Workflow Templates

This template uses VAPI and Cal.com to book appointments through a voice conversation. It detects whether the user wants to check availability or book an appointment, then responds naturally with real-time scheduling options. Who is this for? This workflow is perfect for: Voice assistant developers AI receptionists and smart concierge tools Service providers (salons, clinics, coaches) needing hands-free scheduling Anyone building voice-based customer experiences What does it do? This workflow turns a natural voice conversation into a working appointment system. It starts with a Webhook connected to your VAPI voice agent. The Set node extracts user intent (like “check availability” or “book now”). A Switch node branches logic based on the intent. If the user wants to check availability, the workflow fetches available times from Cal.com. If the user wants to book, it creates a new event using Cal.com's API. The final result is sent back to VAPI as a conversational voice response. How to use it Import this workflow into your n8n instance. Set up a Webhook node and connect it to your VAPI voice agent. Add your Cal.com API token as a credential (use HTTP Header Auth). Deploy and test using VAPI’s simulator or real phone input. (Optional) Customize the OpenAI prompt if you're using it to process or moderate inputs. Requirements A working VAPI agent A Cal.com account with API access n8n (cloud or self-hosted) An understanding of how to configure webhook and API credentials in n8n Customization Ideas Swap out Cal.com with another booking API (like Calendly) Add a Google Sheets or Supabase node to log appointments Use OpenAI to summarize or sanitize voice inputs before proceeding Build multi-turn conversations in VAPI for more complex bookings

This is the first version of a template for a RAG/GenAI App using WordPress content. As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template! How It Works This template includes three workflows: Workflow 1**: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store. Workflow 2**: Handle upserts for WordPress content when edits are made. Workflow 3**: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents. Why use this template? This template can be applied to various use cases: Build a GenAI application that requires embedded documents from your website's content. Embed or create a chatbot page on your website to enhance user experience as visitors search for information. Gain insights into the types of questions visitors are asking on your website. Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking. Prerequisites Access to Supabase for storing embeddings. Basic knowledge of Postgres and pgvector. A WordPress website with content to be embedded. An OpenAI API key Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency. Workflow 1 : Initial Embedding This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector. Step 0 : Create Supabase tables Nodes : Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions Postgres - Create Workflow Execution History Table These two nodes create tables in Supabase: The documents table, which stores embeddings of your website content. The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp. Step 1 : Retrieve and Merge WordPress Pages and Posts Nodes : WordPress - Get All Posts WordPress - Get All Pages Merge WordPress Posts and Pages These three nodes retrieve all content and metadata from your posts and pages and merge them. Important: ** **Apply filters to avoid generating embeddings for all site content. Step 2 : Set Fields, Apply Filter, and Transform HTML to Markdown Nodes : Set Fields Filter - Only Published & Unprotected Content HTML to Markdown These three nodes prepare the content for embedding by: Setting up the necessary fields for content embeddings and document metadata. Filtering to include only published and unprotected content (protected=false), ensuring private or unpublished content is excluded from your GenAI application. Converting HTML to Markdown, which enhances performance and relevance in Retrieval-Augmented Generation (RAG) by optimizing document embeddings. Step 3: Generate Embeddings, Store Documents in Supabase, and Log Workflow Execution Nodes: Supabase Vector Store Sub-nodes: Embeddings OpenAI Default Data Loader Token Splitter Aggregate Supabase - Store Workflow Execution This step involves generating embeddings for the content and storing it in Supabase, followed by logging the workflow execution details. Generate Embeddings: The Embeddings OpenAI node generates vector embeddings for the content. Load Data: The Default Data Loader prepares the content for embedding storage. The metadata stored includes the content title, publication date, modification date, URL, and ID, which is essential for managing upserts. ⚠️ Important Note : Be cautious not to store any sensitive information in metadata fields, as this information will be accessible to the AI and may appear in user-facing answers. Token Management: The Token Splitter ensures that content is segmented into manageable sizes to comply with token limits. Aggregate: Ensure the last