text Ad headlines and descriptions using Relevance AI Validate character limits and ensure Google Ads compliance Route non-compliant ads to a Slack review channel Save compliant, ready-to-publish ads in Google Sheets Notify your marketing team automatically after each generation cycle 🧠 Key Benefits ✅ 100% automated ad copy pipeline ✅ AI-generated, human-quality Google Ads text ✅ Built-in compliance verification (Google Ads policy) ✅ Google Sheet integration for team review ✅ Daily automatic schedule (zero manual effort) ✅ Slack alerts for QA and transparency ✅ Modular design — extendable for Shopping and Performance Optimization ✅ Scalable for 10 → 10,000+ product ads ⚙️ System Architecture Tech Stack n8n** – Automation Orchestrator Relevance AI** – AI tools for copy generation and policy compliance Google Sheets** – Data storage and team collaboration Slack** – Real-time alerts and notifications (Optional) Channable – Product feed integration 🧭 Workflow Logic Daily Trigger (00:00) ⬇️ 1️⃣ Get Product Feed (Channable or custom API) ⬇️ 2️⃣ Split Into Batches (50 products each) ⬇️ 3️⃣ Generate Ad Copy (Relevance AI tool → Claude 3.5 prompt) ⬇️ 4️⃣ Validate Character Limits (JS node: max 30 headline / 90 description) ⬇️ 5️⃣ Compliance Check (Relevance AI agent → Google Ads policies) ⬇️ 6️⃣ IF Compliant → CSV / Google Sheets ↳ ❌ Non-Compliant → Slack Alert ⬇️ 7️⃣ Aggregate Batches + Generate CSV ⬇️ 8️⃣ Save to Google Sheets (“Generated Ads” tab) ⬇️ 9️⃣ Slack Notification → Summary Report 📋 Environment Variables Set these in n8n → Settings → Variables → Add Variable Copy-paste from your ENVIRONMENT_VARIABLES_CORRECTED.txt. Includes: ✅ Relevance AI region, API key, tool & agent IDs ✅ Google Ads, Merchant Center, and Sheets credentials ✅ Slack channel name ✅ Optional Channable endpoint Example: RELEVANCE_AI_API_URL=https://api-f1db6c.stack.tryrelevance.com/latest RELEVANCE_TOOL_AD_COPY_ID=bueQG8io04dw RELEVANCE_AGENT_COMPLIANCE_ID=xT29mQ4QKsl GOOGLE_SHEET_ID=1q2w3e4r5t6y7u8i9o0p SLACK_CHANNEL=#google-ads-automation 🏗️ Node-by-Node Breakdown | Node | Description | Endpoint / Logic | | -------------------------------------- | ----------------------------------------------- | ----------------------------------------------------------------------------- | | 🕓 Schedule Trigger | Runs daily at 00:00 | Cron 0 0 * * * | | 📦 Get Product Feed | Pulls product data from Channable or custom API | GET {{$env.CHANNABLE_API_URL}}/v1/projects/{{$env.PROJECT_ID}}/items | | 🧮 Split Into Batches | Processes 50 products at a time | Avoids rate limits | | ✍️ Generate Ad Copy (Relevance AI) | Calls AI tool for each product | POST {{$env.RELEVANCE_AI_API_URL}}/tools/google_text_ad_copy_generator/run | | 🔍 Validate Character Limits | JS validation (≤30 headline / ≤90 description) | Truncates smartly | | 🧠 Compliance Check Agent | Verifies Google Ads compliance | POST {{$env.RELEVANCE_AI_API_URL}}/agents/google_ads_compliance_checker/run | | ⚖️ IF Compliant | Routes APPROVED vs REJECTED | "contains 'APPROVED'" | | 💾 Format for CSV | Formats compliant ads for export | Maps ID, headline, desc, URLs | | 📊 Aggregate Batches | Combines all results | Merges datasets | | 🧱 Generate CSV File | Converts JSON → CSV | Escaped string-safe format | | 📑 Save to Google Sheets | Saves reviewed ads | Sheet: Generated Ads | | 📢 Slack Notification (Success) | Posts completion summary | Shows ad count, timestamp | | 🚨 Slack Alert (Non-Compliant) | Notifies team for review | Includes issues, category | 🔑 API Authentication Setup 🔹 Relevance AI Create “HTTP Header Auth” credential Header Name: Authorization Header Value: Bearer {{$env.RELEVANCE_AI_API_KEY}} 🔹 Google Sheets Credential type: “Google OAuth2 API” Scopes: https://www.googleapis.com/auth/spreadsheets https://www.googleapis.com/auth/drive.file 🔹 Slack Create Slack App → Add Bot Token Scopes → chat:write Paste token in n8n “Slack API” credential. 🔹 (Optional) Channable Header Auth: Bearer {{$env.CHANNABLE_API_TOKEN}} 🧩 Google Sheet Template Sheet name: Generated Ads Columns: | product_id | headline | description | final_url | display_url | generated_at | Optional: Add compliance_status or notes columns for QA. ⚙️ Testing Procedure Manual Trigger: Disable the schedule → click “Execute Workflow”. Batch Size: Start small (3 products). Expected Output: ✅ Ad copy generated ✅ Character limits validated ✅ Slack alerts for rejects ✅ Google Sheet filled Check logs in Executions for errors. Re-enable the cron trigger after successful validation. 🧾 Example Output | product_id | headline | description | final_url | display_url | generated_at | | ---------- | ------------------ | --------------------------------------------- | ------------------------------------------------ | ----------- | -------------------- | | 12243 | “Eco Bamboo Socks” | “Soft, breathable comfort for everyday wear.” | https://shop.com/socks | shop.com | 2025-10-22T00:00:00Z | 📬 Slack Alert Templates ✅ Success Notification ✅ Google Ads Generation Complete 📊 Summary: • Total Ads Generated: 50 • Saved to Google Sheets: Generated Ads • Timestamp: 2025-10-22T00:00:00Z ⚠️ Non-Compliant Alert ⚠️ Non-Compliant Ad Flagged Product: Bamboo Socks Issues: Contains “Free Shipping” Headline too long Timestamp: 2025-10-22T00:00:00Z 🧰 Maintenance & Monitoring | Frequency | Task | | --------- | -------------------------------- | | Daily | Check Slack alerts for rejects | | Weekly | Review ad performance metrics | | Monthly | Update Relevance AI prompts | | Quarterly | Refresh API tokens and variables | 📊 Success Metrics ✅ Compliance approval rate: >85% 🚫 Disapproval rate: <5% 📈 CTR improvement: +15–25% ⏱️ Time saved: 10–15 hours/week 🌐 Scalable: 1,000+ ads/day 🪜 Next Steps Deploy and monitor for 7 days. After 30 days → activate Workflow 2: Performance Optimization Loop. Extend to Shopping Feed Optimization. Add multi-language generation using Relevance AI. Integrate Google Ads API publishing (full automation). 🔗 Resources n8n Docs Relevance AI Docs Google Ads API Merchant API Channable Help 🎉 Conclusion You now have a production-ready, scalable AI-powered ad generation system integrating Channable, Google Ads, and Relevance AI — built entirely on n8n. This delivers: 💡 AI creativity at scale ✅ Google Ads policy compliance ⚙️ Hands-free daily automation 📊 Transparent reporting and collaboration > Start small → validate → scale to 10,000+ ads per day. > Within weeks, you’ll have a self-learning, always-on ad pipeline driving consistent performance.

