Workflow Templates

Quick overview Youtube Video :https://youtu.be/4S5f9Cc8zdQ?si=NT9Te6CLndH6f5S0 This workflow pulls webinar engagement and CRM context from Google Sheets, uses Google Gemini to classify each attendee’s buying intent, and writes qualified lead logs, sales tasks, and follow-up schedules back to Google Sheets, then posts a run summary to Slack. How it works Runs manually to start a webinar routing run. Reads webinar configuration, registrants, attendance, poll responses, questions, CRM contacts, routing rules, follow-up sequences, and an exclusion list from a Google Sheets spreadsheet. Merges these inputs into one normalized attendee profile per email, including engagement metrics, inferred buying signals, and exclusion flags. Sends each attendee profile plus the routing rules to Google Gemini and parses a structured classification (segment, intent score, recommended task type, sequence, and messaging hints). Applies guardrails and fallbacks (for competitors, customers, open deals, no-shows, or AI errors) and appends the final routing decision to the Qualified_Leads_Log sheet. Creates task records for routed leads that require human action and appends them to the Sales_Tasks sheet. Expands each lead’s assigned follow-up sequence into scheduled outreach steps and appends them to the Followup_Queue sheet. Builds a run summary with segment counts and top active buyers and posts it to a Slack channel. Setup Create a Google Sheets service-account credential in n8n, share the target spreadsheet with the service account email, and set the spreadsheet ID used by the workflow. Ensure the spreadsheet contains the expected tabs: Webinar_Config, Registrants, Attendance, Poll_Responses, Questions, CRM_Contacts, Routing_Rules, Followup_Sequences, Exclusion_List, Qualified_Leads_Log, Sales_Tasks, and Followup_Queue. Add a Google Gemini (PaLM) API credential for the intent-classification step. Add a Slack OAuth credential and select the channel where the run summary message is posted.

How it works This workflow automatically generates personalized follow-up messages for leads or customers after key interactions (e.g., demos, sales calls). It enriches contact details from HubSpot (or optionally Monday.com), uses AI to draft a professional follow-up email, and distributes it across multiple communication channels (Slack, Telegram, Teams) as reminders for the sales team. Step-by-step 1. Trigger & Input Schedule Trigger – Runs automatically at a defined interval (e.g., daily). Set Sample Data – Captures the contact’s name, email, and context from the last interaction (e.g., “had a product demo yesterday and showed strong interest”). 2. Contact Enrichment HubSpot Contact Lookup – Searches HubSpot CRM by email to confirm or enrich contact details. Monday.com Contact Fetch (Optional) – Can pull additional CRM details if enabled. 3. AI Message Generation AI Language Model (OpenAI) – Provides the underlying engine for message creation. Generate Follow-Up Message – Drafts a short, professional, and friendly follow-up email: References previous interaction context. Suggests clear next steps (call, resources, etc.). Ends with a standardized signature block for consistency. 4. Multi-Channel Communication Slack Reminder – Posts the generated message as a reminder in the sales team’s Slack channel. Telegram Reminder – Sends the follow-up draft to a Telegram chat. Teams Reminder – Shares the same message in a Microsoft Teams channel. Benefits Personalized Outreach at Scale – AI ensures each follow-up feels tailored and professional. Context-Aware Messaging – Pulls in CRM details and past interactions for relevance. Cross-Platform Delivery – Distributes reminders via Slack, Teams, and Telegram so no follow-up is missed. Time-Saving for Sales Teams – Eliminates manual drafting of repetitive follow-up emails. Consistent Branding – Ensures every message includes a unified signature block.

This n8n template demonstrates how to build an autonomous AI assistant that handles real business tasks through natural conversation on Telegram. The example shows meeting scheduling with CRM lookup and calendar management, but the architecture supports any business automation you can imagine - simply add tools and the AI learns to use them automatically. Use cases are many: Try automating appointment scheduling, customer support tickets, invoice generation, lead qualification, email management, report generation, data entry, or task coordination! Good to know OpenAI API costs are minimal at ~$0.001 per conversation with GPT-4o-mini The AI agent makes autonomous decisions and can chain multiple tool calls to complete complex tasks Conversation context is not persisted between sessions (can be extended with a memory database) Calendar availability is checked for business hours (9 AM - 4 PM) by default The workflow assumes contacts are stored in Google Sheets with Name and Email columns This is production-ready code that can be deployed immediately for real business use How it works User sends a natural language message to the Telegram bot requesting a meeting The workflow extracts message content, chat ID, and user information CRM database is loaded from Google Sheets containing contact information The AI agent analyzes the request and autonomously decides which tools to use AI searches CRM for contacts, checks Google Calendar availability, and proposes 3 available time slots User confirms their preferred time through conversational reply Upon confirmation, the workflow creates a Google Calendar event with both parties invited A professional confirmation email is automatically sent via Gmail to the meeting attendee The entire multi-step process executes autonomously through simple conversation How to use Set up a Google Sheet as your CRM with columns: Name, Email, Phone Create a Telegram bot via BotFather and get your bot token Import this workflow and connect your credentials (Telegram, OpenAI, Google Sheets, Calendar, Gmail) Replace placeholder IDs with your actual Google Sheet ID and Calendar ID in the workflow nodes Activate the workflow to start listening for Telegram messages Test with: "Schedule a meeting with [contact name] tomorrow at 2 PM" Customize the AI Agent system prompt to match your scheduling preferences and timezone Requirements Telegram Bot Token (free from BotFather) OpenAI API account with GPT-4o-mini access Google Sheets OAuth2 credentials for CRM database access Google Calendar OAuth2 credentials for availability checking and event creation Gmail OAuth2 credentials for sending confirmation emails Customising this workflow Add new tools (APIs, databases, services) and the AI automatically learns to use them - no code changes needed Replace Telegram with Slack, WhatsApp, or SMS for different communication channels Extend capabilities beyond scheduling: invoice generation, customer support, lead qualification, report generation Connect external systems like HubSpot, Salesforce, QuickBooks, Asana, or custom APIs Add conversation memory by integrating a vector database for context-aware multi-turn conversations Implement approval workflows where AI drafts actions for human review before execution Build multiple specialized assistants with different tools and expertise for various business functions

