Workflow Templates

Who this is for? Extract & Summarize Yelp Business Review is an automated workflow that extracts the Yelp business reviews using Bright Data Web Unlocker, process and formats the raw data, summarizes using the Google Gemini's LLM, and forward the concise summary with the review respose to a specified webhook endpoint. This workflow is tailored for: Local SEO Specialists who need structured insights from Yelp reviews to optimize listings. Business Owners wanting quick summaries of what customers love or complain about. Reputation Managers who monitor brand sentiment and identify customer pain points. Data Analysts & Researchers extracting Yelp review patterns at scale. AI Product Builders needing clean Yelp review data as input for their LLMs or recommender systems. What problem is this workflow solving? Yelp reviews are rich in customer sentiment but messy to work with manually. This workflow solves: The pain of scraping Yelp review content manually. The challenge of building the structured data with the summary. The need for structured outputs suitable for analysis, reports, or AI input. What this workflow does This automated pipeline does the following: Bright Data Integration**: Queries Yelp and scrapes business listing data using Bright Data's Web Unlocker. Structured Data Formatting**: Formats the Yelp review data to a structured response in JSON format. Google Gemini Summarization**: Sends the cleaned reviews to Google Gemini to: Output Delivery**: Returns the structured response with the concise summary over the webhook endpoint. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Yelp Business Review URL with the Bright Data zone by navigating to the Set Yelp URL with the Bright Data Zone node. Update the Webhook Notifier for the merged response node with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you’re a market researcher, entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: Target Specific Business Categories** Update the Yelp Business Review input to scrape different businesses like gyms, salons etc. Limit Reviews** Add filters by description, location, page range to get the top reviews. Tweak the Data Extraction Node** Update the Structured Data Extractor node Output Parser for building the JSON response with the appropriate fields or attributes. Tweak the Summarization Prompt** Modify the Gemini prompt to generate a comprehensive summary. Send Output to Other Destinations** Replace the Webhook URL to forward output to: Google Sheets Airtable Slack or Discord Custom API endpoints

Set Up ElevenLabs Voice Chat Agent using Graph RAG Knowledge Graphs as Experts This workflow creates an AI voice chatbot agent that has access to several knowledge bases at the same time (used as "experts"). These knowledge bases are provided using the InfraNodus GraphRAG using the knowledge graphs and providing high-quality responses without the need to set up complex RAG vector store workflows. We use ElevenLabs to set up a voice agent that can be embedded to any website or used via their API. The advantages of using GraphRAG instead of the standard vector stores for knowledge are: Easy and quick to set up (no complex data import workflows needed) and to update with new knowledge A knowledge graph has a holistic overview of your knowledge base Better retrieval of relations between the document chunks = higher quality responses Ability to reuse in other n8n workflows How it works This template uses the n8n AI agent node as an orchestrating agent that decides which tool (knowledge graph) to use based on the user's prompt. The user's prompt is received from the ElevenLabs Conversational AI agent via an n8n Webhook, which also takes care of the voice interaction. The response from n8n is then sent to the Webhook, which is polled by the ElevenLabs voice agent. This agent processes the response and provides the final answer. Here's a description step by step: The user submits a question using ElevenLabs voice interface The question is sent via the knowledge_base tool in ElevenLabs to the n8n Webhook with the POST request containing the user's prompt and sessionID for Chat Memory node in n8n. The n8n AI agent node checks a list of tools it has access to. Each tool has a description of the knowledge auto-generated by InfraNodus (we call each tool an "expert"). The n8n AI agent decides which tool should be used to generate a response. It may reformulate user's query to be more suitable for the expert. The query is then sent to the InfraNodus HTTP node endpoint, which will query the graph that corresponds to that expert. Each InfraNodus GraphRAG expert provides a rich response that takes the whole context into account and provides a response from each expert (graph) along with a list of relevant statements retrieved using a combination or RAG and GraphRAG. The n8n AI Agent node integrates the responses received from the experts to produce the final answer. The final answer is sent back to the Webhook endpoint ElevenLabs conversational AI agent picks up the response arriving from the knowledge_base tool via the webhook and then condenses it for conversational format and transforms text into voice. How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Create a separate knowledge graph for each expert (using PDF / content import options) in InfraNodus For each graph, go to the workflow, paste the name of the graph into the body name field. Keep other settings intact or learn more about them at the InfraNodus access points page. Once you add one or more graphs as experts to your flow, add the LLM key to the OpenAI node and launch the workflow You will also need to set up an ElevenLabs account and to set up a conversational AI agent there. See the Post note in the n8n workflow for a complete step-by-step description or our support article on setting up ElevenLabs AI voice agent Once the voice AI agent is ready, you might want to combine it with a text AI chatbot workflow so your users have a choice between the text and voice interaction. In that case, you may be interested to use our free open-source website popup chat widget popupchat.dev where you can create an embed code to add to your blog or website and allow the user to choose between the text and voice interaction. Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key An ElevenLabs account FAQ 1. How many "experts" should I aim for? We recommend to aim for the number of experts as the optimal number of people in a team, which is usually 2-7. If you add more experts, your AI orchestrating agent will have troubles choosing the most suitable "expert" tool for the user's query. You can mitigate this by specifying in the AI agent description that it can choose maximum 3-7 experts to provide a response. 2. Why use InfraNodus GraphRAG and not standard vector store for knowledge? First, vector stores are complex to set up and to update. You'd need a separate workflow for that, decide on the vector dimensions, add metadata to your knowledge, etc. With InfraNodus, you have a complete RAG / GraphRAG solution under the hood that is easy to set up and provides high-quality responses that takes the overall structure and the relations between your ideas into account. 3 Why not use ElevenLabs' own knowledge? One of the reasons is that you want your knowledge base to be in one place so you can reuse it in other n8n workflows. Another reason is that you will not have such a good separation between the "experts" when you converse with the agent. So the answers you get will be based on top matches from all the books / articles you upload, while with the InfraNodus GraphRAG setup you can better control which graphs are consulted as experts and have an explicit way to display this data. Customizing this workflow You can use this same workflow with a Telegram bot, so you can interact with it using Telegram. There are many more customizations available on our GitHub repo for n8n workflows. Check out the complete setup guide for this workflow at https://support.noduslabs.com/hc/en-us/articles/20318967066396-How-to-Build-a-Text-Voice-AI-Agent-Chatbot-with-n8n-Elevenlabs-and-InfraNodus Also check out the video tutorial with a demo:

Monitor BeyondPresence video agent conversations in real-time to automatically score leads (0-100+) based on buying signals and send instant Slack alerts when hot opportunities or competitors are mentioned. This template helps sales teams prioritize leads immediately, never miss competitor mentions, and respond to high-intent prospects while they're still engaged. How it works Real-time webhook** processes each user message as it happens during calls Scoring engine** analyzes for buying signals (+points) and objections (-points) Competitor detection** instantly identifies when alternatives are mentioned Smart routing** sends alerts to different Slack channels based on urgency Hot leads** (70+ score) trigger immediate notifications with recommendations Call summary (Optional)** provides final qualification score when conversation ends Set up steps Connect Slack OAuth2 - Use n8n's built-in Slack integration (no webhooks needed!) Create Slack channels - Set up #sales-hot-leads, #sales-competitors, #sales-qualified Add webhook to BeyondPresence - Copy URL from n8n to BeyondPresence Settings → Webhooks Customize competitors - Edit the scoring node to add your specific competitor names Adjust scoring weights (optional) - Tune point values for your sales process Setup time: 10-15 minutes Requirements: BeyondPresence account, Slack workspace admin access

This n8n template demonstrates how to build a simple but effective vintage image restoration service using an AI model with image editing capabilities. With Gemini now capable of multimodal output, it's a great time to explore this capability for image or graphics automation. Let's see how well it does for a task such as image restoration. Good to know At time of writing, each image generated will cost $0.039 USD. See Gemini Pricing for updated info. The model used in this workflow is geo-restricted! If it says model not found, it may not be available in your country or region. How it works Images are imported into our workflow via the HTTP node and converted to base64 strings using the Extract from file node. The image data is then pipelined to Gemini's Image Generation model. A prompt is provided to instruct Gemini to "restore" the image to near new condition - of course, feel free to experiment with this prompt to improve the results! Gemini's responds with the image as a base64 string and hence, a convert to file node is used to transform the data to binary. With the restored image as a binary, we can then use this with our Google Drive node to upload it to our desired folder. How to use This demonstration uses 3 random images sourced from the internet but any typical image file will work. Use a webhook node to allow integration from other applications. Use a telegram trigger for instant mobile service! Requirements Google Gemini for LLM/Image generation Google Drive for Upload Storage Customising this workflow AI image editing can be applied to many use-cases not just image restoration. Try using it to add watermarks, branding or modify an existing image for marketing purposes.