node is run only for 1 item. Store Execution Details: The Supabase - Store Workflow Execution node saves the workflow execution ID and timestamp, enabling tracking of when each content update was processed. This setup ensures that content embeddings are stored in Supabase for use in downstream applications, while workflow execution details are logged for consistency and version tracking. This workflow should be executed only once for the initial embedding. Workflow 2, described below, will handle all future upserts, ensuring that new or updated content is embedded as needed. Workflow 2: Handle document upserts Content on a website follows a lifecycle—it may be updated, new content might be added, or, at times, content may be deleted. In this first version of the template, the upsert workflow manages: Newly added content** Updated content** Step 1: Retrieve WordPress Content with Regular CRON Nodes: CRON - Every 30 Seconds Postgres - Get Last Workflow Execution WordPress - Get Posts Modified After Last Workflow Execution WordPress - Get Pages Modified After Last Workflow Execution Merge Retrieved WordPress Posts and Pages A CRON job (set to run every 30 seconds in this template, but you can adjust it as needed) initiates the workflow. A Postgres SQL query on the n8n_website_embedding_histories table retrieves the timestamp of the latest workflow execution. Next, the HTTP nodes use the WordPress API (update the example URL in the template with your own website’s URL and add your WordPress credentials) to request all posts and pages modified after the last workflow execution date. This process captures both newly added and recently updated content. The retrieved content is then merged for further processing. Step 2 : Set fields, use filter Nodes : Set fields2 Filter - Only published and unprotected content The same that Step 2 in Workflow 1, except that HTML To Makrdown is used in further Step. Step 3: Loop Over Items to Identify and Route Updated vs. Newly Added Content Here, I initially aimed to use 'update documents' instead of the delete + insert approach, but encountered challenges, especially with updating both content and metadata columns together. Any help or suggestions are welcome! :) Nodes: Loop Over Items Postgres - Filter on Existing Documents Switch Route existing_documents (if documents with matching IDs are found in metadata): Supabase - Delete Row if Document Exists: Removes any existing entry for the document, preparing for an update. Aggregate2: Used to aggregate documents on Supabase with ID to ensure that Set Fields3 is executed only once for each WordPress content to avoid duplicate execution. Set Fields3: Sets fields required for embedding updates. Route new_documents (if no matching documents are found with IDs in metadata): Set Fields4: Configures fields for embedding newly added content. In this step, a loop processes each item, directing it based on whether the document already exists. The Aggregate2 node acts as a control to ensure Set Fields3 runs only once per WordPress content, effectively avoiding duplicate execution and optimizing the update process. Step 4 : HTML to Markdown, Supabase Vector Store, Update Workflow Execution Table The HTML to Markdown node mirrors Workflow 1 - Step 2. Refer to that section for a detailed explanation on how HTML content is converted to Markdown for improved embedding performance and relevance. Following this, the content is stored in the Supabase vector store to manage embeddings efficiently. Lastly, the workflow execution table is updated. These nodes mirros the **Workflow 1 - Step 3 nodes. Workflow 3 : An example of GenAI App with Wordpress Content : Chatbot to be embed on your website Step 1: Retrieve Supabase Documents, Aggregate, and Set Fields After a Chat Input Nodes: When Chat Message Received Supabase - Retrieve Documents from Chat Input Embeddings OpenAI1 Aggregate Documents Set Fields When a user sends a message to the chat, the prompt (user question) is sent to the Supabase vector store retriever. The RPC function match_documents (created in Workflow 1 - Step 0) retrieves documents relevant to the user’s question, enabling a more accurate and relevant response. In this step: The Supabase vector store retriever fetches documents that match the user’s question, including metadata. The Aggregate Documents node consolidates the retrieved data. Finally, Set Fields organizes the data to create a more readable input for the AI agent. Directly using the AI agent without these nodes would prevent metadata from being sent to the language model (LLM), but metadata is essential for enhancing the context and accuracy of the AI’s response. By including metadata, the AI’s answers can reference relevant document details, making the interaction more informative. Step 2: Call AI Agent, Respond to User, and Store Chat Conversation History Nodes: AI Agent** Sub-nodes: OpenAI Chat Model Postgres Chat Memories Respond to Webhook** This step involves calling the AI agent to generate an answer, responding to the user, and storing the conversation history. The model used is gpt4-o-mini, chosen for its cost-efficiency.

