Workflow Templates

This n8n template from Intuz provides a complete solution to automate your entire invoicing process. It intelligently syncs confirmed sales orders from your Airtable base to QuickBooks, automatically creating new customers if they don't exist before generating a perfectly matched invoice. It then logs all invoice details back into Airtable, creating a flawless, end-to-end financial workflow. Use Cases 1. Accounting & Finance Teams: Automatically generate QuickBooks invoices from new orders confirmed in Airtable. Keep all invoices and customer details synced across systems in real time. 2. Sales & Operations Teams: Track order status and billing progress directly from Airtable without switching platforms. Ensure every confirmed sale automatically triggers an invoice in QuickBooks. 3. Business Owners / Admins: Eliminate double-entry between Airtable and QuickBooks. Maintain accurate, audit-ready financial records with minimal effort. How it works 1. Trigger from Airtable: The workflow starts instantly when a sales order is ready to be invoiced in your Airtable base (triggered via a webhook). 2. Check for Customer in QuickBooks: It searches your QuickBooks account to see if the customer from the sales order already exists. 3. Create New Customer (If Needed): If the customer is not found, it automatically creates a new customer record in QuickBooks using the details from your Airtable Customers table. 4. Create QuickBooks Invoice: Using the correct customer record (either existing or newly created), it gathers all order line items from Airtable and generates a detailed invoice in QuickBooks. 5. Log Invoice Back to Airtable: After the invoice is successfully created, the workflow updates your Airtable base by adding a new record to your Invoices & Payments table and updating the original Confirmed Orders record with the new QuickBooks Invoice ID, marking it as synced. Key Requirements to Use This Template 1. n8n Instance: An active n8n account (Cloud or self-hosted). 2. Airtable Base: An Airtable base on a "Pro" plan or higher with tables for Confirmed Orders, Customers, Order Lines, Product & Service, and Invoices & Payments. Field names must match those in the setup guide. 3. QuickBooks Online Account: An active QuickBooks Online account with API access. Step-by-Step Setup Instructions Step 1: Import and Configure the n8n Workflow Import Workflow:** In n8n, import the Client-Quickbook-Invoices-via-AirTable.json file. Get Webhook URL:** Click on the first node, "Webhook". Copy the "Test URL". Keep this n8n tab open. Configure Airtable Nodes:** There are six Airtable nodes. For each one, connect your Airtable credentials and select the correct Base and Table. Configure QuickBooks Nodes:** There are four QuickBooks-related nodes. For each one, connect your QuickBooks Online credentials. CRITICAL:** Click on the "Create Invoice URL" (HTTP Request) node. You must edit the URL and replace the placeholder number (9341455145770046) with your own QuickBooks Company ID. (Find this in your QuickBooks account settings under "Billing & Subscription"). Save and Activate**: Click "Save", then toggle the workflow to "Active". After activating, copy the new "Production URL" from the Webhook node. Customization Guide You can adapt this template for various workflows by tweaking a few nodes: Use a different Airtable Base:** Update the Base ID and Table ID in all Airtable nodes (Get Orders Records, Get Customer Details, Get Products, etc.). Switch from Sandbox to Live QuickBooks:** Replace the Sandbox company ID and endpoint in the “Create Invoice URL” node with your production QuickBooks company ID. Add more invoice details:** Edit the Code and Parse in HTTP nodes to include additional fields (like Tax, Shipping, or Notes). Support multiple currencies:** Add a “Currency” field mapping in both Airtable and QuickBooks nodes. Connect with us Website: https://www.intuz.com/services Email: getstarted@intuz.com LinkedIn: https://www.linkedin.com/company/intuz Get Started: https://n8n.partnerlinks.io/intuz For Custom Workflow Automation Click here- Get Started

Icebreaker Generator powered with ChatGPT This n8n template crawls a company website, distills the content with AI, and produces a short, personalized icebreaker you can drop straight into your cold emails or CRM. Perfect for SDRs, founders, and agencies who want “real research” at scale. Good to know Works from a Google Sheet of leads (domain + LinkedIn, etc.). Handles common scrape failures gracefully and marks the lead’s Status as Error. Uses ChatGPT to summarize pages and craft one concise, non-generic opener. Output is written back to the same Google Sheet (IceBreaker, Status). You’ll need Google credentials (for Sheets) and OpenAI credentials (for GPT). How it works Step 1 — Discover internal pages Reads a lead’s website from Google Sheets. Scrapes the home page and extracts all links. A Code node cleans the