Enhance Security Operations with the Qualys Slack Shortcut Bot! Our Qualys Slack Shortcut Bot is strategically designed to facilitate immediate security operations directly from Slack. This powerful tool allows users to initiate vulnerability scans and generate detailed reports through simple Slack interactions, streamlining the process of managing security assessments. Workflow Highlights: Interactive Modals**: Utilizes Slack modals to gather user inputs for scan configurations and report generation, providing a user-friendly interface for complex operations. Dynamic Workflow Execution**: Integrates seamlessly with Qualys to execute vulnerability scans and create reports based on user-specified parameters. Real-Time Feedback**: Offers instant feedback within Slack, updating users about the status of their requests and delivering reports directly through Slack channels. Operational Flow: Parse Webhook Data**: Captures and parses incoming data from Slack to understand user commands accurately. Execute Actions**: Depending on the user's selection, the workflow triggers other sub-workflows like 'Qualys Start Vulnerability Scan' or 'Qualys Create Report' for detailed processing. Respond to Slack**: Ensures that every interaction is acknowledged, maintaining a smooth user experience by managing modal popups and sending appropriate responses. Setup Instructions: Verify that Slack and Qualys API integrations are correctly configured for seamless interaction. Customize the modal interfaces to align with your organization's operational protocols and security policies. Test the workflow to ensure that it responds accurately to Slack commands and that the integration with Qualys is functioning as expected. Need Assistance? Explore our Documentation or get help from the n8n Community for more detailed guidance on setup and customization. Deploy this bot within your Slack environment to significantly enhance the efficiency and responsiveness of your security operations, enabling proactive management of vulnerabilities and streamlined reporting. To handle the actual processing of requests, you will also need to deploy these two subworkflows: Qualys Start Vulnerability Scan Qualys Create Report To simplify deployment, use this Slack App manifest to quickly create an app with the correct permissions: { "display_information": { "name": "Qualys n8n Bot", "description": "n8n Integration for Qualys", "background_color": "#2a2b2e" }, "features": { "bot_user": { "display_name": "Qualys n8n Bot", "always_online": false }, "shortcuts": [ { "name": "Scan Report Generator", "type": "global", "callback_id": "qualys-scan-report", "description": "Generate a report from the latest scan to review vulnerabilities and compliance." }, { "name": "Launch Qualsys VM Scan", "type": "global", "callback_id": "trigger-qualys-vmscan", "description": "Start a Qualys Vulnerability scan from the comfort of your Slack Workspace" } ] }, "oauth_config": { "scopes": { "bot": [ "commands", "channels:join", "channels:history", "channels:read", "chat:write", "chat:write.customize", "files:read", "files:write" ] } }, "settings": { "interactivity": { "is_enabled": true, "request_url": "Replace everything inside the double quotes with your workflow webhook url, for example: https://n8n.domain.com/webhook/99db3e73-57d8-4107-ab02-5b7e713894ad"", "message_menu_options_url": "Replace everything inside the double quotes with your workflow message options webhook url, for example: https://n8n.domain.com/webhook/99db3e73-57d8-4107-ab02-5b7e713894ad"" }, "org_deploy_enabled": false, "socket_mode_enabled": false, "token_rotation_enabled": false } }

