Workflow Templates

Imagine having an AI chatbot on Slack that seamlessly integrates with your company’s workflow, automating repetitive requests. No more digging through emails or documents to find answers about IT requests, company policies, or vacation days—just ask the bot, and it will instantly provide the right information. With its 24/7 availability, the chatbot ensures that team members get immediate support without waiting for a colleague to be online, making assistance faster and more efficient. Moreover, this AI-powered bot serves as a central hub for internal communication, allowing everyone to quickly access procedures, documents, and company knowledge without searching manually. A simple Slack message is all it takes to get the information you need, enhancing productivity and collaboration across teams. How It Works Slack Trigger: The workflow starts when a user mentions the AI bot in a Slack channel. The trigger captures the message and forwards it to the AI Agent. AI Agent Processing: The AI Agent, powered by Anthropic's Claude 3.7 Sonnet model, processes the query. It uses Retrieval-Augmented Generation (RAG) to fetch relevant information from the company’s internal knowledge base stored in Qdrant (a vector database). A Simple Memory buffer retains recent conversation context (last 10 messages) for continuity. Knowledge Retrieval: The RAG tool searches Qdrant’s vector store using OpenAI embeddings to find the most relevant document chunks (top 10 matches). Response Generation: The AI synthesizes the retrieved data into a concise, structured response (1-2 sentences for the answer, 2-3 supporting details, and a source citation). The response is formatted in Slack-friendly markdown (bullet points, blockquotes) and sent back to the user. Set Up Steps Prepare Qdrant Vector Database: Create a Qdrant collection via HTTP request (Create collection node). Optionally, refresh/clear the collection (Refresh collection node) before adding new documents. Load Company Documents: Fetch files from a Google Drive folder (Get folder → Download Files). Process documents: Split text into chunks (Token Splitter) and generate embeddings (Embeddings OpenAI2). Store embeddings in Qdrant (Qdrant Vector Store1). Configure Slack Bot: Create a Slack bot via Slack API with required permissions Add the bot to the desired Slack channel and note the channelId for the workflow. Deploy AI Components: Connect the AI Agent to Anthropic’s model, RAG tool, and memory buffer. Ensure OpenAI embeddings are configured for both RAG and document processing. Test & Activate: Use the manual trigger (When clicking ‘Test workflow’) to validate document ingestion. Activate the workflow to enable real-time Slack interactions. Need help customizing? Contact me for consulting and support or add me on Linkedin.

This workflow allows you to send position updates of the ISS every minute to a queue using the AWS SQS node. Cron node: The Cron node will trigger the workflow every minute. HTTP Request node: This node will make a GET request to the API https://api.wheretheiss.at/v1/satellites/25544/positions to fetch the position of the ISS. This information gets passed on to the next node in the workflow. Set node: We will use the Set node to ensure that only the data that we set in this node gets passed on to the next nodes in the workflow. AWS SQS: This node will send the data from the previous node to the iss-position queue. If you have created a queue with a different one, you can use that queue instead.