list (removes emails/anchors/social/external domains, normalizes paths, de-duplicates) and returns unique internal URLs. If the home page is unreachable or no links are found, the lead is marked Error and the workflow moves on. Step 2 — Convert pages to text Visits each collected URL and converts the response into HTML/Markdown text for analysis. You can cap depth/amount with the Limit node. Step 3 — Summarize & generate the icebreaker A GPT node produces a two-paragraph abstract for each page (JSON output). An Aggregate node merges all abstracts for the company. Another GPT node turns the merged summary into a personalized, multi-line icebreaker (spartan tone, non-obvious details). The result is written back to Google Sheets (IceBreaker = ..., Status = Done). The workflow loops to the next lead. How to use Prepare your sheet Include at least: organization_website_url, linkedin_url, and any other lead fields you track. Keep an empty IceBreaker and Status column for the workflow to fill. Connect credentials Google Sheets: use the Google account that owns the sheet and link it in the nodes. OpenAI: add your API key to the GPT nodes (“Summarize Website Page”, “Generate Multiline Icebreaker”). Run the workflow Start with the Manual Trigger (or replace with a schedule/webhook). Adjust Limit if you want fewer/more pages per company. Watch Status (Done/Error) and IceBreaker populate in your sheet. Requirements n8n instance Google Sheets account & access to the leads sheet OpenAI API key (for summarization + icebreaker generation) Customizing this workflow Tone & format: tweak the prompts (both GPT nodes) to match your brand voice and structure. Depth: change the Limit node to scan more/less pages; add simple rules to prioritize certain paths (e.g., /about, /blog/*). Fields: write additional outputs (e.g., Company Summary, Key Products, Recent News) back to new sheet columns. Lead selection: filter rows by Status = "" (or custom flags) to only process untouched leads. Error handling: expand the Error branch to retry with www./HTTP→HTTPS or to log diagnostics in a separate tab. Tips Keep icebreakers short, specific, and free of clichés—small, non-obvious details from the site convert best. Start with a small batch to validate quality, then scale up. Consider adding a rate limit if target sites throttle requests. In short: Sheet → crawl internal pages → AI abstracts → single tailored icebreaker → write back to the sheet, then repeat for the next lead. This automation can work great with our automation for automated cold emailing.

How it works This Lead Capture & Auto-Qualification workflow transforms raw leads into qualified prospects through intelligent automation. Here's the high-level flow: Lead Intake → Data Validation → Enrichment → Scoring → Smart Routing → CRM Integration & Notifications The system captures leads from any source, validates the data, enriches it with company intelligence, scores based on qualification criteria, and automatically routes high-value prospects to sales while nurturing lower-priority leads. Set up steps Time to set up: Approximately 30-45 minutes Prerequisites: Active accounts with HubSpot, Clearbit, Apollo, and Slack Step 1: Import Workflow (2 minutes) Copy the workflow JSON and import into your n8n instance The workflow will appear with all nodes and sticky note documentation Step 2: Configure Environment Variables (5 minutes) Set these in your n8n environment: APOLLO_API_URL SLACK_SALES_CHANNEL_ID SLACK_MARKETING_CHANNEL_ID CRM_ASSIGNMENT_URL Step 3: Set Up API Credentials (15 minutes) Create credential connections for: Clearbit API (enrichment) Apollo API (HTTP Header Auth) HubSpot API (CRM integration) Slack API (notifications) Step 4: Customize Scoring Logic (10 minutes) Review the qualification criteria in the Code node Adjust scoring weights based on your ideal customer profile Modify industry targeting and company size thresholds Step 5: Test & Activate (8 minutes) Send test webhook requests to validate the flow Verify CRM contact creation and Slack notifications Activate the workflow for live lead processing

Introduction Automates flight deal discovery and intelligent analysis for travel bloggers and deal hunters. Scrapes live pricing, enriches with weather data, applies AI evaluation, and auto-publishes to WordPress—eliminating manual research and accelerating content delivery. How It Works User submits route via form, scrapes real-time flight prices and weather data, AI analyzes deal quality considering weather conditions, formats results, publishes to WordPress, sends Slack notification—fully automated from input to publication. Workflow Template Form Input → Extract Data → Scrape Flight Prices → Extract Pricing → Fetch Weather → Parse Weather → Prepare AI Input → AI Analysis → Parse Output → Format Results → Publish WordPress → Slack Alert → User Response Setup Instructions Form Setup: Configure user input fields for