Video Guide I prepared a detailed guide that showed the whole process of building a call analyzer. .png) Who is this for? This workflow is ideal for sales teams, customer support managers, and online education services that conduct follow-up calls with clients. It’s designed for those who want to leverage AI to gain deeper insights into client needs and upsell opportunities from recorded calls. What problem does this workflow solve? Many follow-up sales calls lack structured analysis, making it challenging to identify client needs, gauge interest levels, or uncover upsell opportunities. This workflow enables automated call transcription and AI-driven analysis to generate actionable insights, helping teams improve sales performance, refine client communication, and streamline upselling strategies. What this workflow does This workflow transcribes and analyzes sales calls using AssemblyAI, OpenAI, and Supabase to store structured data. The workflow processes recorded calls as follows: Transcribe Call with AssemblyAI: Converts audio into text with speaker labels for clarity. Analyze Transcription with OpenAI: Using a predefined JSON schema, OpenAI analyzes the transcription to extract metrics like client intent, interest score, upsell opportunities, and more. Store and Access Results in Supabase: Stores both transcription and analysis data in a Supabase database for further use and display in interfaces. Setup Preparation Create Accounts: Set up accounts for N8N, Supabase, AssemblyAI, and OpenAI. Get Call Link: Upload audio files to public Supabase storage or Dropbox to generate a direct link for transcription. Prepare Artifacts for OpenAI: Define Metrics: Identify business metrics you want to track from call analysis, such as client needs, interest score, and upsell potential. Generate JSON Schema: Use GPT to design a JSON schema for structuring OpenAI’s responses, enabling efficient storage, analysis, and display. Create Analysis Prompt: Write a detailed prompt for GPT to analyze calls based on your metrics and JSON schema. Scenario 1: Transcribe Call with AssemblyAI Set Up Request: Header Authentication: Set Authorization with AssemblyAI API key. URL: POST to https://api.assemblyai.com/v2/transcript/. Parameters: audio_url: Direct URL of the audio file. webhook_url: URL for an N8N webhook to receive the transcription result. Additional Settings: speaker_labels (true/false): Enables speaker diarization. speakers_expected: Specify expected number of speakers. language_code: Set language (default: en_us). Scenario 2: Process Transcription with OpenAI Webhook Configuration: Set up a POST webhook to receive AssemblyAI’s transcription data. Get Transcription: Header Authentication: Set Authorization with AssemblyAI API key. URL: GET https://api.assemblyai.com/v2/transcript/<transcript_id>. Send to OpenAI: URL: POST to https://api.openai.com/v1/chat/completions. Header Authentication: Set Authorization with OpenAI API key. Body Parameters: Model: Use gpt-4o-2024-08-06 for JSON Schema support, or gpt-4o-mini for a less costly option. Messages: system: Contains the main analysis prompt. user: Combined speakers’ utterances to analyze in text format. Response Format: type: json_schema. json_schema: JSON schema for structured responses. Save Results in Supabase: Operation: Create a new record. Table Name: demo_calls. Fields: Input: Transcription text, audio URL, and transcription ID. Output: Parsed JSON response from OpenAI’s analysis.