Who is this for? For developers and organizations that need to convert PPTX files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the PPTX file from the web. Converts the PPTX file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

🖼️ Image-to-Image AI Generator from Google Sheets with Google Drive Upload ✅ Use Case Automatically generate AI images from prompts listed in a Google Sheet, upload the images to Google Drive, and log the result back into the sheet. Uses the image-to-image-gpt API for fast, customizable generation. 💡 Problem It Solves Manual image generation workflows are inefficient and error-prone. Creative and content teams often have to: Manually paste prompts into image generation tools Save images locally Upload to Google Drive Paste the link back into tracking spreadsheets This automation removes all that friction—turning one spreadsheet into a complete image creation pipeline. 🌟 Benefits 🔁 Fully automated image generation 📤 Direct uploads to Google Drive 🧾 Image links and timestamps logged in Google Sheets ⚠️ Built-in error logging for API failures 🧩 Modular and easily extensible 📊 Keeps a historical log of successes and errors 🧩 Workflow Overview | Node | Description | |------|-------------| | 1. Manual Trigger | Starts the workflow when executed manually | | 2. Google Sheets2 | Reads all rows from the input Google Sheet | | 3. Loop Over Items | Processes one row (prompt) at a time | | 4. If2 | Filters only rows where Prompt is not empty and drive path is empty | | 5. HTTP Request1 | Calls the image-to-image-gpt API with the prompt | | 6. If1 (Error Handling) | If an error exists in the API response, route to logging | | 7. Google Sheets4 (Error Log) | Appends error details to a log sheet for review | | 8. Code1 | Decodes the base64 image returned by the API | | 9. Google Drive1 | Uploads the image to a selected Google Drive folder | | 10. Google Sheets1 (Write Back) | Updates the original row with the image drive path and timestamp | | 11. Wait | Delays the next prompt to prevent hitting API rate limits | 🛠 Tech Stack n8n** (no-code automation) Google Sheets** (data input/output) Google Drive** (image storage) image-to-image-gpt API via RapidAPI JavaScript (in Code node)** for base64 processing 📝 Sheet Format Your Google Sheet should include the following columns: | Column | Purpose | |----------------|----------------------------------| | Prompt | The AI prompt to send to the API | | Image url | (Optional) Initial image URL | | drive path | Updated with Drive link by flow | | Generated Date | Auto-filled by the workflow | | Base64 | Stores raw or error data | 🚀 How to Use Import this workflow into your n8n instance Set up Google Sheets and Google Drive service credentials Add your RapidAPI key in the HTTP Request node headers Use the image-to-image-gpt endpoint in the HTTP request Configure the Google Sheet and Drive folder in the respective nodes Execute manually or add a Cron node for scheduling 📌 Example Applications 🛍 eCommerce: Auto-generate product mockups 🧵 Fashion/Design: Visualize styles or fabrics from prompts ✍️ Blogging/Content: Auto-generate header images from titles 📣 Marketing: Generate ad banners from text 🧪 Tips You can add a Cron node if you want this to run on a schedule Use a separate tab/sheet for logging failed prompts Extend the flow by adding: Email notifications Slack alerts File name templating Create your free n8n account and set up the workflow in just a few minutes using the link below: 👉 Start Automating with n8n Save time, stay consistent, and grow your LinkedIn presence effortlessly!

What it does This workflow automates content syndication and posting to LinkedIn and X/Twitter. It takes existing long-form articles and generates from them engaging social posts optimized for each platform. The workflow takes links to existing articles from a given sitemap. It randomly selects the next article to republish, making sure that articles are not repeated. For simplicity it uses a Google spreadsheet to track the articles that have already been republished. Requirements Existing sitemap Google Gemini API key (or other model provider's key) Google Sheets credentials LinkedIn credentials X/Twitter credentials How to set up Adjust the schedule as needed (by default triggers daily at noon) Configure parameters in the parameters node: Set the sitemap URL (e.g. https://example.com/sitemap.xml) Set the language of the posts Enable/disable channels as needed Configure Google Sheets credentials for get processed articles and add processed articles nodes Create a new Google spreadsheet document with "url", "status", "timestamp", "LinkedIn post" and "Twitter post" columns Specify that spreadsheet document in get processed articles node (other nodes will take it from here) Add Google Gemini API key for the model (or change to any other model of choice) Configure LinkedIn and X/Twitter credentials for publishing

Workflow Overview This workflow automatically fetches the latest "Ask HN: Who is hiring?" posts from Hacker News, extracts individual job listings, cleans the raw text, converts them into structured job listings using Google Gemini AI, and saves them into Airtable. Components It’s a full end-to-end automation system combining: Algolia API** for HN data Text cleaning** Gemini AI (via LangChain)** for parsing job descriptions Structured JSON extraction** Airtable integration** to store the final data 🎯 Use Cases Automatically build a job board from HN posts Track startup hiring trends Feed remote job alerts into a CRM or Slack Enrich a hiring intelligence database 🔧 Nodes & Services Used HTTP Request (Algolia + Firebase API) SplitOut, Set, Filter, Function, Limit Google Gemini (via LangChain integration) Output Parser Structured Airtable (API token required) 📌 Credentials Required Google Gemini (PaLM/Gemini API) Airtable Personal Access Token Algolia Application ID & API Key (via Header Auth) 📦 Tags hacker-news, jobs, airtable, ai, gemini, automation, hn, langchain, workflow Screenshots