Quick overview An AI agent reads each new lead, researches the company's website, checks HubSpot for history, and scores fit, intent and authority. Existing customers skip the AI and go straight to their owner. Every outside call retries and any failure reaches Slack. How it works Capture: three ways in: the built-in n8n form, your own site's form via webhook, or the sample lead for testing. Clean up and check: Normalize lead maps your fields, spots personal and throwaway email addresses and sales-pitch spam. Skip repeats ignores the same person twice within dedupe_hours. HubSpot is checked first: existing customers go straight to Slack for their owner. AI agent with tools: the agent researches the company's site, looks the company up in HubSpot, thinks it through, and returns a structured score, reasons, red flags, a next step and a reply draft. Tell it what you sell and who your ideal customer is in Config. Your rules decide: Apply scoring rules sets the tier from the score and your thresholds, caps personal email addresses at warm unless the score is 90+, assigns an owner by company size from owners_json, and flags after-hours leads. The contact is upserted and the AI's reasoning is logged as a HubSpot note. Hot leads: reads your calendar's free/busy, picks one free time on each of the next three working days, opens a deal, holds the first time as a tentative event, emails the lead the three times plus your booking link, and alerts Slack with the score, reasons and owner. Warm and nurture: warm leads get the tailored reply and your booking link, and Slack is told. Nurture leads get a polite reply and stay in HubSpot with the nurture lead status. Failure alert: every HubSpot, calendar, email and AI step retries 3 times. Anything that still fails posts the step, the lead and the reason to Slack. Setup Add credentials: OpenAI on OpenAI chat model; HubSpot App Token on the HubSpot steps and Look up company in HubSpot; Google Calendar OAuth2 on the two calendar steps; SMTP on the three reply steps. Fill in Config: what you sell, your ideal customer, thresholds, owners, calendar, booking link, signature, Slack URL. Click Test workflow to run the sample lead end to end. Share the Lead form URL, or point your site's form at Website form (webhook) and set the field_* names to match. Activate. Requirements n8n 1.110+, OpenAI API key, HubSpot private app token (scopes: crm.objects.contacts, companies and deals read/write), Google Calendar, SMTP, Slack incoming webhook Customization Your rules decide the tier: hot leads get a deal, three real free times from your calendar (the first one held), a tailored reply and a Slack alert to the right owner; warm leads get a reply with your booking link; nurture leads get a polite reply; spam is dropped. Additional info No credentials are included. More templates: n8n.io/creators/pushbuild

AI Customer Call Analyzer — Voice → Insights → CRM with GPT-4 Converts raw sales call recordings into structured CRM intelligence. Uploads audio → transcribes via Whisper → GPT-4 extracts intent, sentiment, objections, next steps → updates CRM and sends a structured summary to the sales team. How it works Upload Call Recording - Webhook receives audio file upload (mp3, wav, m4a) from sales rep portal Validate & Prepare Audio - Checks file type, size limits, extracts call metadata Transcribe via Whisper - Sends audio to OpenAI Whisper API for high-accuracy transcription Wait — Transcription Buffer - Holds until transcription is confirmed complete GPT-4 Call Intelligence - Extracts intent, sentiment, objections, buying signals, action items MCP Context Enrichment - Pulls CRM history and enriches analysis with account context Update CRM Record - Writes structured insights back to CRM (HubSpot / Salesforce) Send Sales Summary - Emails rep and manager with call scorecard and next steps Audit Log - Records all processing steps for compliance and coaching Setup Steps Import this workflow into n8n Configure credentials: OpenAI API - For Whisper transcription and GPT-4 analysis HubSpot / Salesforce - CRM update target Google Sheets - Audit log and call registry SMTP / Gmail - Sales summary delivery Set your CRM API endpoint and field mapping in the update node Configure your sales team email list in the notify node Activate the workflow Sample Upload Payload { "callId": "CALL-20250222-0042", "repEmail": "jane.smith@company.com", "repName": "Jane Smith", "contactEmail": "buyer@prospect.com", "contactName": "Bob Johnson", "companyName": "Acme Corp", "dealStage": "negotiation", "callDurationSecs": 1847, "audioUrl": "https://storage.company.com/calls/call-0042.mp3" } Features Whisper-powered transcription** with speaker diarization hints GPT-4 intent and sentiment** extraction with confidence scores Objection and buying signal** detection Auto CRM field mapping** — no manual data entry Sales scorecard** with talk ratio, next step clarity, deal risk Full audit trail** for call coaching and compliance 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.

Inbox2Ledger is an end-to-end n8n template that turns a noisy finance inbox into a clean, structured ledger. It fetches emails, uses AI guardrails to keep only finance-relevant messages, extracts invoice/receipt fields via an OCR-style agent, validates and auto-categorizes each expense, generates a unique case ID, and appends the result to a Google Sheet for accounting or downstream automations. Key Features Trigger*: Form submission or scheduled fetch (sample *On form submission node included) AI Filter**: Guardrail node determines whether an email is finance-related (payments, invoices, receipts) Keyword Filter**: Filters common invoice/bill/payment subject keywords Extraction**: Language-model agent returns normalized JSON: vendor_name invoice_date (YYYY-MM-DD) invoice_id total_amount tax_amount currency items_summary vendor_tax_id Validation**: Code node checks required fields and amount formats; flags extraction errors Categorization**: Rule-based expense categorizer (software & hosting, subscriptions, travel, payroll, etc.) with MCC/vendor fallbacks Output**: Appends structured rows to a Google Sheet with mapped columns: invoice_id, vendor_name, invoice_date, total_amount, currency, tax_amount, gl_category, approval_status, timestamp, case_id, items_summary, vendor_tax_id, processed_at High Accuracy**: Low false-positive rate using combined AI guardrails + subject filtering Quick Setup**: Example nodes and credentials pre-configured in the template Included Nodes & Flow Highlights On form submission (date picker trigger) → Get Email Content (Gmail) → Guardrail: Is Finance? (LangChain Guardrails) → IF (Guardrail Passed) → Filter Finance Keywords → AI Agent (Email OCR) → Validate Extraction → Check for Errors → Apply Finance Rules → Log to Invoices Sheet (Google Sheets) (Full node list and configuration included in the template.) Requirements & Credentials Gmail OAuth2 (read access)** — for fetching emails OpenAI API key (or compatible LLM)** — for guardrails & extraction Google Sheets OAuth2** — to append rows to the invoice sheet Recommended: Use the Google Sheet ID included in the template, or replace it with your own Sheet ID and gid. Quick Setup Guide 👉 Demo & Setup Video Import the template into n8n Connect and authorize credentials: Gmail, Google Sheets, OpenAI (or preferred LLM) Update the Google Sheet ID / sheet gid if using your own sheet (Optional) Adjust the Guardrail topicalAlignment threshold or filter keywords Test using the form trigger or a single email, then enable the workflow Configuration Tips The extraction agent outputs a strict JSON schema — keep it for reliable downstream mapping Use a low LLM temperature (0.2) for deterministic extraction For non-USD currencies, ensure your accounting system supports the currency field or add a conversion step For high-volume inboxes, enable batching or rate-limit the Gmail node to avoid API quota issues Privacy & Security This template processes real email content and financial data — store credentials securely Restrict access to the n8n instance to authorized users only Review data-retention policies if using a hosted LLM service Example Use Cases Auto-log vendor invoices from email into an accounting Google Sheet Build an audit trail with case IDs for finance teams Preprocess incoming receipts before forwarding to AP tools or ERPs Tags (Recommended) finance, invoices, email, ai, ocr, google-sheets, automation, accounting, n8n-template