This n8n template demonstrates how to use AI to score the all Resumes by matching it with Job profile Problem Statement: A Hr person is flooded with resume and spends hours manually checking each to find most suitable ones. How it works It is linked to Gmail Trigger which upon receving any mail with specific subject will check for the attachment. Attachment will be parsed to understand the resume Candidate informtion will be broken into Personal, Eductional and Professional type Job profile will be pulled from Notion Board A HR expert powered by Gemini LLM will score each profile on basis on its relevancy Information will be updated back to Gsheet Message lable will be updated back for clarity How to use The gmail trigger node is used as an example but feel free to replace this with other triggers such as webhook or even a form. Requirements Gemini account for LLM Google sheet for upload Gmail as trigger Llama parse credentials

Generate new keywords for SEO with the monthly Search volumes This workflow is an improvement on the workflows below. It can be used to generate new keywords that you can use for your SEO campaigns or Google ads campaigns Generate SEO Keyword Search Volume Data using Google API and Generating Keywords using Google Autosuggest Usage Send the keywords you need as an array to this workflow Pin the data and map it to the set Keywords node Map the keywords to the Google ads API with the location and Language of your choice Split the results and set them data Pass this to the next nodes as needed for storage Make a copy of this spreedsheet and update the data accordingly Having challenges with the google Ads API? Read this blog Setup Replace the trigger with your desired trigger eg a webhook or manual trigger Map the data correctly to the set Keywords node On the Generate new keywords, Update the {customer_id} on the url and login-customer-id with your actual one. Update the developer-token` also with your values. The url should be corrected as below https://googleads.googleapis.com/v18/customers/{customer-id}:generateKeywordIdeas You should send the headers as below { "name": "content-type", "value": "application/json" }, { "name": "developer-token", "value": "5j-tyzivCNmiCcoW-xkaxw" }, { "name": "login-customer-id", "value": "513554 " } and the json body should take the following format { "geoTargetConstants": ["geoTargetConstants/2840"], "includeAdultKeywords": false, "pageToken": "", "pageSize": 2, "keywordPlanNetwork": "GOOGLE_SEARCH", "language": "languageConstants/1000", "keywordSeed": { "keywords": {{ $json.Keyword }} } } Troubleshooting If you get an error with the workflow, check the credentials you are using Check the account you are using eg the right customer id and developer token Follow the guide on the blog to set up your Google ads account Made by @Imperol

Transform your BeyondPresence video agent conversations into comprehensive insights by automatically analyzing each call with AI and organizing 35+ data points in Google Sheets. This template helps customer success, support, and training teams save 30+ minutes per call on documentation while ensuring no critical action items or insights are missed. How it works Webhook receives** completed call data from BeyondPresence including full transcript Data validation** ensures quality and adds enriched metadata (duration, time calculations) AI analysis** (GPT-4) extracts action items, sentiment, decisions, and recommendations Parse response** handles the AI output and structures it for sheets Auto-append** to Google Sheets with 35+ insights per call organized beautifully Set up steps Copy our Google Sheets template - One click! Get pre-formatted sheet: BeyondPresence Call Analytics Template Connect accounts - Add OpenAI API key and Google Sheets OAuth2 Configure webhook - Copy URL from n8n to BeyondPresence Settings → Webhooks Customize AI prompt (optional) - Adjust analysis focus for your use case Test with a call - Make a test call and watch insights appear! Setup time: 5-10 minutes Requirements: BeyondPresence account, OpenAI API key, Google account

A medium-term trend analyzer for the Binance Spot Market that leverages core technical indicators across 4-hour candle data to provide human-readable swing-trade signals via AI. 🎥 Watch Tutorial: 🎯 What It Does Accepts a Binance trading pair (e.g., AVAXUSDT) Sends the symbol to an internal webhook for technical indicator calculation Computes 4h RSI, MACD, Bollinger Bands, SMA, EMA, ADX Returns structured, GPT-analyzed signals ready for Telegram delivery 🧠 AI Agent Details Model:** GPT-4.1-mini (OpenAI Chat) Agent Role:** Translates raw indicator values into sentiment-labeled signals Memory:** Tracks session + symbol context for cleaner multi-turn logic 🔗 Required Backend Workflow To calculate indicators, this tool depends on: POST https://treasurium.app.n8n.cloud/webhook/4h-indicators { "symbol": "AVAXUSDT" } Returns a JSON object with the latest 40×4h candle-based calculations. 