Automate Actions After PDF Generation with PDFMonkey in n8n Overview This n8n workflow template allows you to automatically react to PDF generation events from PDFMonkey. When a new PDF is successfully created, this workflow retrieves the file and processes it based on your needs—whether it’s sending it via email, saving it to cloud storage, or integrating it with other apps. How It Works Trigger: The workflow listens for a PDFMonkey webhook event when a new PDF is generated. Retrieve PDF: It fetches the newly generated PDF file from PDFMonkey. Process & Action: Depending on the outcome: ✅ On success: The workflow downloads the PDF and can distribute or store it. ❌ On failure: It handles errors accordingly (e.g., sending alerts, retrying, or logging the issue). Configuration To set up this workflow, follow these steps: Copy the Webhook URL generated by n8n. Go to your PDFMonkey Webhooks dashboard and paste the URL in the appropriate field to define the callback URL. Save your settings and trigger a test to ensure proper integration. 📖 For detailed setup instructions, visit: PDFMonkey Webhooks Documentation Use Cases This workflow is ideal for: Automating invoice processing (e.g., sending PDFs to customers via email). Archiving reports** in cloud storage (e.g., Google Drive, Dropbox, or AWS S3). Sending notifications** via Slack, Microsoft Teams, or WhatsApp when a new PDF is available. Logging generated PDFs** in Airtable, Notion, or a database for tracking. Customization You can customize this workflow to: Add conditional logic** (e.g., different actions based on the document type). Enhance security** (e.g., encrypting PDFs before sharing). Extend integrations** by connecting with CRM tools, task managers, or analytics platforms. Need Help? If you need assistance setting up or customizing this workflow, feel free to reach out to us via chat on pdfmonkey.io—we’ll be happy to help! 🚀

Chat with thing This n8n template lets you build a smart AI chat assistant that can handle text, images, and PDFs — using OpenAI's GPT-4o multimodal model. It supports dynamic conversations and file analysis, making it great for AI-driven support bots, personal assistants, or embedded chat widgets. 🔍 How it Works The chat trigger node kicks off a session using n8n's hosted chat UI. Users can send text or upload images or PDFs — the workflow checks if a file was included. If an image is uploaded, the file is converted to base64 and analyzed using GPT-4o's vision capabilities. GPT-4o generates a natural language description of the image and responds to the user's question in context. A memory buffer keeps track of the conversation thread, so follow-up questions are handled intelligently. OpenAI’s chat model handles both text-only and mixed media input seamlessly. 🧪 How to Use You can embed this in a website or use it with your own webhook/chat interface. The logic is modular — just swap out the chatTrigger node for another input (e.g. form or API). To use with documents, you can modify the logic to pass PDF content to GPT-4 directly. You can extend it with action nodes, e.g. saving results to Notion, Airtable, or sending replies via email or Slack. 🔐 Requirements Your OpenAI GPT-4o API key Set File Upload on the chat 🚀 Use Cases PDF explainer bot Internal knowledge chat with media support Personal assistant for mixed content

How it Works This n8n template makes it possible to send emails directly from your Obsidian notes. It leverages the power of the Obsidian Post Webhook plugin, allowing seamless integration between your notes and the email workflow. What it does: Receives note content and metadata from Obsidian via a Webhook. Parses YAML frontmatter to define email recipients, subject, and more. Automatically processes attachments, encoding them into an email-friendly format. Sends emails via Gmail and confirms the status back to Obsidian. Includes a testing feature to verify everything works before going live. Set-up Steps Webhook Configuration: Set your n8n POST Webhook URL in the Obsidian Obsidian Post Webhook plugin settings. Email Integration: Submit the Gmail credentials in n8n email nodes. Test the Workflow: Run a test from Obsidian to ensure the template functions correctly. Activate and Enjoy: Start sending customized emails with attachments from your notes in no time!