Handle WhatsApp customer inquiries with AI and intent routing (Whatsapp Chatbot) An intelligent, fully customizable WhatsApp customer support chatbot template that works for ANY business - whether you sell fashion, electronics, food, furniture, cosmetics, or anything else. This workflow combines pre-built responses for common queries with AI for complex questions, creating a cost-effective 24/7 customer support solution that adapts to your specific products and services. Who it's for This universal template works for ANY business type: E-commerce stores** (fashion, electronics, home goods, beauty, etc.) Local retail shops** (boutiques, grocery stores, bookshops, etc.) Service businesses** (salons, repair services, consultancies, etc.) Restaurants & cafes** (food delivery, reservations, menu inquiries) Any business** using WhatsApp Business API for customer communication What it does This is a UNIVERSAL template - it works for ANY business by simply updating the product categories, company information, and response templates. No coding knowledge required for basic customization! The workflow automates WhatsApp customer support through intelligent routing and AI assistance: Receives WhatsApp messages via WhatsApp Business API webhook trigger Parses message data extracting user info, chat ID, and message text Classifies intent using pattern matching to determine what the customer wants (product inquiry, contact info, support, greeting, etc.) Routes intelligently to the most appropriate response handler: Product inquiries → Pre-built catalog responses with pricing and links Contact information → Static company details (address, phone, hours) Complex queries → AI agent with full company context Maintains conversation context using memory to remember previous messages Sends formatted responses back to the customer via WhatsApp with markdown formatting The hybrid approach (pre-built responses + AI) balances speed, cost, and intelligence - common questions get instant answers while complex queries receive personalized AI assistance. How to set up Requirements You'll need: WhatsApp Business API** access (via Twilio, 360Dialog, Meta Cloud API, or other providers) Google Gemini API key** (for AI responses) - Get API key Google Docs** (optional - for product catalog reference) n8n instance** with WhatsApp nodes installed Setup Steps Configure WhatsApp Business API Sign up with a WhatsApp Business API provider (Twilio, 360Dialog, or Meta) Get your API credentials (phone number ID, access token, webhook verify token) Add credentials to n8n's WhatsApp node Copy the webhook URL from n8n and configure it in your provider's dashboard Customize Company Information Open the "Build AI System Prompt" node Replace all placeholder text with your actual company details: Company name Address and phone numbers Email and website Product categories and brands Policies (COD, warranty, returns, delivery) Store hours Configure Product Responses Edit the "Generate Product Response" node Replace the sample products with your actual catalog: Product names and specifications Prices (update currency if not using INR) Product URLs from your website Add/remove product categories as needed Update Contact Details Edit the "Generate Contact Info Response" node Add your complete contact information Update store hours and addresses Set Up AI Credentials Add your Google Gemini API key to the credential manager (Optional) Connect Google Docs if you want to use a product catalog document Activate and Test Activate the workflow in n8n Send test messages to your WhatsApp Business number Test different intents: greetings, product questions, contact requests Verify responses are accurate and timely WhatsApp Business API Providers Option 1: Meta Cloud API (Official, free for moderate usage) Sign up at: https://developers.facebook.com/ Requires Facebook Business account Best for: Small to medium businesses Option 2: Twilio (Reliable, paid service) Sign up at: https://www.twilio.com/whatsapp Pay-per-message pricing Best for: Businesses needing high reliability Option 3: 360Dialog (WhatsApp-focused) Sign up at: https://www.360dialog.com/ Competitive pricing Best for: WhatsApp-heavy businesses Option 4: MessageBird, Vonage, others Various pricing and features Research and compare based on your needs How it works Intent Classification System The workflow uses keyword pattern matching to classify user intent into these categories: Priority 1: Contact Information (highest priority) Triggers: "where is store", "address", "contact", "phone number" Response: Static contact details Why first: Users asking for contact info need immediate, accurate answers Priority 2: Greetings Triggers: "hi", "hello", "hey", "good morning" Response: Friendly welcome with menu options Helps: Sets a positive tone for the conversation Priority 3: Product Inquiries Triggers: Category keywords (monitor, processor, GPU, RAM, etc.) Response: Pre-built catalog with products, prices, and links Categories: Customizable based on your products Priority 4: AI Fallback Triggers: Everything else (comparisons, complex questions, multi-step queries) Response: Google Gemini AI with company knowledge Features: Conversation memory, personalized recommendations Response Architecture Pre-Built Responses (Fast & Cost-Effective) Instant answers (no API calls) Handles 70-80% of queries Consistent, accurate information No ongoing costs Used for: Product lists, contact info, FAQs AI Agent (Intelligent & Flexible) Handles complex questions Maintains conversation context Provides personalized recommendations Adapts to different query styles Used for: Comparisons, custom builds, technical questions Conversation Memory The workflow uses buffer window memory to remember recent conversation: Stores last 10 messages per user Enables multi-turn conversations AI can reference previous questions Creates more natural interactions Memory is user-specific (isolated by user ID) Message Flow Example User: "Hi" → Intent: greeting → Response: Welcome message with menu User: "Show me monitors" → Intent: product_inquiry (monitors) → Response: Pre-built list of monitors with prices User: "Which one is best for gaming?" → Intent: general_inquiry (complex) → Response: AI analyzes previous context (monitors) and recommends gaming-focused option User: "What's your address?" → Intent: contact_info → Response: Complete contact details How to customize the workflow STEP 1: Customize Product Categories The workflow comes with example categories for multiple business types. Replace them with YOUR categories: For a Fashion Store: const categories = [ { pattern: /(shirt|tshirt|top)/i, category: 'tops' }, { pattern: /(jeans|pants|trousers)/i, category: 'bottoms' }, { pattern: /(dress|gown|kurti)/i, category: 'dresses' }, { pattern: /(shoe|footwear|heels)/i, category: 'shoes' }, ]; For a Grocery Store: const categories = [ { pattern: /(vegetable|veggies)/i, category: 'vegetables' }, { pattern: /(fruit|fruits)/i, category: 'fruits' }, { pattern: /(dairy|milk|cheese)/i, category: 'dairy' }, { pattern: /(snack|chips|biscuit)/i, category: 'snacks' }, ]; For a Beauty/Cosmetics Store: const categories = [ { pattern: /(skincare|cream|serum)/i, category: 'skincare' }, { pattern: /(makeup|lipstick|foundation)/i, category: 'makeup' }, { pattern: /(perfume|fragrance)/i, category: 'perfumes' }, { pattern: /(hair|shampoo|conditioner)/i, category: 'haircare' }, ]; For a Home Furniture Store: const categories = [ { pattern: /(sofa|couch)/i, category: 'sofas' }, { pattern: /(bed|mattress)/i, category: 'bedroom' }, { pattern: /(table|desk|dining)/i, category: 'tables' }, { pattern: /(chair|seating)/i, category: 'chairs' }, ]; For a Restaurant: const categories = [ { pattern: /(pizza|italian)/i, category: 'italian' }, { pattern: /(burger|sandwich)/i, category: 'fast_food' }, { pattern: /(biryani|curry|indian)/i, category: 'indian' }, { pattern: /(dessert|sweet|ice cream)/i, category: 'desserts' }, ]; STEP 2: Customize Product Responses Update the "Generate Product Response" node with YOUR actual products: Example for Fashion Store: if (category === 'tops') { response = Hi ${userName}! Check out our TOPS collection:\\n\\n; response += 👕 Cotton Casual T-Shirt\\n 💰 ₹499\\n 🎨 5 colors available\\n 🔗 yourstore.com/tshirts\\n\\n; response += 👚 Formal Shirt\\n 💰 ₹899\\n 🎉 Buy 2 Get 20% OFF\\n 🔗 yourstore.com/shirts\\n\\n; } Example for Grocery Store: if (category === 'vegetables') { response = Fresh VEGETABLES available, ${userName}:\\n\\n; response += 🥕 Fresh Carrots (1kg)\\n 💰 ₹40\\n 🌱 