Stop managing onboarding as a checklist. Let automation handle the infrastructure so you can focus on the people. Manually provisioning a new hire's digital workspace is a recipe for human error and "Day 1" friction. This workflow orchestrates the entire Microsoft 365 onboarding stack in a single, parallel execution. It doesn't just run tasks; it verifies them, handles API failures gracefully, and reports a live status dashboard directly back to HR. How it works The workflow operates in four synchronized stages: Ingestion & Smart Parsing: The workflow triggers via Microsoft Agent 365 (Teams @mentions or Outlook emails). A JavaScript engine parses the unstructured HR request using flexible regex to extract the employee's name, role, department, and start date. True Parallel Execution: Once parsed, the data fans out into four independent branches: SharePoint: Creates a structured entry in the HR Onboarding master list. OneDrive: Provisions a personal root folder and automatically generates 5 templated sub-folders (Contracts, Equipment, Training, etc.). Teams: Creates a private onboarding channel for the employee's cohort. AI Communications: Leverages Claude 3.5 Sonnet to draft a warm, personalized welcome email based on the specific role and department, then sends it via Outlook. Fault-Tolerant Merging: The workflow uses a multi-stage Merge Cascade. Every node is configured with Continue on Fail logic. This means if one branch hits an API limit or error, it passes an error object forward rather than crashing the entire process. Verified Status Delivery: The final stage gathers all outputs, validates whether each task actually returned a success ID/URL, and generates a Microsoft Teams Adaptive Card. HR receives a clean summary with live links to provisioned assets and clear ❌ markers for any step that requires manual intervention. Key Benefits Zero-Hang Resilience:** Built with "Continue on Fail" logic. If SharePoint is down, the employee still gets their welcome email and OneDrive folder. Live Asset Verification:** The final report doesn't just "hope" things worked; it checks for live IDs and URLs from the Microsoft Graph API before showing a green checkmark. Parallel Performance:** Provisioning happens concurrently. The total wait time is only as long as the slowest single task (usually the AI generation). Global Error Handling:* Includes a dedicated *Error Trigger** flow that catches catastrophic system crashes and alerts IT with a full stack trace and the name of the failed node. Setup Credentials: Add credentials for Microsoft SharePoint, OneDrive, Teams, Outlook, and Anthropic (Claude). Environment Config: Open the Parse New Hire + Config node and replace the placeholder IDs with your specific SharePoint Site ID, List ID, and Teams Team ID. Entra ID Permissions: Ensure your registered Azure App has the necessary Graph API permissions (Sites.ReadWrite.All, Files.ReadWrite.All, Channel.Create, Mail.Send). Activate: Turn the workflow on and trigger it by @mentioning your Agent in Teams with new hire details. Who this is for IT Operations Teams** looking to standardize M365 provisioning without expensive third-party SaaS. HR Departments** that want immediate, transparent feedback on onboarding progress. Managed Service Providers (MSPs)** providing automated "Hiring-as-a-Service" for their clients. Required APIs & Credentials Microsoft 365 Stack:** SharePoint, OneDrive, Teams, Outlook. AI Provider:** Anthropic (Claude 3.5 Sonnet) or OpenAI (GPT-4o). How to customise it Add More Branches:** Easily add nodes for Jira, Slack, or HRIS updates by fanning out from the Parser and adding them to the Merge Cascade. Adjust Folder Templates:** Modify the Build SubFolder Paths code node to match your company's specific folder structure. Modify AI Tone:** Change the System Message in the AI node to match your company's unique brand voice. Custom Triggers:** Swap the Agent 365 trigger for a Webhook (to connect to Workday/HiBob) or a Typeform trigger.