📥 Input Format { "message": "AVAXUSDT", "sessionId": "telegram_chat_id" } 📊 Sample Output 🕓 4h Technical Signals – AVAXUSDT • RSI: 64 → Slightly Bullish • MACD: Bullish Cross above baseline • BB: Upper band touch – volatility expanding • EMA > SMA → Confirmed Upside Momentum • ADX: 31 → Strengthening Trend 📚 Use Case Scenarios | Use Case | Result | | ----------------------------- | ---------------------------------------------------- | | Swing trend confirmation | Uses 4h indicators to validate or reject setups | | Breakout signal confluence | Helps assess if momentum is real or noise | | Inputs to Quant AI or Analyst | Supports higher-frame trade recommendation synthesis | 🛠️ Setup Instructions Import the JSON template into your n8n workspace. Set your OpenAI API credentials for the GPT node. Ensure the /webhook/4h-indicators backend tool is live and accessible. Connect this to your Binance Financial Analyst Tool or master Quant AI orchestrator. 🤖 Parent Workflows That Use This Tool Binance SM Financial Analyst Tool Binance Spot Market Quant AI Agent 📎 Sticky Notes & Annotations This workflow includes internal sticky notes describing: Node roles (GPT, webhook, memory) System behavior (reasoning agent logic) Telegram formatting guidance 🔐 Licensing & Attribution © 2025 Treasurium Capital Limited Company All architecture, prompt logic, and signal formatting are proprietary. Redistribution or rebranding is prohibited. 🔗 Connect with the creator: Don Jayamaha – LinkedIn

Zoho CRM - Smart Meeting Scheduler This workflow automatically schedules meetings for new Zoho CRM leads by detecting their timezone, checking the sales rep’s Google Calendar, generating conflict-free time slots, creating a Zoom meeting and sending a personalized AI-generated email to the lead. If no slots are available, it sends a fallback message to the lead without updating Zoho CRM. When a meeting is created, all details are logged inside Zoho CRM for visibility. ⚡ Quick Implementation Steps (Fast Start Guide) Import the workflow JSON into n8n. Configure Zoho CRM, Google Calendar, Gmail, Zoom OAuth and Gemini AI credentials. Update meeting duration, working hours, buffer time and search window. Set email recipient to the lead’s email instead of test/static values. Add the webhook URL to Zoho CRM → Automation → Webhooks. Test with a new lead and activate the workflow. 📘 What It Does This workflow automates scheduling for new Zoho CRM leads. As soon as a lead is created, it retrieves full lead and owner details, detects the lead’s timezone and checks the assigned sales rep’s upcoming Google Calendar events. This helps identify when the rep is available. Using your settings—working hours, meeting duration, buffer before/after and days to evaluate—the system generates valid meeting time slots with no conflicts. If suitable slots exist, it authenticates with Zoom and creates a meeting for the earliest option, then generates a polished HTML invitation using Gemini AI and emails it to the lead. This ensures a fast, smart and personalized lead engagement process. If no slots exist, the workflow sends a fallback email informing the lead that no availability is open in the next few days. In this branch, Zoho CRM is not updated, because no meeting was scheduled. 🎯 Who’s It For This workflow is perfect for: Sales teams managing high inbound volume CRM managers automating lead qualification & engagement SaaS companies scheduling demos automatically Agencies booking consultation calls Any team struggling with timezone-based scheduling manually 🔧 Requirements to Use This Workflow Platform Requirements n8n (Cloud or self-hosted) Required Integrations Zoho CRM OAuth2 Google Calendar OAuth2 Gmail OAuth2 Zoom OAuth (account-level) Gemini AI / Google PaLM API Required Lead Fields Email (mandatory for sending the invite) Country / State (for timezone detection) Lead Owner (to fetch rep details) 🔄 How It Works Zoho CRM Webhook triggers when a new lead is created. Workflow fetches full lead and owner details. Detects the lead’s timezone using country/state mapping. Fetches the sales rep’s availability from Google Calendar. Generates valid time slots based on working hours, buffers and meeting duration. If slots exist: Authenticate with Zoom Create a Zoom meeting Generate personalized HTML invite using Gemini AI Send email to the lead Log meeting details in Zoho CRM If no slots exist: Generate fallback message Send fallback email to the lead (Zoho CRM is NOT updated in this path) 🛠️ Setup Steps (Configuration Guide) 1. Import Workflow Go to: n8n → Workflows → Import and upload the JSON file. 2. Add Required Credentials Configure the following inside n8n: Zoho CRM OAuth Google Calendar OAuth Gmail OAuth Zoom OAuth Gemini AI API key 3. Update Workflow Configuration Node Set: Meeting duration Buffer before/after Working hours Days to look ahead Default meeting provider (Zoom) 4. Fix Email Recipient In Send Meeting Invite node, set: sendTo = {{$('Detect Lead Timezone').item.json.Email}} yaml Copy code 5. Update Google Calendar Email/ID Ensure the calendar ID matches the sales rep’s Google Calendar. 