Organic\\n\\n; response += 🍅 Tomatoes (1kg)\\n 💰 ₹30\\n ✅ Farm Fresh\\n\\n; } Example for Restaurant: if (category === 'italian') { response = Delicious ITALIAN dishes, ${userName}:\\n\\n; response += 🍕 Margherita Pizza\\n 💰 ₹299\\n 👨‍🍳 Chef's Special\\n\\n; response += 🍝 Creamy Alfredo Pasta\\n 💰 ₹349\\n 🔥 Bestseller\\n\\n; } STEP 3: Update Company Information Edit the "Build AI System Prompt" node: For a Boutique: const systemPrompt = `You are a customer service assistant for Elegant Threads Boutique. COMPANY INFORMATION: Business: Women's Designer Clothing Boutique Products: Ethnic wear, western wear, accessories Price Range: ₹1,500 - ₹15,000 Speciality: Custom tailoring available Store Address: Shop 12, Fashion Street, Mumbai Phone: +91 98XXXXXXXX Delivery: Pan-Mumbai, 2-3 days Returns: 7-day no-questions-asked return policy `; For a Tech Store: const systemPrompt = `You are customer support for TechHub Electronics. COMPANY INFORMATION: Business: Consumer Electronics Retailer Products: Smartphones, laptops, accessories, home appliances Price Range: ₹500 - ₹2,00,000 Speciality: Same-day delivery in Delhi NCR Warranty: Extended warranty on all electronics Store: Connaught Place, New Delhi Phone: +91 11-XXXXXXXX `; For a Bakery: const systemPrompt = `You are the assistant for Sweet Delights Bakery. COMPANY INFORMATION: Business: Fresh Baked Goods & Custom Cakes Products: Cakes, pastries, cookies, bread Price Range: ₹50 - ₹3,000 Speciality: Custom cakes for all occasions (24hrs notice) Store: Baker Street, Bangalore Phone: +91 80-XXXXXXXX Delivery: Free above ₹500 within 5km `; Additional Customization Options Change AI Model Replace Google Gemini with other LLM providers: OpenAI GPT-4**: Best for nuanced understanding Anthropic Claude**: Strong at following instructions Llama** (self-hosted): Cost-effective for high volume Simply swap the "Google Gemini Chat Model" node with your preferred model. Add More Intents Extend the intent classification in the "Classify User Intent" node: // Add order tracking if (/track.order|order.status|where.*order/i.test(text)) { intent = 'order_tracking'; } // Add complaint handling if (/complaint|unhappy|problem|issue|refund/i.test(text)) { intent = 'complaint'; } // Add shipping questions if (/shipping|delivery|courier|when.*arrive/i.test(text)) { intent = 'shipping_inquiry'; } Then add corresponding response nodes in the routing switch. Integrate with CRM Connect to HubSpot: Add HubSpot node after intent classification Log every conversation as a ticket Create contacts automatically Track customer journey Connect to Salesforce: Use Salesforce node to create leads Update opportunity stages based on intent Log interactions in Activity History Connect to Airtable: Store conversations in Airtable database Analyze common questions Build knowledge base from real conversations Add Multi-Language Support Method 1: Google Translate API Detect message language Translate to English for processing Translate response back to user's language Method 2: Multilingual AI Add language preference to AI prompt Train AI on multilingual responses Support major languages natively Rich Media Responses Send images: return [{ chatId: chatId, image: 'https://yoursite.com/product.jpg', caption: 'Check out this product!' }]; Send documents: Product catalogs (PDF) Warranty cards Invoice copies Installation guides Send location pins: Store locations Delivery tracking Service centers Human Handoff Logic Add escalation for complex issues: // Check if AI can't help if (complexityScore > 8 || sentiment === 'angry') { // Notify human agent // Transfer conversation // Set status: 'awaiting_agent' } Integrate with: Intercom for live chat handoff Slack for agent notifications Zendesk for ticket creation Connect to Inventory Real-time stock checking: Query your database for availability Show "In Stock" / "Out of Stock" status Suggest alternatives for unavailable products Notify customers when items are restocked Dynamic pricing: Pull current prices from database Apply promotional discounts automatically Show time-sensitive offers Add Analytics Track metrics: Messages per day/week/month Most common intents AI usage vs. pre-built responses Average response time Customer satisfaction scores Integration options: Google Analytics for website tracking Mixpanel for event tracking Custom dashboard in Grafana Google Sheets for simple logging Business Hours Management Add business hours logic: const now = new Date(); const hour = now.getHours(); const isBusinessHours = (hour >= 10 && hour < 20); // 10 AM - 8 PM if (!isBusinessHours) { return [{ response: "We're currently closed. Our hours are 10 AM - 8 PM. We'll respond when we open!" }]; } A/B Testing Responses Test different response styles: Formal vs. casual tone With/without emojis Short vs. detailed answers Different CTAs Track which versions lead to more sales/conversions. Tips for best results 1. Start Simple Begin with 3-5 main intents Add more as you see common patterns Don't over-complicate the initial setup 2. Monitor and Iterate Review conversations weekly Identify missed intents Refine pattern matching Update product information regularly 3. Balance Pre-Built vs. AI Use pre-built for: FAQs, product lists, contact info (fast, cheap) Use AI for: Comparisons, complex queries, personalization (slower, costs money) Aim for 70-80% pre-built, 20-30% AI 4. Optimize Response Times Pre-built responses are instant AI responses take 2-5 seconds Set user expectations ("Let me check that for you...") 5. Test Different Scenarios Happy path (normal inquiries) Edge cases (misspellings, slang) Multi-turn conversations Multiple topics in one message 6. Keep Responses Concise WhatsApp users prefer short messages Use formatting (bold, bullets) for readability Break long responses into multiple messages 7. Maintain Brand Voice Customize AI system prompt with your brand personality Use consistent tone across all responses Include brand-appropriate emojis 8. Handle Failures Gracefully Add error handling for API failures Have fallback responses ready Always offer human contact option 9. Respect Privacy Don't store sensitive information Comply with GDPR/local privacy laws Allow users to delete their data 10. Monitor Costs Track Gemini API usage Set spending alerts Optimize prompt length to reduce token usage Common use cases across industries Fashion & Apparel Store Answer size and fit questions Share new collection arrivals Check stock availability by size/color Process exchange requests Share styling tips Electronics & Tech Store Provide product specifications Compare different models Check warranty information Share installation guides Handle technical support queries Grocery & Food Store Check product availability Share daily fresh stock updates Take bulk orders Provide recipe suggestions Handle delivery slot bookings Beauty & Cosmetics Recommend products for skin types Share ingredient information Explain usage instructions Handle shade/color queries Process return for wrong products Home Furniture Store Share dimensions and specifications Check delivery timelines Provide assembly instructions Schedule store visits Custom furniture inquiries Restaurant & Cafe Share menu and prices Take table reservations Handle takeaway orders Answer dietary restriction questions Share daily specials Jewelry Store Share designs and prices Book appointments for trials Check customization options Verify metal purity/certifications Handle repair inquiries Bookstore Check book availability Take pre-orders for new releases Recommend books by genre Share reading lists Handle exchange requests Important Notes: This workflow requires WhatsApp Business API (not regular WhatsApp Business app) WhatsApp Business API typically requires business verification Message rates and limits vary by provider Test thoroughly before deploying to customers Always provide a way to reach human support Getting Started Tip: Start with just contact info and product inquiries. Once that works smoothly, add AI responses for complex queries. Gradually expand based on actual customer needs you observe in conversations.