flight routes and preferences APIs: Connect Google Flights scraping endpoint, weather API credentials, OpenAI/Chat Model API key Publishing: Set WordPress credentials, target blog category, Slack webhook URL AI Configuration: Define analysis prompts, output structure, parser rules Workflow Steps Data Collection: Form captures route, scrapes Google Flights pricing, fetches destination weather via API AI Processing: Enriches flight data with weather context, analyzes deal quality using OpenAI/Chat Model with structured output parsing Publishing: Formats analysis results, creates WordPress post, sends Slack notification, delivers response to user Prerequisites n8n instance, Google Flights access, weather API key, OpenAI/compatible AI service, WordPress site with API access, Slack workspace Use Cases Travel blog automation, flight deal newsletters, price comparison services, seasonal travel planning, destination weather analysis, automated social media content Customization Modify AI analysis criteria, adjust weather impact weighting, customize WordPress post templates, add email distribution, integrate additional data sources, expand to hotel/rental deals Benefits Eliminates manual price checking, combines multiple data sources automatically, delivers AI-enhanced insights, accelerates publishing workflow, scales across unlimited routes, provides weather-aware recommendations

AWS News Analysis and LinkedIn Automation Pipeline Transform AWS industry news into engaging LinkedIn content with AI-powered analysis and automated approval workflows. Who's it for This template is perfect for: Cloud architects and DevOps engineers** who want to stay current with AWS developments Content creators** looking to automate their AWS news coverage Marketing teams** needing consistent, professional AWS content Technical leaders** who want to share industry insights on LinkedIn AWS consultants** building thought leadership through automated content How it works This workflow creates a comprehensive AWS news analysis and content generation pipeline with two main flows: Flow 1: News Collection and Analysis Scheduled RSS Monitoring: Automatically fetches latest AWS news from the official AWS RSS feed daily at 8 PM AI-Powered Analysis: Uses AWS Bedrock (Claude 3 Sonnet) to analyze each news item, extracting: Professional summary Key themes and keywords Importance rating (Low/Medium/High) Business impact assessment Structured Data Storage: Saves analyzed news to Feishu Bitable with approval status tracking Flow 2: LinkedIn Content Generation Manual Approval Trigger: Feishu automation sends approved news items to the webhook AI Content Creation: AWS Bedrock generates professional LinkedIn posts with: Attention-grabbing headlines Technical insights from a Solutions Architect perspective Business impact analysis Call-to-action engagement Automated Publishing: Posts directly to LinkedIn with relevant hashtags How to set up Prerequisites AWS Bedrock access** with Claude 3 Sonnet model enabled Feishu account** with Bitable access LinkedIn company account** with posting permissions n8n instance** (self-hosted or cloud) Detailed Configuration Steps 1. AWS Bedrock Setup Step 1: Enable Claude 3 Sonnet Model Log into your AWS Console Navigate to AWS Bedrock Go to Model access in the left sidebar Find Anthropic Claude 3 Sonnet and click Request model access Fill out the access request form (usually approved within minutes) Once approved, verify the model appears in your Model access list Step 2: Create IAM User and Credentials Go to IAM Console Click Users → Create user Name: n8n-bedrock-user Attach policy: AmazonBedrockFullAccess (or create custom policy with minimal permissions) Go to Security credentials tab → Create access key Choose Application running outside AWS Download the credentials CSV file Step 3: Configure in n8n In n8n, go to Credentials → Add credential Select AWS credential type Enter your Access Key ID and Secret Access Key Set Region to your preferred AWS region (e.g., us-east-1) Test the connection Useful Links: AWS Bedrock Documentation Claude 3 Sonnet Model Access AWS Bedrock Pricing 2. Feishu Bitable Configuration Step 1: Create Feishu Account and App Sign up at Feishu International Create a new Bitable (multi-dimensional table) Go to Developer Console → Create App Enable Bitable permissions in your app Generate App Token and App Secret Step 2: Create Bitable Structure Create a new Bitable with these columns: title (Text) pubDate (Date) summary (Long Text) keywords (Multi-select) rating (Single Select: Low, Medium, High) link (URL) approval_status (Single Select: Pending, Approved, Rejected) Get your App Token and Table ID: App Token: Found in app settings Table ID: Found in the Bitable URL (tbl...) Step 3: Set Up Automation In your Bitable, go to Automation → Create automation Trigger: When field value changes → Select approval_status field Condition: approval_status equals "Approved" Action: Send HTTP request Method: POST URL: Your n8n webhook URL (from Flow 2) Headers: Content-Type: application/json Body: {{record}} Step 4: Configure Feishu Credentials in n8n Install Feishu Lite community node (self-hosted only) Add Feishu credential with your App Token and App Secret Test the connection Useful Links: Feishu Developer Documentation Bitable API Reference Feishu Automation Guide 3. LinkedIn Company Account Setup Step 1: Create LinkedIn App Go to LinkedIn Developer Portal Click Create App Fill in app details: App name: AWS News Automation LinkedIn Page: Select your company page App logo: Upload your logo Legal agreement: Accept terms Step 2: Configure OAuth2 Settings In your app, go to Auth tab Add redirect URL: https://your-n8n-instance.com/rest/oauth2-credential/callback Request these scopes: w_member_social (Post on behalf of members) r_liteprofile (Read basic profile) r_emailaddress (Read email address) Step 3: Get Company Page Access Go to your LinkedIn Company Page Navigate to Admin tools → Manage admins Ensure you have Content admin or Super admin role Note your Company Page ID (found in page URL) Step 4: Configure LinkedIn Credentials in n8n Add LinkedIn OAuth2 credential Enter your Client ID and Client Secret Complete OAuth2 flow by clicking Connect my account Select your company page for posting Useful Links: LinkedIn Developer Portal LinkedIn API Documentation LinkedIn OAuth2 Guide 4. Workflow Activation Final Setup Steps: Import the workflow JSON into n8n Configure all credential connections: AWS Bedrock credentials Feishu credentials LinkedIn OAuth2 credentials Update webhook URL in Feishu automation to match your n8n instance Activate the scheduled trigger (daily at 8 PM) Test with manual webhook trigger using sample data Verify Feishu Bitable receives data Test approval workflow and LinkedIn posting Requirements Service Requirements AWS Bedrock** with Claude 3 Sonnet model access AWS account with Bedrock service enabled IAM user with Bedrock permissions Model access approval for Claude 3 Sonnet Feishu Bitable** for news storage and approval workflow Feishu account (International or Lark) Developer app with Bitable permissions Automation capabilities for webhook triggers LinkedIn Company Account** for automated posting LinkedIn company page with admin access LinkedIn Developer app with posting permissions OAuth2 authentication setup n8n community nodes**: Feishu Lite node (self-hosted only) Technical Requirements n8n instance** (self-hosted recommended for community nodes) Webhook endpoint** accessible from Feishu automation Internet connectivity** for API calls and RSS feeds Storage space** for workflow execution logs Cost Considerations AWS Bedrock**: ~$0.01-0.05 per news analysis Feishu**: Free tier available, paid plans for advanced features LinkedIn**: Free API access with rate limits n8n**: Self-hosted (free) or cloud subscription How to customize the workflow Content Customization Modify AI prompts** in the AI Agent nodes to change tone, focus, or target audience Adjust hashtags** in the LinkedIn posting node for different industries Change scheduling** frequency by modifying the Schedule Trigger settings Integration Options Replace LinkedIn** with Twitter/X, Facebook, or other social platforms Add Slack notifications** for approved content before posting Integrate with CRM** systems to track content performance Add content calendar** integration for better planning Advanced Features Multi-language support** by modifying AI prompts for different regions Content categorization** by adding tags for different AWS services Performance tracking** by integrating analytics platforms Team collaboration** by adding approval workflows with multiple reviewers Technical Modifications Change RSS sources** to monitor other AWS blogs or competitor news Adjust AI models** to use different Bedrock models or external APIs Add data validation** nodes for better error handling Implement retry logic** for failed API calls Important Notes Service Limitations This template uses community nodes (Feishu Lite) and requires self-hosted n8n Geo-restrictions** may apply to AWS Bedrock models in certain regions Rate limits** may affect high-frequency posting - adjust scheduling accordingly Content moderation** is recommended before automated posting Cost considerations**: Each AI analysis costs approximately $0.01-0.05 USD per news item Troubleshooting Common Issues AWS Bedrock Issues: Model not found**: Ensure Claude 3 Sonnet access is approved in your region Access denied**: Verify IAM permissions include Bedrock service access Rate limiting**: Implement retry logic or reduce analysis frequency Feishu Integration Issues: Authentication failed**: Check App Token and App Secret are correct Table not found**: Verify Table ID matches your Bitable URL Automation not triggering**: Ensure webhook URL is accessible and returns 200 status LinkedIn Posting Issues: OAuth2 errors**: Re-authenticate LinkedIn credentials Posting failed**: Verify company page admin permissions Rate limits**: LinkedIn has daily posting limits for company pages Security Best Practices Never hardcode credentials** in workflow nodes Use environment variables** for sensitive configuration Regularly rotate API keys** and access tokens Monitor API usage** to prevent unexpected charges Implement error handling** for failed API calls