What does this template do? This workflow sets a small "lock" value in Redis so that only one copy of a long job can run at the same time. If another trigger fires while the job is still busy, the workflow sees the lock, stops early, and throws a clear error. This protects your data and keeps you from hitting rate limits. Because the workflow also stores simple progress flags ("working", "loading", "finishing"), you can poll the current status and show live progress for very long jobs. Use Case Great when the same workflow can be called many times in parallel (for example by webhooks, cron jobs, or nested Execute Workflow calls) and you need an "only run once at a time" guarantee without building a full queue system. What the Workflow Does ⚡ Starts through Execute Workflow Trigger called by another workflow 🔄 A Switch sends the run to Get, Set, or Unset actions 💾 Redis reads or writes a key named process_status_<key> with a time‑to‑live (default 600 s) 🚦 If nodes check the key and decide to continue or stop ⏱️ Wait nodes stand in for the slow part of your job (replace these with your real work) 📈 Updates the key with human‑readable progress values that another workflow can fetch with action = get 🏁 When done, the lock is removed so the next run can start Apps & Services Used Redis Core n8n nodes (Switch, If, Set, Wait, Stop and Error) Pre‑requisites A Redis server that n8n can reach Redis credentials stored in n8n A second workflow that calls this one and sends: action set to get, set, or unset key set to a unique name for the job Optional timeout in seconds Customization Tips Increase or decrease the TTL in the Set Timeout node to match how long your job usually runs Add or rename status values ("working", "loading", "finishing", and so on) to show finer progress Replace Stop and Error with a Slack or email alert, or even push the extra trigger into a queue if you prefer waiting instead of failing Use different Redis keys if you need separate locks for different tasks Build a small "status endpoint" workflow that calls this one with action = get to display real‑time progress to users Additional Use Cases 🛑 Telegram callback spam filter If a Telegram bot sends many identical callbacks in a burst, call this workflow first to place a lock. Only the first callback will proceed; the rest will exit cleanly until the lock clears. This keeps your bot from flooding downstream APIs. 🧩 External API rate‑limit protection Run heavy API syncs one after the other so parallel calls do not break vendor rate limits. 🔔 Maintenance window lock Block scheduled maintenance tasks from overlapping, making sure each window finishes before the next starts.

Automatically Send Weekly Sales Reports from Square via Outlook What It Does This workflow automatically connects to the Square API and generates a weekly sales summary report for all your Square locations. The report matches the figures displayed in Square Dashboard > Reports > Sales Summary. It's designed to run weekly and pull the previous week’s sales into a CSV file, which is then sent to a manager/finance team for analysis. This workflow builds on my previous template, which allows users to automatically pull data from the Square API into n8n for processing. (See here: https://n8n.io/workflows/6358) Prerequisites To use this workflow, you'll need: A Square API credential (configured as a Header Auth credential) A Microsoft Outlook credential How to Set Up Square Credentials: Go to Credentials > Create New Choose Header Auth Set the Name to Authorization Set the Value to your Square Access Token (e.g., Bearer <your-api-key>) How It Works Trigger: The workflow runs every Monday at 4:00 AM Fetch Locations: An HTTP request retrieves all Square locations linked to your account Fetch Orders: For each location, an HTTP request pulls completed orders for the previous week (e.g., Monday to Sunday) Filter Empty Locations: Locations with no sales are ignored Aggregate Sales Data: A Code node processes the order data and produces a summary identical to Square’s built-in Sales Summary report Create CSV File: A CSV file is created containing the relevant data Send Email: An email is sent via Microsoft Outlook to the chosen third party Example Use Cases Automatically send weekly Square sales data to management to improve the quality of planning and scheduling decisions Automatically send data to an external third party, such as a landlord or agent, who is paid via commission Automatically send data to a bookkeeper for entry into QuickBooks How to Use Configure both HTTP Request nodes to use your Square API credential Set the workflow to Active so it runs automatically Enter the email address of the person you want to send the report to and update the message body If you want to remove the n8n attribution, you can do so in the last node Customization Options Add pagination to handle locations with more than 1,000 orders per week Why It's Useful This workflow saves time, reduces manual report pulling from Square, and enables smarter automation around sales data — whether for operations, finance, or performance monitoring.