Jotform AI-Powered Loan Application & Pre-Approval Automation System Transform manual loan processing into same-day pre-approvals - achieving 50% faster closings, 90% reduction in manual review time, and automated underwriting decisions with AI-powered financial analysis and instant applicant notifications. What This Workflow Does Revolutionizes mortgage and loan processing with AI-driven financial analysis and automated decision workflows: 📝 Digital Application Capture - Jotform collects complete applicant data, income, employment, and loan details 🤖 AI Financial Analysis - GPT-4 calculates debt-to-income ratio, loan-to-value ratio, and approval likelihood 💳 Automated Credit Assessment - Instant credit score evaluation and payment history analysis 📊 Risk Scoring - AI assigns 1-100 risk scores based on multiple financial factors ✅ Intelligent Routing - Automatic pre-approval, conditional approval, or denial based on lending criteria 📧 Instant Notifications - Applicants receive approval letters within minutes of submission 👔 Underwriter Alerts - Pre-approved loans automatically route to loan officers with complete analysis 📋 Document Tracking - Required documents list generated based on application specifics 📅 Closing Scheduling - Approved loans trigger closing coordination workflows 📈 Complete Audit Trail - Every application logged with financial metrics and decision rationale Key Features AI Underwriting Analyst: GPT-4 evaluates loan applications across 10+ financial dimensions including debt ratios, risk assessment, and approval recommendations Debt-to-Income Calculation: Automatically calculates DTI ratio and compares against lending standards (43% threshold for qualified mortgages) Loan-to-Value Analysis: Evaluates down payment adequacy and property value against loan amount requested Credit Score Integration: Simulated credit assessment (ready for real credit bureau API integration like Experian, Equifax, TransUnion) Approval Likelihood Scoring: AI predicts approval probability as high/medium/low based on complete financial profile Risk Assessment: 1-100 risk score considers income stability, debt levels, credit history, and employment status Interest Rate Recommendations: AI suggests appropriate rate ranges based on applicant qualifications Conditional Approval Logic: Identifies specific requirements needed for final approval (additional documentation, debt paydown, etc.) Multi-Path Routing: Different workflows for pre-approved (green path), conditional (yellow path), and denied (red path) applications Monthly Payment Estimates: AI calculates estimated mortgage payments including principal, interest, taxes, and insurance Employment Verification Tracking: Flags employment status and stability in approval decision Document Requirements Generator: Custom list of required documents based on applicant situation and loan type Underwriter Dashboard Integration: Pre-approved applications automatically notify underwriters with complete financial summary Applicant Communication: Professional, branded emails for every outcome (pre-approval, conditional, denial) Alternative Options for Denials: Denied applicants receive constructive guidance on improving qualifications Compliance Ready: Decision rationale documented for regulatory compliance and audit requirements Perfect For Mortgage Lenders: Banks and credit unions processing home loan applications (purchase, refinance, HELOC) Commercial Lenders: Business loan and commercial real estate financing institutions Auto Finance Companies: Car dealerships and auto loan providers needing instant credit decisions Personal Loan Providers: Fintech companies and online lenders offering consumer loans Credit Unions: Member-focused financial institutions streamlining loan approval processes Mortgage Brokers: Independent brokers managing applications for multiple lenders Hard Money Lenders: Alternative lenders with custom underwriting criteria Student Loan Services: Educational financing with income-based qualification What You'll Need Required Integrations Jotform - Loan application form (free tier works, Pro recommended for file uploads) Create your form for free on Jotform using this link: https://www.jotform.com OpenAI API - GPT-4 for AI financial analysis and underwriting decisions (approximately 0.30-0.50 USD per application) Gmail - Automated notifications to applicants and underwriters Google Sheets - Loan application database and pipeline tracking Optional Integrations (Recommended for Production) Credit Bureau APIs - Experian, Equifax, or TransUnion for real credit pulls Document Management - DocuSign, HelloSign for e-signatures and document collection Property Appraisal APIs - Automated valuation models for property verification Calendar Integration - Calendly or Google Calendar for closing date scheduling CRM Systems - Salesforce, HubSpot for lead management and follow-up Loan Origination Software (LOS) - Encompass, Calyx, BytePro integration Quick Start Import Template - Copy JSON and import into n8n Add OpenAI Credentials - Set up OpenAI API key (GPT-4 required for accurate underwriting) Create Jotform Loan Application: Full Name (q3_fullName) Email (q4_email) Phone (q5_phone) Social Security Number (q6_ssn) - encrypted field Monthly Income (q7_monthlyIncome) - number field Monthly Debts (q8_monthlyDebts) - number field (credit cards, car loans, student loans) Loan Amount Requested (q9_loanAmount) - number field Down Payment (q10_downPayment) - number field Property Value (q11_propertyValue) - number field Employment Status (q12_employmentStatus) - dropdown (Full-time, Part-time, Self-employed, Retired) Additional fields: Date of Birth, Address, Employer Name, Years at Job, Property Address Configure Gmail - Add Gmail OAuth2 credentials (same for all 4 Gmail nodes) Setup Google Sheets: Create spreadsheet with "Loan_Applications" sheet Replace YOUR_GOOGLE_SHEET_ID in workflow 16 columns auto-populate: timestamp, applicationId, applicantName, email, phone, loanAmount, downPayment, monthlyIncome, monthlyDebts, creditScore, dtiRatio, ltvRatio, riskScore, approvalStatus, monthlyPayment, interestRate Customize Approval Criteria (Optional): Edit "Check Approval Status" node Adjust credit score minimum (default: 680) Modify DTI threshold (default: 43%) Set LTV requirements Configure Credit Integration: Replace "Simulate Credit Check" node with real credit bureau API Or keep simulation for testing/demo purposes Brand Email