Knowledge RAG & AI Chat Agent: Google Drive to Qdrant Description This workflow transforms a Google Drive folder into an intelligent, searchable knowledge base and provides a chat agent to query it. It’s composed of two distinct flows: An ingestion pipeline to process documents. A live chat agent that uses RAG (Retrieval-Augmented Generation) and optional web search to answer user questions. This system fully automates the creation of a “Chat with your docs” solution and enhances it with external web-searching capabilities. Quick Implementation Steps Import the workflow JSON into your n8n instance. Set up credentials for Google Drive, Mistral AI, OpenAI, and Qdrant. Open the Web Search node and add your Tavily AI API key to the Authorization header. In the Google Drive (List Files) node, set the Folder ID you want to ingest. Run the workflow manually once to populate your Qdrant database (Flow 1). Activate the workflow to enable the chat trigger (Flow 2). Copy the public webhook URL from the When chat message received node and open it in a new tab to start chatting. What It Does The workflow is divided into two primary functions: 1. Knowledge Base Ingestion (Manual Trigger) This flow populates your vector database. Scans Google Drive:** Lists all files from a specified folder. Processes Files Individually:** Downloads each file. Extracts Text via OCR:* Uses *Mistral AI OCR API** for text extraction from PDFs, images, etc. Generates Smart Metadata:** A Mistral LLM assigns metadata like document_type, project, and assigned_to. Chunks & Embeds:* Text is cleaned, chunked, and embedded via *OpenAI’s text-embedding-3-small** model. Stores in Qdrant:** Text chunks, embeddings, and metadata are stored in a Qdrant collection (docaiauto). 2. AI Chat Agent (Chat Trigger) This flow powers the conversational interface. Handles User Queries:** Triggered when a user sends a chat message. Internal RAG Retrieval:* Searches *Qdrant Vector Store** first for answers. Web Search Fallback:* If unavailable internally, the agent offers to perform a *Tavily AI web search**. Contextual Responses:** Combines internal and external info for comprehensive answers. Who's It For Ideal for: Teams building internal AI knowledge bases from Google Drive. Developers creating AI-powered support, research, or onboarding bots. Organizations implementing RAG pipelines. Anyone making unstructured Google Drive documents searchable via chat. Requirements n8n instance** (self-hosted or cloud). Google Drive Credentials** (to list and download files). Mistral AI API Key** (for OCR & metadata extraction). OpenAI API Key** (for embeddings and chat LLM). Qdrant instance** (cloud or self-hosted). Tavily AI API Key** (for web search). How It Works The workflow runs two independent flows in parallel: Flow 1: Ingestion Pipeline (Manual Trigger) List Files: Fetch files from Google Drive using the Folder ID. Loop & Download: Each file is processed one by one. OCR Processing: Upload file to Mistral Retrieve signed URL Extract text using Mistral DOC OCR Metadata Extraction: Analyze text using a Mistral LLM. Text Cleaning & Chunking: Split into 1000-character chunks. Embeddings Creation: Use OpenAI embeddings. Vector Insertion: Push chunks + metadata into Qdrant. Flow 2: AI Chat Agent (Chat Trigger) Chat Trigger: Starts when a chat message is received. AI Agent: Uses OpenAI + Simple Memory to process context. RAG Retrieval: Queries Qdrant for related data. Decision Logic: Found → Form answer. Not found → Ask if user wants web search. Web Search: Performs Tavily web lookup. Final Response: Synthesizes internal + external info. How To Set Up 1. Import the Workflow Upload the provided JSON into your n8n instance. 2. Configure Credentials Create and assign: Google Drive** → Google Drive nodes Mistral AI** → Upload, Signed URL, DOC OCR, Cloud Chat Model OpenAI** → Embeddings + Chat Model nodes Qdrant** → Vector Store nodes 3. Add Tavily API Key Open Web Search node → Parameters → Headers Add your key under Authorization (e.g., tvly-xxxx). 4. Node Configuration Google Drive (List Files):** Set Folder ID. Qdrant Nodes:** Ensure same collection name (docaiauto). 5. Run Ingestion (Flow 1) Click Test workflow to populate Qdrant with your Drive documents. 6. Activate Chat (Flow 2) Toggle the workflow ON to enable real-time chat. 7. Test Open the webhook URL and start chatting! How To Customize Change LLMs:** Swap models in OpenAI or Mistral nodes (e.g., GPT-4o, Claude 3). Modify Prompts:** Edit the system message in ai chat agent to alter tone or logic. Chunking Strategy:** Adjust chunkSize and chunkOverlap in the Code node. Different Sources:** Replace Google Drive with AWS S3, Local Folder, etc. Automate Updates:* Add a *Cron** node for scheduled ingestion. Validation:** Add post-processing steps after metadata extraction. Expand Tools:** Add more functional nodes like Google Calendar or Calculator. Use Case Examples Internal HR Bot:** Answer HR-related queries from stored policy docs. Tech Support Assistant:** Retrieve troubleshooting steps for products. Research Assistant:** Summarize and compare market reports. Project Management Bot:** Query document ownership or project status. Troubleshooting Guide | Issue | Possible Solution | |------------|------------------------| | Chat agent doesn’t respond | Check OpenAI API key and model availability (e.g., gpt-4.1-mini). | | Known documents not found | Ensure ingestion flow ran and both Qdrant nodes use same collection name. | | OCR node fails | Verify Mistral API key and input file integrity. | | Web search not triggered | Re-check Tavily API key in Web Search node headers. | | Incorrect metadata | Tune Information Extractor prompt or use a stronger Mistral model. | Need Help or More Workflows? Want to customize this workflow for your business or integrate it with your existing tools? Our team at Digital Biz Tech can tailor it precisely to your use case from automation logic to AI-powered enhancements. 💡 We can help you set it up for free — from connecting credentials to deploying it live. Contact: shilpa.raju@digitalbiz.tech Website: https://www.digitalbiz.tech LinkedIn: https://www.linkedin.com/company/digitalbiztech/ You can also DM us on LinkedIn for any help.