6. Add Webhook in Zoho CRM Navigate to: Setup → Automation → Webhooks → Create Webhook → Lead Created Paste the webhook URL from n8n. 7. Test the Automation Verify: Correct timezone detection Calendar availability check Zoom meeting creation AI email sent to the lead Zoho CRM updated only when meeting is created 8. Activate Workflow Enable the workflow for live operation. 🧩 How To Customize Nodes 1. Adjust Meeting Logic Modify the Workflow Configuration node to change: Slot duration Buffer time Working hour ranges Days to consider 2. Expand Timezone Detection Edit the Detect Lead Timezone node to add new countries/states. 3. Personalize Email Content Update the prompt inside the Generate Personalized Invite node. 4. Add New Regions Duplicate timezone logic for new regions (Australia, Middle East, etc.) 5. Replace Zoom Swap Zoom with Google Meet, Microsoft Teams or Zoho Meeting. ➕ Add-Ons (Optional Enhancements) Auto-book calendar events when lead confirms a slot WhatsApp notifications via Twilio or Gupshup Slack/Email internal alerts for reps Follow-up reminder emails Log lead activity to Google Sheets Attach downloadable ICS calendar file 💼 Use Case Examples SaaS demo scheduling Consultation & discovery calls Global timezone-based sales teams Onboarding/support calls Event follow-up scheduling (And many more…) 🩻 Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|----------| | Lead not receiving email | Gmail OAuth expired / wrong email field | Reconnect Gmail OAuth & fix sendTo value | | Wrong time slots | Incorrect timezone detection | Update mapping in Detect Lead Timezone | | Zoom meeting not created | Invalid/expired Zoom OAuth | Reconnect Zoom credentials | | CRM not updated after fallback email | Expected behavior | No CRM update when slots don’t exist | | Workflow not triggering | Missing Zoho webhook | Re-add webhook | | Empty AI email | Gemini key incorrect | Reconfigure Gemini credentials | 🤝 Need Help? If you want assistance setting up, customizing or extending this workflow, the n8n automation team at WeblineIndia is here to help. We specialize in: Advanced automation workflows Multi-timezone scheduling systems CRM-integrated AI communication Custom Zoho + n8n development End-to-end automation architecture 👉 Contact WeblineIndia for expert workflow development and enhancements.

One-Line Description Automatically detects missed Zoom demos booked via Calendly and triggers AI-powered follow-up sequences. Detailed Description What it does: When a prospect books a demo through Calendly but fails to join the Zoom meeting, this workflow automatically detects the no-show, generates personalized recovery messages using AI, updates your database, and notifies your sales team—all within minutes of the meeting ending. It bridges Calendly, Zoom, and your follow-up channels to ensure no lead falls through the cracks. Who it's for: Sales teams** running high-volume demo calendars who lose 20-40% of booked meetings to no-shows Customer success managers** conducting onboarding calls where attendance tracking matters SDRs and BDRs** who need immediate alerts when prospects miss scheduled meetings Revenue operations teams** seeking to improve demo-to-opportunity conversion rates through faster follow-up Key Features: Real-time no-show detection** - Automatically checks Zoom participant lists against expected attendees within seconds of meeting end AI-generated recovery messaging** - Creates contextual, empathetic follow-up emails and LinkedIn messages tailored to each no-show scenario Instant team notifications** - Sends formatted Slack alerts with attendee details and suggested next actions so reps can manually follow up if needed Attendance tracking database** - Maintains a searchable record of all bookings and attendance status for reporting and analysis Multi-channel follow-up orchestration** - Coordinates email, Slack notifications, and optional CRM updates from a single automation Selective event filtering** - Processes only specific Calendly event types so you control which meetings trigger the workflow How it works: Booking capture: Calendly webhook fires when a demo is scheduled, extracting Zoom meeting details and attendee information Meeting monitoring: When the Zoom meeting ends, a second webhook triggers attendance verification by pulling the participant list from Zoom's API No-show identification: Workflow cross-references the expected attendee email with actual Zoom participants to confirm whether they attended Automated response: For confirmed no-shows, AI generates