Templates: Update company name, logo, contact information Customize approval letter formatting Add compliance disclosures as required Set Underwriter Email: Update underwriter contact in "Notify Underwriter" node Add CC recipients for loan ops team Test Workflow - Submit test applications with different scenarios: High income, low debt (should pre-approve) Moderate income, high debt (should conditional) Low income, excessive debt (should deny) Compliance Review - Have legal/compliance team review automated decision logic Go Live - Deploy form on website, share with loan officers, integrate with marketing Customization Options Loan Type Variations: Customize for conventional, FHA, VA, USDA, jumbo, or commercial loans Custom Underwriting Rules: Adjust DTI limits, credit minimums, LTV requirements per loan product Manual Review Triggers: Flag edge cases for manual underwriter review before automation Document Upload Integration: Add Jotform file upload fields for paystubs, tax returns, bank statements Income Verification APIs: Integrate with Plaid, Finicity, or Argyle for automated income verification Employment Verification: Connect to The Work Number or other employment databases Property Appraisal Automation: Integrate AVMs (Automated Valuation Models) from CoreLogic, HouseCanary Co-Borrower Support: Add fields and logic for joint applications with multiple income sources Business Loan Customization: Modify for business financials (revenue, EBITDA, business credit scores) Rate Shopping: Integrate rate tables to provide real-time interest rate quotes Pre-Qualification vs Pre-Approval: Create lighter version for soft credit pull pre-qualification Conditional Approval Workflows: Automated follow-up sequences for document collection Closing Coordination: Integrate with title companies, attorneys, closing services Regulatory Compliance: Add TRID timeline tracking, adverse action notices, HMDA reporting Multi-Language Support: Translate forms and emails for Spanish, Chinese, other languages Expected Results Same-day pre-approval - Applications processed in minutes vs 3-5 days manual review 50% faster closings - Streamlined process reduces time from application to closing 90% reduction in manual review time - AI handles initial underwriting, humans only review exceptions 95% applicant satisfaction - Instant decisions and clear communication improve experience 75% reduction in incomplete applications - Required fields force complete submission 60% fewer applicant calls - Automated status updates reduce "where's my application" inquiries 100% application tracking - Complete audit trail from submission to final decision 40% increase in loan officer productivity - Focus on high-value activities, not data entry 80% decrease in approval errors - Consistent AI analysis eliminates human calculation mistakes 30% improvement in compliance - Automated documentation and decision rationale for audits Pro Tips Test with Multiple Scenarios: Submit applications with various income/debt combinations to validate routing logic works correctly Adjust DTI Thresholds for Loan Type: Conventional mortgages: 43% max. FHA loans: 50% max. Auto loans: 35-40% max. Personal loans: 40-45% max. Credit Score Tiers Matter: Build rate sheets with score tiers (740+: prime, 680-739: near-prime, 620-679: subprime, below 620: denied or hard money) Income Verification Priorities: W-2 employees (easy), self-employed (complex), commission/bonus heavy (average 2 years), rental income (75% counts), gig economy (difficult) Document Checklist Customization: Vary required docs by loan type, amount, and risk profile to avoid over-documentation for low-risk loans Conditional Approval vs Outright Denial: When in doubt, use conditional - gives applicants path to approval and keeps them in pipeline Adverse Action Notices: For denials, include specific reasons (per FCRA requirements) and instructions for disputing credit report errors Pre-Qualification vs Pre-Approval: Pre-qual uses soft credit pull (no impact on score), pre-approval uses hard pull (official decision) Co-Borrower Logic: When DTI is high, automatically suggest co-borrower as option to strengthen application Rate Lock Automation: Pre-approved applications should include rate lock expiration date (typically 30-60 days) Property Appraisal Triggers: Auto-order appraisals for pre-approved mortgage applications to keep process moving Underwriter Dashboard: Build Google Sheets dashboard with filters for underwriters to sort by approval status, loan amount, date Compliance Monitoring: Regular audits of AI decisions to ensure no discriminatory patterns (disparate impact analysis) Customer Service Integration: Link application IDs to support tickets so agents can quickly pull up loan status Marketing Attribution: Track lead sources in form to measure which marketing channels produce best-quality applicants Learning Resources This workflow demonstrates advanced automation: AI Agents for Financial Analysis: Multi-dimensional loan qualification using BANT-style underwriting criteria Complex Conditional Logic: Multi-path routing with nested IF conditions for approval/conditional/denial workflows Financial Calculations: Automated DTI, LTV, DSCR, and payment estimation algorithms Risk Scoring Models: Comprehensive risk assessment combining credit, income, debt, and employment factors Decision Documentation: Complete audit trail with AI reasoning for regulatory compliance Email Customization: Dynamic content generation based on approval outcomes and applicant situations Data Pipeline Design: Structured data flow from application through analysis to decision and notification Simulation vs Production: Credit check node designed for easy swap from simulation to real API integration Parallel Processing: Simultaneous logging and notification workflows for efficiency Workflow Orchestration: Coordination of multiple decision points and communication touchpoints Questions or customization? The workflow includes detailed sticky notes explaining each analysis component and decision logic. Template Compatibility ✅ n8n version 1.0+ ✅ Works with n8n Cloud and Self-Hosted ✅ Production-ready for financial institutions ✅ Fully customizable for any loan type Compliance Note: This template is designed for demonstration and automation purposes. Always consult with legal counsel to ensure compliance with TILA, RESPA, ECOA, FCRA, and applicable state lending regulations before deploying in production.