🎬 Veo3 Instagram Reel Generator – AI-Powered Ad Creation in Minutes Description: This no-code workflow transforms your creative brief into an engaging Instagram Reel using OpenAI and Veo3 API (via Wavespeed) — fully automated in n8n. Just type a product, theme, or trend via chat, and get a short-form video plus caption delivered and logged, ready to post. Perfect for marketers, creators, and content teams looking to scale their ad content output without hiring editors or creative agencies. Watch step-by-step build video tutorial here: https://www.youtube.com/@Automatewithmarc ⚙️ How It Works: 💬 Chat Trigger  Start by sending a message like “Create an ad for a minimalist perfume brand using the ‘quiet luxury’ trend.” 🧠 Prompt Engineer (ChatGPT)  Generates a 5–8 second descriptive video prompt suitable for Veo3 based on your input — including visual tone, motion, and hook. 📡 API Call to Veo3 via Wavespeed  Submits the prompt to create a short video (9:16 ratio, ~8 seconds), then polls for the final video URL. ✍️ Caption Generator (GPT)  Creates an Instagram-friendly caption to pair with the video, using a playful, impactful writing style. 📄 Google Sheets Integration  Logs each generated video prompt, final video URL, caption, and status into a Google Sheet for easy management and scheduling. 🔌 Tools & Integrations: OpenAI GPT (Prompt generation & caption copywriting) Veo3 via Wavespeed API (Video generation) Google Sheets (Content tracking and publishing queue) Telegram / Chat UI trigger (Optional – easily swappable) 💡 Use Cases: Instagram & TikTok ad generation Creative automation for digital agencies Short-form UGC testing at scale Trend-driven campaign ideation

Overview Do you often forget to record expenses? 你是不是會常常忘記紀錄花費? Let Spending Tracker Bot help you! 讓 Spending Tracker Bot 來幫你! This AI image/text Spending Tracker LINE Bot Workflow allows you to quickly create a customized spending tracker robot without writing a line of code. At any time, you can speak or send a photo, and the AI will parse it and automatically log the expense to your cloud ledger. 這套 AI 圖片文字記帳 LINE Bot Workflow ，讓你不用寫一行程式碼，就能快速打造一個量身訂製的記帳機器人。無論何時，只需要口述或發送一張照片，AI 就會幫你整理好自動計入雲端帳本 Preparation ① Enable the Google Sheets API in GCP and complete the OAuth setup ② Create the Google Sheet and populate the field names (Feel free to modify based on your own needs) ③ Configure the Webhook URL in the LINE Developers Console ④ OpenAI API Key ① 在 GCP 啟用 Google Sheets API，並完成 OAuth ② 建立並填好 Google Sheet 欄名 (按照自己的需求做更動） ③ 於 LINE Developers 控制台設定 Webhook URL ④ OpenAI API Key Node Configurations Webhook Purpose: The URL is used to receive incoming requests from LINE. Configuration: Paste this URL into the Webhook URL field in your LINE Developers Console. 用途: 要接收 Line 的 URL 設定: 將 URL 放到 Line Webhook URL Switch based on Expense Type & Set/Https Purpose: To distinguish whether the incoming message is text or an image. Configuration: Use a Switch node to route the flow accordingly. 用途: 區分 text 或 image 設定: switch 分流 AI Agent Purpose: To extract and organize the required fields. Configuration: Chat Model & Structured Output Parser. 用途: 整理出需要的欄位 設定: Chat Model & Structured Output Parser Create a deduplication field Purpose: To prevent duplicate entries by creating a unique "for_deduplication" field. Configuration: Join multiple field names using hyphens (-) as separators. 用途: 確保不會重複寫入，先創建一個"去重使用"欄位 設定: 用 - 連接多個欄位 Aggregrate & Merge_all Purpose: To prevent duplicate entries in the data table. Configuration: Read the Google sheet, extract the existing "for_deduplication" column into a dedupeList, and compare it against the newly generated "for_deduplication" value from the previous step. 用途: 防止重複寫入資料表 設定：讀取雲端表，將原本的"去重使用欄位"整理成dedupeList，與前一步整理好的"去重使用"欄位做比對 Response Switch Purpose: To route data and send appropriate replies based on the content. Configuration: Use the replyToken to respond after branching logic. Depending on the result, either write to the data table or return a message: ✅ Expense recorded successfully: <for_deduplication> Irrelevant details or images will not be logged. ⚠️ This entry has already been logged and will not be duplicated. 用途: 資料分流，回應訊息 設定：使用 replyToken ，資料分流後，寫入資料表或回應訊息 ✅ 記帳成功 : <去重使用欄位> 不相關明細或圖片，不會計入 ⚠️ 此筆資料已記錄過，不會重複記帳 Step by step teaching notes 【Auto Expense Tracker from LINE Messages with GPT-4 and Google Sheets】 【AI 圖片文字記帳 Line Bot，自動記帳寫入 Google Sheet】