🔮 Mystic Tarot Bot — AI-Powered iMessage Readings This magical n8n template turns your iMessage inbox into a soulful tarot reading experience powered by Blooio and AI. Users can send in questions or photos of their tarot spreads, and the bot replies like a mystical oracle — interpreting symbols, offering gentle insights, and guiding with poetic warmth. ✨ Ideal for solo reflection, spiritual creators, or client-based guidance services — no tech knowledge needed. 🌟 Use Cases Offer intuitive, emotionally resonant tarot readings via iMessage Support coaching, wellness, and metaphysical businesses with AI-enhanced readings Accept photos of real tarot card spreads or plain text questions Great for automating daily card pulls, client responses, or onboarding into spiritual flows 🧠 Good to Know Built using Blooio’s iMessage API — supports image attachments and conversational replies Includes visual recognition and symbolic interpretation of real tarot card photos Responses generated by OpenAI with a custom “Mystic Tarot Reader” persona Onboards users if they say “Hi” or request a virtual card draw Responds in poetic, spiritually attuned language — no markdown, no tech-speak ⚙️ How it Works Trigger: iMessage webhook via Blooio receives user message or image Check: Bot ignores self-sent messages to prevent loops Detect: If a photo is attached, it’s passed to AI for card recognition Interpret: The AI agent gives a heartfelt, symbolic interpretation Respond: A final, warm tarot reading is sent back through iMessage 📝 How to Use Set Up Blooio: Sign up at https://blooio.com Choose a Dedicated or Enterprise plan (image support required) Copy your API key from Settings → API Keys Paste it into the Send Message HTTP node as a Bearer token Customize the Experience: Adjust the prompt for a different tone or deck style Add journaling prompts, affirmations, or follow-ups Use other workflows to track users, create reading logs, or offer upsells Try It Out: Text your Blooio-connected number with: “Hi” → get onboarding “Draw a card for me” → get a virtual pull A tarot photo + question → get a full, soulful reading ✅ Requirements Blooio Account & API Token (Dedicated plan or higher for images) Optional: Tarot images, user questions, or both 🔧 Customizing This Workflow Add personalized spreads (e.g. past/present/future layouts) Send AI-generated visuals of the pulled cards Route readings into Notion, Airtable, or Google Sheets Expand to WhatsApp, web, or email with Blooio’s multichannel support 🃏 Let the cards speak. Let the messages flow.

This workflow enables seamless, bidirectional communication between WhatsApp and Slack using n8n. It automates the reception, processing, and forwarding of messages (text, media, and documents) between users on WhatsApp and private Slack channels. Key Features & Flow: 1. WhatsApp to Slack Flow Trigger: The workflow starts with a WhatsApp Trigger node that listens for new incoming messages via a webhook. Channel Handling: It checks if a Slack channel with the WhatsApp sender’s number exists If not, it creates a private Slack channel with the sender's number as the name. Message Type Routing: A Switch Node (Message Type) inspects the message type (text, image, audio, document). Based on type: Text: Sends the message directly to Slack. Image/Audio/Document: Retrieves media URL via WhatsApp API → downloads the media → uploads it to the appropriate Slack channel. 2. Slack to WhatsApp Flow Trigger: A Slack Trigger listens for new messages or file uploads in Slack. Message Type Routing: A second Switch Node (Checking Message Type) checks if the message is text or media. Routing Logic: Text Message: Extracts and forwards it to the WhatsApp contact (identified by the Slack channel name). Media/File Message: Retrieves media file URL from Slack → downloads it → sends it as a document via WhatsApp API. Key Integrations: WhatsApp Cloud API: For receiving messages, downloading media, and sending messages. Slack API: For creating/getting channels, posting messages, and uploading files. HTTP Request Node: Used to securely download media from Slack and WhatsApp servers with proper authentication. Automation Use Case: This workflow is ideal for businesses that handle customer support or conversations over WhatsApp and wish to log, respond, and collaborate using Slack as their internal communication tool.