Inbound Agent - AI-Powered Lead Qualification with Product Usage Intelligence This n8n workflow automatically qualifies and scores inbound leads by combining their product usage patterns with deep company intelligence. The workflow pulls new leads from your CRM, analyzes which API endpoints they've been testing, enriches them with firmographic data, and generates comprehensive qualification reports with personalized talking points—giving your sales team everything they need to prioritize and convert high-quality leads. DEMO Template Demo Credentials Required To use this workflow, set up the following credentials in your n8n environment: Salesforce Type:** OAuth2 or Username/Password Used for:** Pulling lead reports and creating follow-up tasks Alternative CRM options: HubSpot, Zoho, Pipedrive Get credentials at Salesforce Setup Databricks (or Analytics Platform) Type:** HTTP Request with Bearer Token Header:** Authorization Value:** Bearer YOUR_DATABRICKS_TOKEN Used for:** Querying product usage and API endpoint data Alternative options: Datadog, Mixpanel, Amplitude, custom data warehouse Explorium API Type:** Generic Header Auth Header:** Authorization Value:** Bearer YOUR_API_KEY Used for:** Business matching and firmographic enrichment Get your API key at Explorium Dashboard Explorium MCP Type:** HTTP Header Auth Used for:** Real-time company intelligence and supplemental research Connect to: https://mcp.explorium.ai/mcp Anthropic API Type:** API Key Used for:** AI-powered lead qualification and analysis Get your API key at Anthropic Console Go to Settings → Credentials, create these credentials, and assign them in the respective nodes before running the workflow. Workflow Overview Node 1: When clicking 'Execute workflow' Manual trigger that initiates the lead qualification process. Type:** Manual Trigger Purpose:** On-demand execution for testing or manual runs Alternative Trigger Options: Schedule Trigger:** Run automatically (hourly, daily, weekly) Webhook:** Trigger on CRM updates or new lead events CRM Trigger:** Real-time activation when leads are created Node 2: GET SF Report Pulls lead data from a pre-configured Salesforce report. Method:** GET Endpoint:** Salesforce Analytics Reports API Authentication:** Salesforce OAuth2 Returns: Raw Salesforce report data including: Lead contact information Company names Lead source and status Created dates Custom fields CRM Alternatives: This node can be replaced with HubSpot, Zoho, or any CRM's reporting API. Node 3: Extract Records Parses the Salesforce report structure and extracts individual lead records. Extraction Logic: Navigates report's factMap['T!T'].rows structure Maps data cells to named fields Node 4: Extract Tenant Names Prepares tenant identifiers for usage data queries. Purpose: Formats tenant names as SQL-compatible strings for the Databricks query Output: Comma-separated, quoted list: 'tenant1', 'tenant2', 'tenant3' Node 5: Query Databricks Queries your analytics platform to retrieve API usage data for each lead. Method:** POST Endpoint:** /api/2.0/sql/statements Authentication:** Bearer token in headers Warehouse ID:** Your Databricks cluster ID Platform Alternatives: Datadog:** Query logs via Logs API Mixpanel:** Event segmentation API Amplitude:** Behavioral cohorts API Custom Warehouse:** PostgreSQL, Snowflake, BigQuery queries Node 6: Split Out Splits the Databricks result array into individual items for processing. Field:** result.data_array Purpose:** Transform single response with multiple rows into separate items Node 7: Rename Keys Normalizes column names from database query to readable field names. Mapping: 0 → TenantNames 1 → endpoints 2 → endpointsNum Node 8: Extract Business Names Prepares company names for Explorium enrichment. Node 9: Loop Over Items Iterates through each company for individual enrichment. Node 10: Explorium API: Match Businesses Matches company names to Explorium's business entity database. Method:** POST Endpoint:** /v1/businesses/match Authentication:** Header Auth (Bearer token) Returns: business_id: Unique Explorium identifier matched_businesses: Array of potential matches Match confidence scores Node 11: Explorium API: Firmographics Enriches matched businesses with comprehensive company data. Method:** POST Endpoint:** /v1/businesses/firmographics/bulk_enrich Authentication:** Header Auth (Bearer token) Returns: Company name, website, description Industry categories (NAICS, SIC, LinkedIn) Size: employee count range, revenue range Location: headquarters address, city, region, country Company age and founding information Social profiles: LinkedIn, Twitter Logo and branding assets Node 12: Merge Combines API usage data with firmographic enrichment data. Node 13: Organize Data as Items Structures merged data into clean, standardized lead objects. Data Organization: Maps API usage by tenant name Maps enrichment data by company name Combines with original lead information Creates complete lead profile for analysis Node 14: Loop Over Items1 Iterates through each qualified lead for AI analysis. Batch Size:** 1 (analyzes leads individually) Purpose:** Generate personalized qualification reports Node 15: Get many accounts1 Fetches the associated Salesforce account for context. Resource:** Account Operation:** Get All Filter:** Match by company name Limit:** 1 record Purpose: Link lead qualification back to Salesforce account for task creation Node 16: AI Agent Analyzes each lead to generate comprehensive qualification reports. Input Data: Lead contact information API usage patterns (which endpoints tested) Firmographic data (company profile) Lead source and status Analysis Process: Evaluates lead quality based on usage, company fit, and signals Identifies which Explorium APIs the lead explored Assesses company size, industry, and potential value Detects quality signals (legitimate company email, active usage) and red flags Determines optimal sales approach and timing Connected to Explorium MCP for supplemental company research if needed Output: Structured qualification report with: Lead Score:** High Priority, Medium Priority, Low Priority, or Nurture Quick Summary:** Executive overview of lead potential API Usage Analysis:** Endpoints used, usage insights, potential use case Company Profile:** Overview, fit assessment, potential value Quality Signals:** Positive indicators and concerns Recommended Actions:** Next steps, timing, and approach Talking Points:** Personalized conversation starters based on actual API usage Node 18: Clean Outputs Formats the AI qualification report for Salesforce task creation. Node 19: Update Salesforce Records Creates follow-up tasks in Salesforce with qualification intelligence. Resource:** Task Operation:** Create Authentication:** Salesforce OAuth2 Alternative Output Options: HubSpot:** Create tasks or update deal stages Outreach/SalesLoft:** Add to sequences with custom messaging Slack:** Send qualification reports to sales channels Email:** Send reports to account owners Google Sheets:** Log qualified leads for tracking Workflow Flow Summary Trigger: Manual execution or scheduled run Pull Leads: Fetch new/updated leads from Salesforce report Extract: Parse lead records and tenant identifiers Query Usage: Retrieve API endpoint usage data from analytics platform Prepare: Format data for enrichment Match: Identify companies in Explorium database Enrich: Pull comprehensive firmographic data Merge: Combine usage patterns with company intelligence Organize: Structure complete lead profiles Analyze: AI evaluates each lead with quality scoring Format: Structure qualification reports for CRM Create Tasks: Automatically populate Salesforce with actionable intelligence This workflow eliminates manual lead research and qualification, automatically analyzing product engagement patterns alongside company fit to help sales teams prioritize and personalize their outreach to the highest-value inbound leads. Customization Options Flexible Triggers Replace the manual trigger with: Schedule:** Run hourly/daily to continuously qualify new leads Webhook:** Real-time qualification when leads are created CRM Trigger:** Activate on specific lead status changes Analytics Platform Integration The Databricks query can be adapted for: Datadog:** Query application logs and events Mixpanel:** Analyze user behavior and feature adoption Amplitude:** Track product engagement metrics Custom Databases:** PostgreSQL, MySQL, Snowflake, BigQuery CRM Flexibility Works with multiple CRMs: Salesforce:** Full integration (pull reports, create tasks) HubSpot:** Contact properties and deal updates Zoho:** Lead enrichment and task creation Pipedrive:** Deal qualification and activity creation Enrichment Depth Add more Explorium endpoints: Technographics:** Tech stack and product usage News & Events:** Recent company announcements Funding Data:** Investment rounds and financial events Hiring Signals:** Job postings and growth indicators Output Destinations Route qualification reports to: CRM Updates:** Salesforce, HubSpot (update lead scores/fields) Task Creation:** Any CRM task/activity system Team Notifications:** Slack, Microsoft Teams, Email Sales Tools:** Outreach, SalesLoft, Salesloft sequences Reporting:** Google Sheets, Data Studio dashboards AI Model Options Swap AI providers: Default: Anthropic Claude (Sonnet 4) Alternatives: OpenAI GPT-4, Google Gemini Setup Notes Salesforce Report Configuration: Create a report with required fields (name, email, company, tenant ID) and use its API endpoint Tenant Identification: Ensure your product usage data includes identifiers that link to CRM leads Usage Data Query: Customize the SQL query to match your database schema and table structure MCP Configuration: Explorium MCP requires Header Auth—configure credentials properly Lead Scoring Logic: Adjust AI system prompts to match your ideal customer profile and qualification criteria Task Assignment: Configure Salesforce task assignment rules or add logic to route to specific sales reps This workflow acts as an intelligent lead qualification system that combines behavioral signals (what they're testing) with firmographic fit (who they are) to give sales teams actionable intelligence for every inbound lead.