How it works This workflow automatically analyzes website visitors in real-time, enriches their data with company intelligence, and provides lead scoring and sales alerts. Key Steps Webhook Trigger - Receives visitor data from your website tracking system. AI-Powered Company Intelligence - Uses ScrapeGraphAI to extract comprehensive company information from visitor domains. Visitor Enrichment - Combines visitor behavior data with company intelligence to create detailed visitor profiles. Lead Scoring - Automatically scores leads based on company size, industry, engagement, and intent signals. CRM Integration - Updates your CRM with enriched visitor data and lead scores. Sales Alerts - Sends real-time notifications to your sales team for high-priority leads. Set up steps Setup time: 10-15 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key for company intelligence gathering. Set up HubSpot connection - Connect your HubSpot CRM to automatically update contact records. Configure Slack integration - Set up your Slack workspace and specify the sales alert channel. Customize lead scoring criteria - Adjust the scoring algorithm to match your target customer profile. Set up website tracking - Configure your website to send visitor data to the webhook endpoint. Test the workflow - Verify all integrations are working correctly with a test visitor. Key Features Real-time visitor analysis** with company intelligence enrichment Automated lead scoring** based on multiple factors (company size, industry, engagement) Intent signal detection** (pricing interest, demo requests, contact intent) Priority-based sales alerts** with recommended actions CRM integration** for seamless lead management Deal size estimation** based on company characteristics

Jira MCP Server Integration with n8n Overview Transform your Jira project management with the power of AI and automation! This n8n workflow template demonstrates how to create a seamless integration between chat interfaces, AI processing, and Jira Software using MCP (Model Context Protocol) server architecture. What This Workflow Does Chat-Driven Automation**: Trigger Jira operations through simple chat messages AI-Powered Issue Creation**: Automatically generate detailed Jira issues with descriptions and acceptance criteria Complete Jira Management**: Get issue status, changelogs, comments, and perform full CRUD operations Memory Integration**: Maintain context across conversations for smarter automations Zero Manual Entry**: Eliminate repetitive data entry and human errors Key Features ✅ Natural Language Processing: Use Google Gemini to understand and process chat requests ✅ MCP Server Integration: Secure, efficient communication with Jira APIs ✅ Comprehensive Jira Operations: Create, read, update, delete issues and comments ✅ Smart Memory: Context-aware conversations for better automation ✅ Multi-Action Workflow: Handle multiple Jira operations from a single trigger Demo Video 🎥 Watch the Complete Demo: Automate Jira Issue Creation with n8n & AI | MCP Server Integration Prerequisites Before setting up this workflow, ensure you have: n8n instance** (cloud or self-hosted) Jira Software** account with appropriate permissions Google Gemini API** credentials MCP Server** configured and accessible Basic understanding of n8n workflows Setup Guide Step 1: Import the Workflow Copy the workflow JSON from this template In your n8n instance, click Import > From Text Paste the JSON and click Import Step 2: Configure Google Gemini Open the Google Gemini Chat Model node Add your Google Gemini API credentials Configure the model parameters: Model: gemini-pro (recommended) Temperature: 0.7 for balanced creativity Max tokens: As per your requirements Step 3: Set Up MCP Server Connection Configure the MCP Client node: Server URL: Your MCP server endpoint Authentication: Add required credentials Timeout: Set appropriate timeout values Ensure your MCP server supports Jira operations: Issue creation and retrieval Comment management Status updates Changelog access Step 4: Configure Jira Integration Set up Jira credentials in n8n: Go to Credentials > Add Credential Select Jira Software API Add your Jira instance URL, email, and API token Configure each Jira node: Get Issue Status: Set project key and filters Create Issue: Define issue type and required fields Manage Comments: Set permissions and content rules Step 5: Memory Configuration Configure the Simple Memory node: Set memory key for