Overview Transform your customer support operations with an intelligent WhatsApp automation system that handles text, voice, and image messages across multiple languages. This comprehensive solution uses advanced AI to provide instant, accurate responses by accessing your company's knowledge base, while maintaining conversation context and supporting both English and Roman Urdu communications. Perfect for businesses serving diverse markets who need 24/7 customer support without the overhead costs. Key Benefits 🤖 Multi-Modal AI Processing Handle text messages, voice notes, and images seamlessly. The system automatically transcribes audio, analyzes images, and processes all content types through a single intelligent pipeline. 🌍 True Multilingual Support Native support for English and Roman Urdu with intelligent language detection and matching responses. The AI automatically detects the customer's language and responds accordingly, making it perfect for South Asian markets. 📚 Dynamic Knowledge Base Integration Real-time access to your Google Docs knowledge base ensures customers always receive current, accurate information about your products and services. No more outdated responses or manual updates needed. 💭 Conversation Memory & Context Advanced memory system maintains conversation history for natural, contextual interactions. Customers can have flowing conversations without repeating information, improving satisfaction rates. ⚡ Instant Response Times Automated responses within seconds of receiving messages, dramatically improving customer satisfaction and reducing response time from hours to seconds. 🔄 Zero Manual Intervention Fully automated system that works 24/7 without human oversight. Handles inquiries, provides information, and maintains professional communication standards automatically. 📊 Scalable Architecture Built on enterprise-grade n8n platform with robust error handling and retry mechanisms. Can handle thousands of concurrent conversations without performance degradation. 💰 Cost-Effective Operations Replace expensive customer support teams with intelligent automation. Reduce operational costs by up to 80% while improving response quality and availability. How It Works Phase 1: Message Reception & Classification The system begins with a WhatsApp webhook trigger that captures all incoming messages in real-time. An intelligent switch node immediately analyzes each message to determine its content type - whether it's a text message, voice note, or image with optional caption. This classification is crucial as each media type requires different processing approaches to extract meaningful information. Phase 2: Advanced Media Processing For voice messages, the system retrieves the audio file URL, downloads the content using authenticated requests, and processes it through OpenAI's Whisper transcription service to convert speech to text. Image messages follow a similar pattern - the system downloads the image and uses GPT-4 Vision to analyze and describe the visual content in detail. Text messages are processed directly, while all media types are ultimately converted to standardized text format for consistent AI processing. Phase 3: Intelligent Response Generation The processed content is fed into a sophisticated AI agent powered by Claude Sonnet 4 via OpenRouter. This agent operates with a comprehensive system prompt that defines its role as a professional customer support representative with specific instructions for tone, language handling, and response protocols. The agent has access to a Google Docs tool that allows it to retrieve real-time information from your company's knowledge base. Phase 4: Contextual Memory Management A memory buffer system maintains conversation history for each unique phone number, allowing for natural, flowing conversations where the AI remembers previous interactions and can reference earlier parts of the conversation. This creates a more human-like experience and reduces customer frustration from having to repeat information. Phase 5: Response Delivery Generated responses are automatically sent back to the customer's WhatsApp number using the WhatsApp Business API, completing the conversation loop. The system maintains proper formatting and ensures message delivery confirmation. Required Setup & Database Requirements API Credentials Needed: WhatsApp Business API**: For receiving and sending messages OpenAI API**: For audio transcription and image analysis OpenRouter API**: For Claude Sonnet 4 language model access Google Docs API**: For knowledge base integration n8n Cloud/Self-hosted instance**: For workflow execution Knowledge Base Setup: Google Docs Document**: Structured company information document Document Permissions**: Shared with the Google service account Content Organization**: FAQ format with clear sections for products, services, pricing, and contact information WhatsApp Configuration: Business Phone Number**: Verified WhatsApp Business account Webhook URL**: Configured to point to n8n webhook endpoint Message Templates**: Pre-approved for business communications Business Use Cases E-commerce Support: Handle product inquiries, order status checks, and return policies across multiple languages, perfect for businesses serving diverse customer bases. Service Business Automation: Appointment scheduling, service explanations, and pricing inquiries for consultancies, agencies, and professional services. Restaurant & Food Industry: Menu inquiries, order modifications, and delivery status updates with support for local language preferences. Real Estate: Property inquiries, showing appointments, and market information with ability to process property images sent by clients. Healthcare & Wellness: Appointment booking, service explanations, and general inquiries while maintaining professional communication standards. Education & Training: Course information, enrollment processes, and student support with multilingual capabilities for international programs. Revenue Potential Direct Cost Savings: $3,000-8,000/month in customer support staff salaries Increased Conversion: 25-40% improvement in lead response rates due to instant replies Extended Availability: 24/7 operation captures international and after-hours inquiries worth $2,000-5,000/month additional revenue Scalability: Handle 10x more inquiries without proportional cost increases Customer Satisfaction: Improved response times lead to 15-30% increase in customer retention ROI Timeline: Typical payback period of 2-3 months with ongoing monthly savings of $4,000-12,000 depending on business size. Difficulty Level & Build Time Complexity: Intermediate to Advanced (7/10) Estimated Build Time: 4-6 hours for experienced n8n users Setup Time: 2-3 hours for API configurations and testing Maintenance: Minimal - primarily updating knowledge base content Skills Required: n8n workflow building experience API credential management WhatsApp Business API familiarity Basic understanding of AI language models Detailed Setup Steps 1. API Account Setup (60 minutes) Create and configure accounts for WhatsApp Business, OpenAI, OpenRouter, and Google Cloud Platform. Obtain all necessary API keys and configure proper permissions for each service. 2. n8n Credential Configuration (30 minutes) Add all API credentials to your n8n instance using the credential manager. Test each connection to ensure proper authentication and access permissions. 3. WhatsApp Business Integration (45 minutes) Configure your WhatsApp Business account with webhook URLs pointing to your n8n instance. Set up phone number verification and message template approvals. 4. Knowledge Base Creation (90 minutes) Structure your Google Docs knowledge base with comprehensive information about your business. Include FAQs, product details, pricing, and contact information in an organized format. 5. Workflow Import & Testing (60 minutes) Import the n8n workflow, configure all node parameters with your specific credentials and settings, then conduct thorough testing with different message types and languages. 6. Production Deployment (30 minutes) Deploy the workflow to production, monitor initial performance, and fine-tune system prompts based on real customer interactions. Advanced Customization Options Custom Language Support: Extend beyond English and Roman Urdu by modifying the system prompt and adding language detection for additional languages like Arabic, Hindi, or French. Integration Expansions: Connect additional data sources like CRM systems, databases, or e-commerce platforms to provide more comprehensive customer information. Advanced Analytics: Add logging nodes to track conversation metrics, response times, and customer satisfaction scores for continuous improvement. Multi-Channel Support: Extend the system to handle Telegram, Facebook Messenger, or other messaging platforms using similar processing logic. Escalation Protocols: Implement human handoff triggers for complex queries that require personal attention, with automatic notification systems for support teams. Custom AI Models: Swap Claude Sonnet 4 for other models like GPT-4, Gemini, or open-source alternatives based on your specific needs and budget requirements. This automation system represents the future of customer support - intelligent, scalable, and incredibly cost-effective while maintaining the personal touch that customers expect from quality businesses.