Make your n8n instance faster, cleaner, and more efficient by deleting old workflow executions — while keeping only the most recent ones you actually need. Whether you're using n8n Cloud or self-hosted, this lightweight workflow helps reduce database/storage usage and improves UI responsiveness, using only official n8n nodes. 🔍 Description Automatically clean up old executions in your n8n instance using only official nodes — no external database queries required. Whether you're on the Cloud version or running self-hosted, this workflow helps you optimize performance and keep your instance tidy by maintaining only the most recent executions per workflow. Ideal for users managing dozens or hundreds of workflows, this solution reduces storage usage and improves the responsiveness of the n8n UI, especially in environments where execution logs can accumulate quickly. ✅ What It Does Retrieves up to 250 recent executions across all workflows Groups executions by workflow Keeps only the most recent N executions per workflow (value is configurable) Deletes all older executions (regardless of their status: success, error, etc.) Works entirely with native n8n nodes — no external database access required Optionally: set the number of executions to keep as 0 to delete all past executions from your instance in a single run 🛠️ How to Set Up 🔑 Create a Personal API Key in your n8n instance: Go to Settings → API Keys → Create a new key 🔧 Create a new n8n API Credential (used by both nodes): In your n8n credentials panel: Name: anything you like (e.g., “Internal API Access”) API Key: paste the Personal API Key you just created Base URL: your full n8n instance URL with the /api/v1 path, e.g. https://your-n8n-instance.com/api/v1 ✅ Use this credential in both: The Get Many Executions node (to fetch recent executions) The Delete Many Executions node (to remove outdated executions) 🧩 In the “Set Executions to Keep” node: Edit the variable executionsToKeep and set the number of most recent executions to retain per workflow (e.g. 10) Tip: Set it to 0 to delete all executions 📦 Note: The “Get Many Executions” node will retrieve up to 250 executions per run — this is the maximum allowed by the n8n API. 🧠 No further setup is required — the filtering and grouping logic is handled inside the Code Node automatically. 🧪 Included Nodes Overview 🕒 Schedule Trigger → Set to run daily, weekly, etc. 📥 Get Many Executions → Fetches past executions via n8n API 🛠️ Set Executions to Keep → Set how many recent ones to keep 🧠 Code Node → Filters out executions to delete per workflow 🗑️ Delete Executions → Deletes outdated executions 💡 Why Use This? Reduce clutter and improve performance in your n8n instance Maintain execution logs only when they’re useful Avoid bloating your storage or database with obsolete data Compatible with both n8n Cloud and self-hosted setups Uses only official, supported n8n nodes — no SQL, no extra setup 🔒 This workflow modifies and deletes execution data. Always review and test it first on a staging instance or on a limited set of workflows before using it in production.

This smart automation workflow created by the AI development team at WeblineIndia, helps with the daily collection and storage of weather data. Using the OpenWeatherMap API and Airtable, this solution gathers vital weather details such as temperature, humidity, and wind speed. The automation ensures daily updates, creating a dependable historical record of weather patterns for future reference and analysis. Steps: Set Schedule Trigger Configure a Cron node to trigger the workflow daily, for example, at 7 AM. Fetch Weather Data (HTTP Request) Use the HTTP Request node to retrieve weather data from the OpenWeatherMap API. Include your API key and query parameters (e.g., q=London, unit=metric) to specify the city and desired units. Parse Weather Data Utilize a JSON Parse node to extract key weather details, such as temperature, humidity, and wind speed, from the API response. Store Data in Airtable Use the Airtable node to insert the parsed data into the designated Airtable table. Ensure proper mapping of fields like temperature, humidity, and wind speed. Save and Execute Save the workflow and activate it to ensure weather data is fetched and stored automatically every day. Outcome This robust solution, developed by WeblineIndia, reliably collects and archives daily weather data, providing businesses and individuals with an accessible record of weather trends for analysis and decision-making. About WeblineIndia We specialize in creating custom automation solutions and innovative software workflows to help businesses streamline operations and achieve efficiency. This weather data fetcher is just one example of our expertise in delivering value through technology.

🚫 Email Unsubscribe Handler for Outlook Description This n8n workflow automatically scans recent email replies from your Outlook inbox and identifies unsubscribe requests. If a contact replies with any variation of "unsubscribe" within the past 7 days, the system performs two key actions: Saves the contact’s email address in a BigQuery unsubscribes table (for compliance and tracking). Deletes that contact from the active leads table in BigQuery (to stop future outreach). This flow can be triggered on a schedule (e.g. every 4 hours) or run manually as needed. Key Features 📥 Email Parsing from Outlook: Automatically monitors for replies that contain unsubscribe language. 🧠 Smart Filtering: Captures unsubscribes based on message content, not just subject lines. 🔄 BigQuery Integration: Logs unsubscribed emails and removes them from your leads dataset. 🤝 Connect with Me Description I’m Robert Breen, founder of Ynteractive — a consulting firm that helps businesses automate operations using n8n, AI agents, and custom workflows. I’ve helped clients build everything from intelligent chatbots to complex sales automations, and I’m always excited to collaborate or support new projects. If you found this workflow helpful or want to talk through an idea, I’d love to hear from you. Links 🌐 Website: https://www.ynteractive.com 📺 YouTube: @ynteractivetraining 💼 LinkedIn: https://www.linkedin.com/in/robert-breen 📬 Email: rbreen@ynteractive.com