conversation context Define memory retention duration Configure memory scope (user/session level) Step 6: Chat Trigger Setup Configure the When Chat Message Received trigger: Set up webhook URL or chat platform integration Define message filters if needed Test the trigger with sample messages Usage Examples Creating a Jira Issue Chat Input: Can you create an issue in Jira for Login Page with detailed description and acceptance criteria? Expected Output: New Jira issue created with structured description Automatically generated acceptance criteria Proper labeling and categorization Getting Issue Status Chat Input: What's the status of issue PROJ-123? Expected Output: Current issue status Last updated information Assigned user details Managing Comments Chat Input: Add a comment to issue PROJ-123: "Ready for testing in staging environment" Expected Output: Comment added to specified issue Notification sent to relevant team members Customization Options Extending Jira Operations Add more Jira operations (transitions, watchers, attachments) Implement custom field handling Create multi-project workflows AI Enhancement Fine-tune Gemini prompts for better issue descriptions Add custom validation rules Implement approval workflows Integration Expansion Connect to Slack, Discord, or Teams Add email notifications Integrate with time tracking tools Troubleshooting Common Issues MCP Server Connection Failed Verify server URL and credentials Check network connectivity Ensure MCP server is running and accessible Jira API Errors Validate Jira credentials and permissions Check project access rights Verify issue type and field configurations AI Response Issues Review Gemini API quotas and limits Adjust prompt engineering for better results Check model parameters and settings Performance Tips Optimize memory usage for long conversations Implement rate limiting for API calls Use error handling and retry mechanisms Monitor workflow execution times Best Practices Security: Store all credentials securely using n8n's credential system Testing: Test each node individually before running the complete workflow Monitoring: Set up alerts for workflow failures and API limits Documentation: Keep track of custom configurations and modifications Backup: Regular backup of workflow configurations and credentials Happy Automating! 🚀 This workflow template is designed to boost productivity and eliminate manual Jira management tasks. Customize it according to your team's specific needs and processes.

Overview Transform your accounts payable department with this enterprise-grade invoice processing solution. This workflow automates the entire invoice lifecycle - from document ingestion through payment processing. It handles invoices from multiple sources (Google Drive, email attachments, API submissions), extracts data using AI, validates against purchase orders, routes for appropriate approvals based on amount thresholds, and integrates seamlessly with your ERP system. The solution includes vendor master data management, duplicate invoice detection, real-time spend analytics, and complete audit trails for compliance. What You Can Do This comprehensive workflow creates an intelligent invoice processing pipeline that monitors multiple input channels (Google Drive, email, webhooks) for new invoices and automatically extracts data from PDFs, images, and scanned documents using AI. It validates vendor information against your master database, matches invoices to purchase orders, and detects discrepancies. The workflow implements multi-level approval routing based on invoice amount and department, prevents duplicate payments through intelligent matching algorithms, and integrates with QuickBooks, SAP, or other ERP systems. Additionally, it generates real-time dashboards showing processing metrics and cash flow insights while sending automated reminders for pending approvals. Who It's For Perfect for medium to large businesses, accounting departments, and financial service providers processing more than 100 invoices monthly across multiple vendors. Ideal for organizations that need to enforce approval hierarchies and spending limits, require integration with existing ERP/accounting systems, want to reduce processing time from days to minutes, need audit trails and compliance reporting, and seek to eliminate manual data entry errors and duplicate payments. The Problem It Solves Manual invoice processing creates significant operational challenges including data entry errors (3-5% error rate), processing delays (8-10 days per invoice), duplicate payments (0.1-0.5% of invoices), approval bottlenecks causing late fees, lack of visibility into pending invoices and cash commitments, and compliance issues from missing audit trails. This workflow reduces processing time by 80%, eliminates data entry errors, prevents duplicate payments, and provides complete visibility into your payables process. Setup Instructions Google Drive Setup: Create dedicated folders for invoice intake and configure access permissions PDF Vector Configuration: Set up API credentials with appropriate rate limits for your volume Database Setup: Deploy the provided schema for vendor master and invoice tracking tables Email Integration: Configure IMAP credentials for invoice email monitoring (optional) ERP Connection: Set up API access to your accounting system (QuickBooks, SAP, etc.) Approval Rules: Define approval thresholds and routing rules in the configuration node Notification Setup: Configure Slack/email for approval notifications and alerts Key Features Multi-Channel Invoice Ingestion**: Automatically collect invoices from Google Drive, email attachments, and API uploads Advanced OCR and AI Extraction**: Process any invoice format including handwritten notes and poor quality scans Vendor Master Integration**: Validate and enrich vendor data, maintaining a clean vendor database 3-Way Matching**: Automatically match invoices to purchase orders and goods receipts Dynamic Approval Routing**: Route based on amount, department, vendor, or custom rules Duplicate Detection**: Prevent duplicate payments using fuzzy matching algorithms Real-Time Analytics**: Track KPIs like processing time, approval delays, and early payment discounts Exception Handling**: Intelligent routing of problematic invoices for manual review Audit Trail**: Complete tracking of all actions, approvals, and system modifications Payment Scheduling**: Optimize payment timing to capture discounts and manage cash flow Customization Options This workflow can be customized to add industry-specific extraction fields, implement GL coding rules based on vendor or amount, create department-specific approval workflows, add currency conversion for international invoices, integrate with additional systems (banks, expense management), configure custom dashboards and reporting, set up vendor portals for invoice status inquiries, and implement machine learning for automatic GL coding suggestions. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.

Automating AWS S3 Operations with n8n: Buckets, Folders, and Files Watch the demo video below: This tutorial walks you through setting up an automated workflow that generates AI-powered images from prompts and securely stores them in AWS S3. It leverages the new AI Tool Node and OpenAI models for prompt-to-image generation. Who’s it for This workflow is ideal for: Designers & marketers** who need quick, on-demand AI-generated visuals. Developers & automation builders* exploring *AI-driven workflows** integrated with cloud storage. Educators or trainers** creating tutorials or exercises on AI image generation. Businesses* looking to automate *image content pipelines** with AWS S3 storage. How it works / What it does Trigger: The workflow starts manually when you click “Execute Workflow”. Edit Fields: You can provide input fields such as image description, resolution, or naming convention. Create AWS S3 Bucket: Automatically creates a new S3 bucket if it doesn’t exist. Create a Folder: Inside the bucket, a folder is created to organize generated images. Prompt Generation Agent: An AI agent generates or refines the image prompt using the OpenAI Chat Model. Generate an Image: The refined prompt is used to generate an image using AI. Upload File to S3: The generated image is uploaded to the AWS S3 bucket for secure storage. This workflow showcases how to combine AI + Cloud Storage seamlessly in an automated pipeline. How to set up Import the workflow into n8n. Configure the following credentials: AWS S3 (Access Key, Secret Key, Region). OpenAI API Key (for Chat + Image models). Update the Edit Fields node with your preferred input fields (e.g., image size, description). Execute the workflow and test by entering a sample image prompt (e.g., “Futuristic city skyline in watercolor style”). Check your AWS S3 bucket to verify the uploaded image. Requirements n8n** (latest version with AI Tool Node support). AWS account** with S3 permissions to create buckets and upload files. OpenAI API key** (for prompt refinement and image generation). Basic familiarity with AWS S3 structure (buckets, folders, objects). How to customize the workflow Custom Buckets**: Replace the auto-create step with an existing S3 bucket. Image Variations**: Generate multiple image variations per prompt by looping the image generation step. File Naming**: Adjust file naming conventions (e.g., timestamp, user input). Metadata**: Add metadata such as tags, categories, or owner info when uploading to S3. Alternative Storage: Swap AWS S3 with **Google Cloud Storage, Azure Blob, or Dropbox. Trigger Options: Replace manual trigger with **Webhook, Form Submission, or Scheduler for automation. ✅ This workflow is a hands-on example of how to combine AI prompt engineering, image generation, and cloud storage automation into a single streamlined process.