💥 Scrape leads from businesses with Claude and Apify to Gmail outreach 📄 Documentation: Notion Guide Automatically scrape business leads with Claude Co-Work and Apify, send the data to n8n through a webhook, store it in Google Sheets, generate personalized outreach emails, and send them automatically with Gmail. This workflow combines lead scraping, lead qualification, data organization, AI copywriting, and cold outreach into one automated pipeline. Who is this for? This template is ideal for: Agencies offering SEO, automation, web design, or lead generation services Freelancers looking to automate prospecting Sales teams doing cold outreach Entrepreneurs searching for local business leads Anyone using AI to scale outbound email campaigns What problem is this workflow solving? / Use case Manual prospecting is slow and repetitive. Most businesses waste hours every week: Searching for leads manually Copying business data into spreadsheets Writing cold emails one by one Sending outreach emails manually This workflow solves that by automating the full process from lead discovery to email delivery. Example use case: Use Claude Co-Work with Apify to find restaurants in Paris Scrape lead data such as business name, address, website, phone number, email, and booking status Automatically send the collected leads to n8n through a webhook Save lead data in Google Sheets Generate a personalized outreach email for each valid lead Send the email automatically with Gmail What this workflow does Claude Co-Work uses Apify to scrape business leads and collect their data Claude Co-Work automatically triggers the n8n webhook with the scraped lead data n8n splits each business lead into separate items n8n appends lead data to Google Sheets n8n checks whether the lead has an email address n8n uses Claude to generate a personalized outreach email n8n extracts the email subject and body automatically n8n sends the email via Gmail Expected outcome: Automated lead scraping with Claude Co-Work and Apify Organized lead database in Google Sheets Personalized AI-generated outreach emails Automated email sending Faster prospecting with less manual work Setup Before using this template, create and connect the following accounts: Google Sheets account Gmail account Anthropic / Claude account required to run Claude Co-Work Apify account required for scraping lead data Then configure: 1. Claude Co-Work + Apify Set up Claude Co-Work so it can use Apify to scrape lead data before n8n starts. Claude Co-Work should collect fields such as: collection_date business_category region_state city_name street_address business_name business_description website_url phone_number customer_rating email_address contact_status 2. Webhook Configure Claude Co-Work to send the scraped data to the n8n Webhook node. The webhook receives the lead payload and starts the workflow automatically. 3. Google Sheets Replace: YOUR_ID_GOOLE_SHEETS YOUR_ID-sheet_GOOLE_SHEETS With your own Google Sheet ID and Sheet tab ID. 4. Claude Prompt Customize the outreach message inside the Outreach email copywriter node. How to customize this workflow to your needs Change target industry Replace restaurants with: Dentists Lawyers Real estate agencies Coaches Ecommerce stores Local businesses Change outreach offer Instead of SEO or booking systems, promote: Web design AI chatbots Lead generation Marketing automation CRM setup Ads management Notes Claude Co-Work and Apify handle the lead scraping step before n8n starts The webhook is triggered automatically after the lead data is collected Phone numbers are automatically cleaned before saving to Google Sheets Only leads with email addresses continue to outreach Gmail errors continue without stopping the workflow Built for users who want to automate scraping, prospecting, and outbound sales with AI. 🎥 Watch This Tutorial 👋 Need help or want to customize this? 📩 Contact: LinkedIn 📺 YouTube: @DRFIRASS 🚀 Workshops: n8n courses (in French) Need help customizing? Contact me for consulting and support : Linkedin / Youtube / 🚀 n8n courses (in French)

KYC Risk Profiling Form with n8n, OpenAI, Google Sheets & Slack This workflow captures customer KYC details through an n8n Form, validates the submitted input, sends the cleaned data to OpenAI for automated risk profiling, formats the AI response and routes the result based on whether the applicant is classified as High Risk or not. High-risk cases are saved separately and trigger a Slack alert, while lower-risk cases are simply logged into Google Sheets for recordkeeping and review. Quick Implementation Steps Import the workflow into n8n. Configure your OpenAI credentials. Connect your Google Sheets credentials. Connect your Slack credentials. Open the KYC Form Submission node and publish the form. Make sure your form collects these exact fields: Income Geography Occupation Configure the two Google Sheets nodes for: High-risk records Normal-risk records Set the Slack channel for high-risk alerts. Test the form using sample submissions. Activate the workflow once everything is verified. What It Does The KYC Risk Profiling Form workflow automates the first layer of customer risk assessment by collecting basic applicant information and using AI to classify the risk