This automated n8n workflow detects and manages fraudulent booking transactions through comprehensive AI-powered analysis and multi-layered security checks. The system processes incoming travel booking data, performs IP geolocation verification, enriches transaction details with AI insights, calculates dynamic risk scores, and executes automated responses based on threat levels. All transactions are logged and appropriate notifications are sent to relevant stakeholders. Good to Know The workflow combines multiple detection methods, including IP geolocation, AI analysis, and risk scoring algorithms Google Gemini Chat Model provides advanced natural language processing for transaction analysis Risk levels are dynamically calculated and categorized as CRITICAL, HIGH, or standard risk Automated blocking and flagging system protects against fraudulent transactions in real-time All transaction data is logged to Google Sheets for audit trails and pattern analysis The system respects API rate limits and includes proper error handling mechanisms How It Works 1. Initial Data Ingestion & Extraction Monitors and captures incoming booking transaction data from various sources Extracts key booking details including user information, payment data, booking location, and transaction metadata Performs initial data validation and formatting for downstream processing 2. IP Geolocation and AI Analysis IP Geolocation Check**: Validates booking IP addresses by checking geolocation details and comparing against expected user locations AI Agent Integration**: Utilizes Google Gemini Chat Model to analyze booking patterns, user behavior, and transaction anomalies Enhanced Data Processing**: Enriches transaction data with geographical context and AI-driven risk indicators 3. Risk Calculation and Decision Logic Enhanced Risk Calculator**: Combines AI-generated risk scores with geolocation-based factors, payment method analysis, and historical patterns Critical Risk Check**: Flags transactions with risk levels marked as CRITICAL for immediate action High Risk Check**: Identifies HIGH risk transactions requiring additional verification steps Dynamic Scoring**: Adjusts risk calculations based on real-time threat intelligence and pattern recognition 4. Action & Notification Block User Account**: Automatically blocks user accounts for CRITICAL risk transactions to prevent immediate fraud Flag for Review**: Marks HIGH risk transactions for manual review by fraud prevention teams Send Notifications**: Dispatches real-time alerts via email and messaging systems to security teams Automated Responses**: Sends appropriate messages to users based on transaction status and risk level 5. Logging & Response Log to Google Sheets**: Records all transaction details, risk scores, and actions taken for comprehensive audit trails Flag for Review**: Maintains detailed logs of flagged transactions for pattern analysis and machine learning improvements Response Tracking**: Monitors and logs all automated responses and manual interventions How to Use Import the workflow into your n8n instance Configure Google Gemini Chat Model API credentials for AI analysis Set up IP geolocation service API access for location verification Configure Google Sheets integration for transaction logging Establish Gmail/email credentials for notification delivery Define risk thresholds and scoring parameters based on your fraud tolerance levels Test the workflow with sample booking data to verify all components function correctly Monitor initial deployments closely to fine-tune risk scoring algorithms Establish manual review processes for flagged transactions Set up regular monitoring and maintenance schedules for optimal performance Requirements Google Gemini Chat Model API access IP Geolocation service API credentials Google Sheets API integration Gmail API or SMTP email service for notifications n8n instance with appropriate node modules installed Customizing This Workflow Risk Scoring Parameters**: Adjust risk calculation algorithms and thresholds based on your specific fraud patterns and business requirements AI Model Configuration**: Fine-tune Google Gemini prompts and analysis parameters for improved accuracy in your use case Notification Channels**: Add or modify notification methods including Slack, SMS, or webhook integrations Data Sources**: Extend input methods to accommodate additional booking platforms or payment processors Logging Destinations**: Configure alternative or additional logging systems such as databases or external SIEM platforms Geographic Rules**: Customize geolocation validation rules based on your service areas and customer base Automated Actions**: Modify or expand automated response actions based on your fraud prevention policies Review Workflows**: Integrate with existing fraud review systems or ticketing platforms for seamless manual review processes