personalized recovery messages while the system updates your database and notifies your team via Slack Optional integrations: Simultaneously updates CRM deal stages or triggers additional follow-up sequences based on your configuration Setup Requirements Prerequisites: Calendly account** (any paid plan) with webhook access and Personal Access Token Zoom account** (Pro or higher) with Server-to-Server OAuth app credentials for API access OpenAI API key** for AI-generated follow-up message creation Slack workspace** with OAuth permissions to post messages (optional but recommended) n8n Data Table** created with columns: meeting_id, email, status (built-in n8n feature, no external database needed) Email sending service** configured in n8n (SMTP, Gmail, SendGrid, etc.) if enabling automated email sending CRM API access** (HubSpot, Salesforce, Pipedrive, etc.) if enabling deal updates (optional) Note: Zoom API has rate limits (varies by plan); this workflow makes 1-2 API calls per meeting end event. Estimated Setup Time: 45-60 minutes including Zoom app creation, Calendly webhook configuration, and Data Table setup Installation Notes Critical setup steps: Zoom webhook validation**: You must complete Zoom's webhook endpoint validation process before receiving real events. The workflow includes a dedicated validation path—run it once after creating your Zoom app. Calendly webhook creation**: Use the "Manual Setup Trigger" path in the workflow to programmatically create your Calendly webhook subscription. This only needs to run once. Event type filtering**: Replace the placeholder YOUR_CALENDLY_EVENT_TYPE_URI with your specific demo event type URI from Calendly to avoid processing all meeting types. Test with a real meeting**: Book a test demo, join briefly with a different email than the booking email, then leave. The workflow should detect the "no-show" for the booking email. Common pitfalls to avoid: Forgetting to enable the disabled "Send Recovery Email" node after testing (it's disabled by default to prevent accidental sends during setup) Not configuring Zoom Server-to-Server OAuth correctly (requires Account ID, Client ID, and Client Secret—not JWT credentials) Using a personal Calendly account instead of an organization account (webhooks require organization-level access) Overlooking the Data Table creation step—the workflow will fail without this internal database Testing recommendations: Start with Slack notifications only (leave email sending disabled) to verify the workflow logic Use your own email as a test booking to safely generate AI messages without sending to real prospects Check the Data Table after each test to confirm booking records are being created and updated correctly Customization Options Easy modifications: Swap email for SMS**: Replace the email node with Twilio SMS to send text message follow-ups instead Add delays**: Insert "Wait" nodes to schedule follow-ups hours or days later rather than immediately Change AI tone**: Modify the OpenAI prompt to match your brand voice (casual, formal, humorous, etc.) Multi-step sequences**: Duplicate the AI and email nodes to create a 3-touch follow-up cadence over several days Different CRM platforms**: The HubSpot node can be swapped for Salesforce, Pipedrive, or any CRM n8n supports Extension possibilities: Add Google Sheets logging for executive dashboard reporting on no-show rates Integrate with Calendly's rescheduling API to automatically send rebooking links Connect to Loom or Vidyard APIs to attach pre-recorded demo videos in follow-up emails Create a "second chance" discount workflow that offers incentives for rescheduling Build a predictive model by exporting no-show data to analyze patterns (time of day, lead source, etc.) Category Sales Tags calendly zoom no-show-recovery demo-automation lead-follow-up sales-automation meeting-tracking ai-messaging slack-notification openai Use Case Examples SaaS sales team**: A B2B software company runs 40+ demos per week. When prospects no-show, this workflow immediately notifies the assigned rep in Slack with a pre-written LinkedIn message, sends an empathetic recovery email offering a Loom recording alternative, and flags the deal in HubSpot for manual outreach within 2 hours. Agency onboarding**: A marketing agency conducts discovery calls with new clients. If a client misses their scheduled kickoff meeting, the workflow logs the no-show, updates the client status in their CRM, and sends a friendly rescheduling email with three alternative time slots—all before the account manager even notices. Customer success**: A customer onboarding team tracks training session attendance. When users don't join their scheduled implementation calls, the workflow automatically sends a resource-rich email with documentation links, notifies the CSM team channel, and schedules a follow-up task in their project management tool.