level. Instead of manually reviewing every entry, the workflow accepts a form submission, validates the data and sends it to an AI model that returns a structured risk decision. Once the AI analysis is returned, the workflow extracts the important risk fields, formats them into a clean structure and checks whether the applicant should be considered High Risk. If the risk category is high, the record is routed into a separate review path where it is saved and also sent to Slack as an alert. If the customer is Low or Medium risk, the record is saved without triggering an alert. This makes the workflow useful as a lightweight KYC intake and triage layer, especially for teams that want faster screening without building a full compliance platform. Who It's For This workflow is useful for businesses and teams that need a simple way to screen customer or applicant risk during intake. Ideal users include: Compliance teams KYC / AML operations teams Risk analysts Fintech startups Financial service providers Customer onboarding teams Internal operations teams Workflow automation teams building compliance intake systems It is especially helpful for teams that want to automate basic risk profiling before manual review. Requirements to Use This Workflow Before using this workflow, make sure you have the following: Required Platforms & Accounts n8n account OpenAI API access** Google Sheets** Slack** Required n8n Credentials You will need to configure: OpenAI credentials** Google Sheets OAuth2 credentials** Slack credentials** Required Form Fields This workflow expects the form to collect the following exact field names: Income Geography Occupation These names are important because they are used directly inside the validation and AI nodes. Required Google Sheets Structure The JSON shows two separate Google Sheets append nodes: 1) High Risk Sheet Used by: Save High Risk Record Recommended columns based on workflow output: riskCategory income geography redFlags 2) Normal Risk Sheet Used by: Save Normal Risk Record Recommended columns based on workflow output: riskCategory redFlags income geography occupation Required Slack Setup You must connect a Slack workspace and choose a valid destination channel in: Send High Risk Alert How It Works & Setup Guide Step 1 — Import the Workflow into n8n Import the provided JSON file into your n8n workspace. After import, the workflow will appear as: KYC-RISK-FORM Step 2 — Understand the Workflow Flow This workflow follows the structure below: KYC Form Submission → Validate KYC Input → Ai Assign The Risk Category → Process AI Response → Format Final Data → Check High Risk ├── High Risk → Format the Data For Sheet → Save High Risk Record → Send High Risk Alert └── Low / Medium Risk → Save Normal Risk Record This structure creates a simple but useful KYC screening pipeline. Step 3 — Configure the KYC Form Node: KYC Form Submission This is the entry point of the workflow. It creates a user-facing form inside n8n. Form title in the workflow: KYC_FORM Fields configured in the form 1) Income Type: number Required: true 2) Geography Type: text Required: true 3) Occupation Type: text Required: true What to do Open the form node Publish the form Share the form link with internal users or customers Keep the field names unchanged unless you also update the downstream nodes This form acts as the customer KYC intake point. Step 4 — Validate the Submitted Input Node: Validate KYC Input This Code node validates the incoming form data before it is sent to AI. Validation logic in this node The workflow checks: Income must be greater than 0 Geography must not be empty Occupation must not be empty It also performs these actions Converts Income into a numeric value Trims extra spaces from text inputs Converts Geography to uppercase Adds: isValid validationErrors createdAt Output fields created Income Geography Occupation isValid validationErrors createdAt Important implementation note Although this node generates isValid and validationErrors, the current JSON does not include an IF node to stop invalid submissions before they reach the AI step. That means invalid or incomplete records may still continue through the workflow unless you manually extend it later. So while validation is present, it is currently used as a data preparation step, not as a hard blocking gate. Step 5 — Configure OpenAI Risk Analysis Node: Ai Assign The Risk Category This node sends the validated KYC data to OpenAI (gpt-4o-mini) for automated risk classification. What the AI is instructed to do The prompt asks the model to: Assign a risk category: Low Medium High List any red flags found Return the original applicant details exactly as received Applicant fields sent to AI Income Geography Occupation Risk rules defined in the workflow The AI is told to classify as High Risk if: the geography is a sanctioned country the geography is a FATF blacklisted country the geography is a high-risk jurisdiction the occupation is in a high-risk sector such as: cryptocurrency gambling arms trade shell companies politically exposed roles The AI is told to classify as Medium Risk if: one moderate concern exists The AI is told to classify as Low Risk if: no major red flags exist Expected AI output format The prompt explicitly