CallForge - AI Sales Call Processing & Insights Extraction Automate sales call analysis with AI-powered insights for sales, marketing, and product teams. Who is This For? This workflow is designed for: ✅ Sales teams looking to extract structured insights from Gong call transcripts. ✅ Marketing professionals seeking AI-driven customer pain points & content strategy. ✅ Product teams needing feedback from sales calls to prioritize feature development. 🔍 What Problem Does This Workflow Solve? Manually analyzing Gong.io sales call transcripts is slow, inconsistent, and lacks structured insights. With CallForge, you can: ✔ Extract AI-powered insights about use cases, objections, competitors, and next steps. ✔ Provide structured marketing & product intelligence to enhance strategy. ✔ Automatically store call insights in Notion and Salesforce for easy access. ✔ Ensure resilience with automated reruns on failed workflows (handling Notion API limits). ✔ Improve decision-making with AI-powered competitor and sentiment analysis. 📌 Key Workflow Features 🎤 AI-Powered Transcript Analysis Uses AI to identify use cases, objections, competitors, and customer pain points. Categorizes insights for sales, marketing, and product teams. 📌 AI Agent Breakdown 🔹 Sales AI Agent – Extracts customer objections, pain points, competitors, and next steps. 🔹 Marketing AI Agent – Identifies recurring topics, keyword trends, and content opportunities. 🔹 Product AI Agent – Captures feature requests and AI/ML-related references. 📊 Structured Output Processing Sales Data Processor* → Stores insights in *Notion & Salesforce** for sales tracking. Marketing Data Processor* → Extracts *SEO & content strategy insights** for marketing teams. Product AI Data Processor* → Logs *customer feedback* to prioritize *feature development**. 💡 Competitor & Integration Analysis Tracks competing products mentioned in calls**. Identifies integration needs**, flagging workarounds used by prospects. 📢 Real-Time Slack Notifications Alerts teams on workflow progress** and completed call analyses. 🔄 Failure Resilience & Automated Re-Runs If a Notion API limit is reached, the process resumes automatically. 🚀 How This Works 🛠 1. Trigger & Call Data Processing The workflow retrieves Gong call transcripts and metadata. Normalizes data**, correcting common mispronunciations like "n8n." 🤖 2. AI Agents Analyze the Call Sales Agent** – Extracts actionable insights for sales follow-ups. Marketing Agent* – Identifies *recurring themes* and *keyword trends**. Product Agent* – Captures *feature requests and AI/ML usage mentions**. 📡 3. Data is Stored in Notion & Salesforce Logs AI-extracted insights* in *Notion** for structured tracking. Pushes sales-related data* to *Salesforce** for team accessibility. 🔔 4. Slack Alerts for Teams Notifies sales, marketing, and product teams** about extracted insights. CallForge - 01 - Filter Gong Calls Synced to Salesforce by Opportunity Stage CallForge - 02 - Prep Gong Calls with Sheets & Notion for AI Summarization CallForge - 03 - Gong Transcript Processor and Salesforce Enricher CallForge - 04 - AI Workflow for Gong.io Sales Calls CallForge - 05 - Gong.io Call Analysis with Azure AI & CRM Sync CallForge - 06 - Automate Sales Insights with Gong.io, Notion & AI CallForge - 07 - AI Marketing Data Processing with Gong & Notion CallForge - 08 - AI Product Insights from Sales Calls with Notion 📊 Sample Output Data 1️⃣ Sales Insights { "UseCases": [ { "Summary": "A manufacturing company wants to automate inventory tracking and reduce manual entry delays.", "DepartmentTags": ["Operations"], "IndustryTags": ["Manufacturing"], "ImplementationStatus": "Evaluating" } ], "Objection": { "ObjectionTags": ["Feature Limitation"], "Nature": "The prospect wanted a deeper integration with their ERP system, which n8n currently lacks." }, "CallSummary": "The call focused on automation for supply chain processes. The prospect expressed interest but wanted confirmation on ERP integration capabilities.", "NextSteps": ["Schedule a follow-up demo for ERP integration."] } 2️⃣ Marketing Insights { "MarketingInsights": [ { "Tag": "Workflow Template Request", "Summary": "The prospect requested a template for automating CRM lead tracking." } ], "RecurringTopics": [ { "Topic": "CRM Integration", "Mentions": 3, "Context": "Discussed how n8n could sync CRM data automatically." } ], "ActionableInsights": [ { "RecommendationType": "Tutorial", "Title": "How to Automate CRM Lead Tracking with n8n", "Topic": "CRM Integration", "Rationale": "The prospect expressed a need for CRM automation templates." } ] } 3️⃣ Product Feedback { "ProductFeedback": [ { "Sentiment": "Positive", "Feedback": "The external speaker praised the simplicity of n8n's UI, making it easier for non-developers to automate tasks." }, { "Sentiment": "Negative", "Feedback": "The external speaker mentioned frustration over the lack of a dedicated ERP integration node." } ], "AI_ML_References": { "Exist": true, "Context": "The external speaker mentioned using AI for automating customer ticket categorization.", "Details": { "DevelopmentStatus": "Building", "Department": "Support", "RequiresAgents": true, "RequiresRAG": false, "RequiresChat": "Yes: External App (e.g., Slack)" } } } 🔧 How to Customize This Workflow 💡 🔗 Change Data Storage – Swap Notion for Airtable, HubSpot, or another CRM. 