This n8n workflow automatically scrapes TechCrunch articles, filters for AI-related content using OpenAI, and delivers curated summaries to your Slack channels. Perfect for individuals or teams who need to stay current on artificial intelligence developments without manually browsing tech news sites. Who's it for AI product teams tracking industry developments and competitive moves Tech investors monitoring AI startup coverage and funding announcements Marketing teams following AI trends for content and positioning strategies Executives needing daily AI industry briefings without manual research overhead Development teams staying current on AI tools, frameworks, and breakthrough technologies How it works The workflow runs on a daily schedule, crawling a specificed amount of TechCrunch articles from the current year. Firecrawl extracts clean markdown content while bypassing anti-bot measures and handling JavaScript rendering automatically. Each article gets analyzed by an AI research assistant that determines if the content relates to artificial intelligence, machine learning, AI companies, or AI technology. Articles marked as "NOT_AI_RELATED" get filtered out automatically. For AI-relevant articles, OpenAI generates focused 3-bullet-point summaries that capture key insights. These summaries get delivered to your specified Slack channel with the original TechCrunch article title and source link for deeper reading. How to set up Configure Firecrawl: Add your Firecrawl API key to the HTTP Request node Set OpenAI credentials: Add your OpenAI API key to the AI Agent node Connect Slack: Configure your Slack webhook URL and target channel Adjust scheduling: Set your preferred trigger frequency (daily recommended) Test the workflow: Run manually to verify article extraction and Slack delivery Requirements Firecrawl account** with API access for TechCrunch web scraping OpenAI API key** for AI content analysis and summarization Slack workspace** with webhook permissions for message delivery n8n instance** (cloud or self-hosted) for workflow execution How to customize the workflow Source expansion: Modify the HTTP node URL to target additional tech publications beyond TechCrunch, or adjust the article limit and date filtering for different coverage needs. AI focus refinement: Update the OpenAI prompt to focus on specific AI verticals like generative AI, robotics, or ML infrastructure. Add company names or technology terms to the relevance filtering logic. Summary formats: Change from 3-bullet summaries to executive briefs, technical analyses, or competitive intelligence reports by modifying the OpenAI summarization prompt. Multi-channel delivery: Extend beyond Slack to email notifications, Microsoft Teams, or database storage for historical trend analysis and executive dashboards.

What this template does Receives meeting data via a webform, cleans/structures it, fills a Word docx template, uploads the file to SharePoint, appends a row to Excel 365, and sends an Outlook email with the document attached. Good to know Uses a community node: DocxTemplater to render the DOCX from a template. Install it from the Community Nodes catalog. The template context is the workflow item JSON. In your docx file, use placeholders. Includes a minimal HTML form snippet (outside n8n) you can host anywhere. Replace the placeholder WEBHOOK_URL with your Webhook URL before testing. Microsoft nodes require Azure app credentials with correct permissions (SharePoint, Excel/Graph, Outlook). How it works Webhook — Receives meeting form JSON (POST). Code (Parse Meeting Data) — Parses/normalizes fields, builds semicolon‑separated strings for attendees/absentees, and flattens discussion points / action items. SharePoint (Download) — Fetches the DOCX template (e.g., meeting_minutes_template.docx). Merge — Combines template binary + JSON context by position. DocxTemplater — Renders meeting_{{now:yyyy-MM-dd}}.docx using the JSON context. SharePoint (Upload) — Saves the generated DOCX to a target folder (e.g., /Meetings). Microsoft Excel 365 (Append) — Appends a row to your sheet (Date, Time, Attendees, etc.). Microsoft Outlook (Send message) — Emails the generated DOCX as an attachment. Requirements Community node DocxTemplater installed Microsoft 365 access with credentials for: SharePoint (download template + upload output) Excel 365 (append to table/worksheet) Outlook (send email) A Word template with placeholders matching the JSON keys Need Help 🔗 LinkedIn – Wessel Bulte

This workflow automatically follows the steps in a custom incident response playbook and manages incidents in PagerDuty, Jira tickets, and notifies the on-call team in Mattermost. This workflow consists of three sub-workflows, each automating specific steps in the playbook. Read more about this use case and learn how to set up the workflows step-by-step in the blog tutorial How to automate every step of an incident response workflow. Prerequisites A PagerDuty account and credentials A Mattermost account and credentials A Jira account and credentials Nodes Webhook nodes trigger the workflows when an incident is created in PagerDuty, and when the incidedent is acknowledged and resolved. Mattermost nodes create an auxiliary channel for the on-call team to discuss the incident with buttons to acknowledge the incident and mark it as resolved. PagerDuty nodes update the status of the incident. Jira nodes create an issue about the incident and update its status when it's resolved.