requests valid JSON in this structure: { "riskCategory": "High", "redFlags": ["flag1", "flag2"], "income": "5000", "geography": "INDIA", "occupation": "Software Engineer" } This structured format is important because the next node depends on it. Step 6 — Parse and Clean the AI Output Node: Process AI Response This Code node processes the raw AI response and converts it into clean workflow fields. What this node does Reads the AI output text Removes markdown code fences if present Parses the returned JSON Extracts important fields Creates fallback values if parsing fails Fields extracted riskCategory redFlags redFlagText income geography occupation Error handling included If the AI output cannot be parsed correctly, the node returns: riskCategory = Unknown empty redFlags fallback text values an error field with the parsing error message This makes the workflow more stable when handling imperfect AI responses. Step 7 — Format the Final Structured Record Node: Format Final Data This Set node standardizes the processed AI result into a cleaner output structure. Fields included riskCategory redFlags income geography occupation This step prepares the record for final risk routing. Step 8 — Route Based on High Risk Status Node: Check High Risk This IF node checks whether: riskCategory == High Routing behavior If riskCategory = High The record goes to the high-risk path: Format the Data For Sheet Save High Risk Record Send High Risk Alert If riskCategory is not High The record goes to the normal-risk path: Save Normal Risk Record This is the main decision point in the workflow. Step 9 — Prepare the High-Risk Sheet Record Node: Format the Data For Sheet This Set node formats high-risk records before saving them. Fields included in this path riskCategory income geography redFlags Important note This high-risk output currently does not include occupation in this specific formatting node, even though it exists earlier in the workflow. That means if you want occupation saved into the high-risk sheet, you would need to add it manually later. Step 10 — Save High-Risk Cases Node: Save High Risk Record This Google Sheets node appends high-risk KYC records to a dedicated sheet. What to do Connect your Google Sheets OAuth2 account Select the spreadsheet for high-risk KYC review Select the correct sheet tab Map the sheet columns to the workflow output Recommended columns based on the workflow riskCategory income geography redFlags This sheet acts as your high-risk review register. Step 11 — Send Slack Alert for High-Risk Cases Node: Send High Risk Alert This Slack node sends a real-time alert when a high-risk applicant is detected. What to do Connect your Slack account Select the Slack channel where alerts should be sent Slack alert content in the workflow The alert includes: Income Geography Occupation Risk Category Red Flags Why this is useful This helps compliance or operations teams react quickly instead of waiting to review a spreadsheet later. Step 12 — Save Low and Medium Risk Cases Node: Save Normal Risk Record This Google Sheets node stores non-high-risk records. What to do Connect your Google Sheets OAuth2 account Select the spreadsheet for lower-risk KYC records Select the correct sheet tab Recommended columns based on workflow output riskCategory redFlags income geography occupation This gives you a separate audit trail for lower-risk applicants. Step 13 — Test the Workflow Before Going Live Before activating the workflow, run a few test submissions through the form. Recommended test scenarios Test 1 — Low Risk Example Example: Income: 5000 Geography: India Occupation: Software Engineer Expected result: record is saved to normal-risk sheet no Slack alert is sent Test 2 — Medium Risk Example Example: Income: 3000 Geography: Some higher-risk region Occupation: Freelancer Expected result: record is saved to normal-risk sheet no Slack alert is sent Test 3 — High Risk Example Example: Income: 15000 Geography: High-risk jurisdiction Occupation: Crypto Trader Expected result: record is saved to high-risk sheet Slack alert is sent Test 4 — Invalid Submission Example: Income: 0 Geography: blank Occupation: blank Expected result: validation fields should show issues AI may still run in the current workflow unless you add a blocking validation step Once these tests work correctly, the workflow is ready to activate. How To Customize Nodes This workflow is flexible and can be extended depending on your onboarding or compliance process. 1) Add More KYC Form Fields Node: KYC Form Submission You can expand the form to include fields such as: full name email date of birth nationality source of funds business type customer type politically exposed person status If you add fields, remember to update: validation logic AI prompt output formatting Google Sheets mappings 2) Make Validation Strict Node: Validate KYC Input Right now, validation results are created but not used to stop the workflow. You can improve this by adding an IF node after validation to block records where: isValid == false This would let you: stop bad form submissions return cleaner downstream results prevent unnecessary AI usage 3) Customize Risk Rules Node: Ai Assign The Risk Category You can tailor the AI prompt to match your internal compliance rules. Example customizations define your own high-risk geographies add more restricted occupations introduce onboarding thresholds include business-specific risk rules add customer segment rules This is one of the most important places to adapt the workflow to your business. 