💡 📩 Customize Slack Notifications – Send alerts via email, webhook, or another channel. 💡 🛠 Modify AI Processing – Adjust AI models or processing prompts. 💡 📊 Add More Integrations – Sync insights with Pipedrive, HubSpot, or another CRM. 🚀 Why Use This Workflow? ✔ Automates Gong call transcript analysis, eliminating manual work. ✔ Improves collaboration by structuring insights for sales, marketing, and product teams. ✔ Boosts sales conversions by identifying objections and next steps. ✔ Enhances marketing and SEO strategy with AI-driven insights. ✔ Optimizes product roadmap decisions based on customer feedback. This workflow scales AI-powered sales intelligence for better decision-making, content strategy, and sales enablement. 🚀

Brief Overview This workflow integrates Linear, Scrapeless, and Claude AI to create an AI research assistant that can respond to natural language commands and automatically perform market research, trend analysis, data extraction, and intelligent analysis. Simply enter commands such as /search, /trends, /crawl in the Linear task, and the system will automatically perform search, crawling, or trend analysis operations, and return Claude AI's analysis results to Linear in the form of comments. How It Works Trigger: A user creates or updates an issue in Linear and enters a specific command (e.g. /search competitor analysis). n8n Webhook: Listens to Linear events and triggers automated processes. Command identification: Determines the type of command entered by the user through the Switch node (search/trends/unlock/scrape/crawl). Data extraction: Calls the Scrapeless API to perform the corresponding data crawling task. Data cleaning and aggregation: Use Code Node to unify the structure of the data returned by Scrapeless. Claude AI analysis: Claude receives structured data and generates summaries, insights, and recommendations. Result writing: Writes the analysis results to the original issue as comments through the Linear API. Features Multiple commands supported /search: Google SERP data query /trends: Google Trends trend analysis /unlock: Unlock protected web content (JS rendering) /scrape: Single page crawling /crawl: Whole site multi-page crawling Claude AI intelligent analysis Automatically structure Scrapeless data Generate executable suggestions and trend insights Format optimization to adapt to Linear comment format Complete automation process Codeless process management based on n8n Multi-channel parallel logic distribution + data standardization processing Support custom API Key, regional language settings and other parameters Requirements Scrapeless API Key**: Scrapeless Service request credentials. Log in to the Scrapeless Dashboard Then click "Setting" on the left -> select "API Key Management" -> click "Create API Key". Finally, click the API Key you created to copy it. n8n Instance**: Self-hosted or n8n.cloud account. Claude AI**: Anthropic API Key (Claude Sonnet 3.7 model recommended) Installation Log in to Linear and get a Personal API Token Log in to n8n Cloud or a local instance Import the n8n workflow JSON file provided by Scrapeless Configure the following environment variables and credentials: Linear API Token Scrapeless API Token Claude API Key Configure the Webhook URL and bind to the Linear Webhook settings page Usage This automated job finder agent is ideal for: | Industry / Role | Use Case | | --------------------------------- | -------------------------------------------------------------------------------------------------- | | SaaS / B2B Software | | | Market Research Teams | Analyze competitor pricing pages using /unlock, and feature pages via /scrape. | | Content & SEO | Discover trending keywords and SERP data via /search and /trends to guide content topics. | | Product Managers | Use /crawl to explore product documentation across competitor sites for feature benchmarking. | | AI & Data-Driven Teams | | | AI Application Developers | Automate info extraction + LLM summarization for building intelligent research agents. | | Data Analysts | Aggregate structured insights at scale using /crawl + Claude summarization. | | Automation Engineers | Integrate command workflows (e.g., /scrape, /search) into tools like Linear to boost productivity. | | E-commerce / DTC Brands | | | Market & Competitive Analysts | Monitor competitor sites, pricing, and discounts with /unlock and /scrape. | | SEO & Content Teams | Track keyword trends and popular queries via /search and /trends. | | Investment / Consulting / VC | | | Investment Analysts | Crawl startup product docs, guides, and support pages via /crawl for due diligence. | | Consulting Teams | Combine SERP and trend data (/search, /trends) for fast market snapshots. | | Media / Intelligence Research | | | Journalists & Editors | Extract forum/news content from platforms like HN or Reddit using /scrape. | | Public Opinion Analysts | Monitor multi-source keyword trends and sentiment signals to support real-time insights. | Output

WordPress Blog to Google Sheets Sync Posts • Categories • Tags • Media 🧩 Overview This n8n workflow automatically syncs your WordPress website content — including posts, categories, tags, and media — into Google Sheets. It helps automate content reporting, SEO analysis, and data backups. The workflow can run on schedule or on demand via a webhook. 💡 Use cases Maintain a live database of blog posts in Google Sheets. Create dashboards in Google Data Studio or Looker Studio. Track new articles for newsletters or social media scheduling. Backup all WordPress content and media outside of your CMS. ⚙️ Prerequisites Before importing the workflow, ensure you have: A WordPress website with the REST API enabled (default in WP 4.7+). Authentication: either Application Passwords or Basic Auth credentials. A Google Sheet with the following tabs: Posts Categories Tags Media The following credentials configured in n8n: HTTP Basic Auth (for WordPress) Google Sheets OAuth2 🚀 Setup instructions Import the workflow into your n8n instance. Replace all example WordPress API URLs with your domain, for example: https://yourdomain.com/wp-json/wp/v2/ Connect your HTTP Basic Auth credentials (WordPress username + Application Password). Connect your Google Sheets OAuth2 account. Update the spreadsheet ID in each Google Sheets node with your own. Adjust the Schedule Trigger (e.g. run daily at 2:00 AM). Run once manually to verify data sync. 🧠 Workflow structure | Section | Description | |----------|--------------| | Schedule / Webhook Trigger | Starts the workflow manually or automatically | | Variables & Loop Vars | Initialize pagination for REST API requests | | Get Posts → Split Out → Update Posts | Fetch and update all WordPress posts | | Get Categories → Update Categories | Sync WordPress categories | | Get Tags → Update Tags | Sync WordPress tags | | Get Media → Split Out → Update Media | Sync media library (images, videos, etc.) | | IF Loops | Handles pagination logic until all items are retrieved | ⚠️ Notes & Limitations Works with standard WordPress REST API endpoints only. Custom post types require editing endpoint URLs. The per_page value defaults to 10; increase for faster syncs. For large sites, consider increasing n8n memory or adding execution logs. Avoid running the workflow too frequently to prevent API rate limits. 🎥 Video Tutorial A step-by-step setup guide is available here: 👉 https://www.youtube.com/watch?v=czSMWyD6f-0 Please subscribe to my YouTube channel to support me: 👉 https://www.youtube.com/@gregfrolovpersonal 👨‍💻 Author Created by: Grigory Frolov SEO & Automation Specialist — helping businesses integrate WordPress, AI, and data tools with n8n. 🧾 License This workflow is provided under the MIT License. Feel free to use, modify, and share improvements with the community.

How it works • Webhook receives lead form submissions from your website • AI Agent (GPT-4o) analyzes lead quality using intelligent scoring framework • Clearbit enriches company data automatically (employee count, industry, revenue) • Qualification score (0-100) determines routing: high-quality leads → HubSpot CRM + Slack alert, low-quality leads → Airtable for manual review • Structured output parser ensures reliable JSON formatting every time Set up steps • Time to set up: 15-20 minutes • Import the Clearbit sub-workflow first (separate workflow file included) • Create 7 custom properties in HubSpot (qualification_score, buying_intent, urgency_level, budget_indicator, ai_summary, pain_points, recommended_action) • Create Airtable base with 14 columns for low-quality lead tracking • Get Slack channel IDs for sales alerts and review requests • Add credentials: OpenAI (GPT-4o), Clearbit API, HubSpot OAuth2, Slack OAuth2, Airtable Token • Replace placeholder IDs in Slack and Airtable nodes • Configure the Clearbit Enrichment Tool node with your sub-workflow ID What you'll need • OpenAI API - OpenAI model access for AI qualification • Clearbit API - Free tier available for company enrichment • HubSpot - Free CRM account works • Slack - Standard workspace • Airtable - Free plan works • Website form - To send webhook data Who this is for Sales teams and agencies that want to automatically qualify inbound leads before they hit the CRM. Perfect for B2B companies with high lead volume that need intelligent routing.