4) Save More AI Output Nodes: Process AI Response Format Final Data You can extend the structured AI output to include more details such as: risk score explanation summary review recommendation escalation notes due diligence level This is useful if you want more than just a category label. 5) Improve the Slack Alert Node: Send High Risk Alert You can customize the Slack message to include: internal case ID submission timestamp review owner priority level direct review link This helps operational teams respond faster. 6) Unify All Records into One Master Sheet Nodes: Save High Risk Record Save Normal Risk Record Instead of storing high-risk and normal-risk cases separately, you can route both into one master Google Sheet and add a field such as: riskCategory This is useful if you prefer centralized reporting. Add-ons This workflow can be extended into a more advanced KYC onboarding automation system. 1) Email Notification for High-Risk Cases Send an email alert in addition to Slack when a high-risk record is detected. 2) Automatic Case Assignment Assign high-risk cases to a specific compliance reviewer. 3) Manual Review Queue Create a review status field such as: Pending Review Under Investigation Approved Escalated 4) Risk Scoring Dashboard Track the volume of: low-risk submissions medium-risk submissions high-risk submissions common red flags 5) CRM or Database Integration Push the final KYC decision into your CRM, database or onboarding system. 6) Enhanced Sanctions / Jurisdiction Screening Add a dedicated compliance screening step before or after AI analysis. Use Case Examples This workflow can support many different customer onboarding and risk intake scenarios. Below are some of the main ones. 1) Basic KYC Intake Screening Use the form to collect customer information and automatically classify onboarding risk before manual review. 2) Fintech Customer Onboarding Route higher-risk financial users into a flagged review process while logging normal applicants automatically. 3) Internal Risk Triage for Sales or Partnerships Screen leads, vendors or applicants before moving them deeper into your onboarding pipeline. 4) Compliance Team Pre-Screening Help compliance teams reduce repetitive manual work by automatically identifying obviously high-risk submissions. 5) Operational Review Queue Creation Automatically create a structured list of high-risk cases that require follow-up or escalation. 6) Lightweight AI-Assisted KYC Automation Use AI as an early-stage risk assistant before implementing a more advanced compliance stack. There can be many more use cases depending on your industry, customer type, onboarding rules and compliance process. Troubleshooting Guide | Issue | Possible Cause | Solution | |---|---|---| | Form submits but no data is processed | Workflow is inactive or form trigger is not published | Activate the workflow and confirm the form is live | | AI output fails to parse | OpenAI returned text outside the expected JSON structure | Review the AI prompt and confirm the model is returning valid JSON | | High-risk records are not reaching Slack | Slack node is not configured or connected correctly | Reconnect Slack credentials and verify the selected channel | | High-risk records are not being saved | Google Sheets high-risk node is not configured | Set the spreadsheet and sheet tab in Save High Risk Record | | Low / medium records are not being saved | Google Sheets normal-risk node is incomplete | Configure Save Normal Risk Record with the correct destination sheet | | Risk category is always Unknown | AI output could not be parsed properly | Inspect the Process AI Response node and test the AI response format | | Invalid form data still moves forward | Validation node does not block the workflow | Add an IF node after Validate KYC Input if you want strict validation | | Geography looks different than expected | Validation node converts geography to uppercase | This is expected behavior in the current workflow | | Occupation is missing in the high-risk sheet | Format the Data For Sheet does not include occupation | Add occupation to that Set node if needed | | Slack alert shows incomplete values | Some fields were not preserved in the high-risk path | Check the data passed into the Slack node and update the mapping if necessary | Need Help? If you want help setting up, customizing or extending this workflow, our n8n workflow automation developers at WeblineIndia / Global can help you implement it faster and more reliably. We can help you with: n8n workflow setup and deployment AI-powered KYC intake automation Google Sheets and Slack integrations Compliance workflow extensions Form optimization and validation logic Onboarding automation Risk scoring enhancements Custom review and escalation pipelines If you want a customized version of this workflow or need similar automation for onboarding, compliance or risk operations, contact WeblineIndia for workflow setup and development support.