Workflow Templates

This template crawls a website from its sitemap, deduplicates URLs in Supabase, scrapes pages with Crawl4AI, cleans and validates the text, then stores content + metadata in a Supabase vector store using OpenAI embeddings. It’s a reliable, repeatable pipeline for building searchable knowledge bases, SEO research corpora, and RAG datasets. ⸻ Good to know • Built-in de-duplication via a scrape_queue table (status: pending/completed/error). • Resilient flow: waits, retries, and marks failed tasks. • Costs depend on Crawl4AI usage and OpenAI embeddings. • Replace any placeholders (API keys, tokens, URLs) before running. • Respect website robots/ToS and applicable data laws when scraping. How it works Sitemap fetch & parse — Load sitemap.xml, extract all URLs. De-dupe — Normalize URLs, check Supabase scrape_queue; insert only new ones. Scrape — Send URLs to Crawl4AI; poll task status until completed. Clean & score — Remove boilerplate/markup, detect content type, compute quality metrics, extract metadata (title, domain, language, length). Chunk & embed — Split text, create OpenAI embeddings. Store — Upsert into Supabase vector store (documents) with metadata; update job status. Requirements • Supabase (Postgres + Vector extension enabled) • Crawl4AI API key (or header auth) • OpenAI API key (for embeddings) • n8n credentials set for HTTP, Postgres/Supabase How to use Configure credentials (Supabase/Postgres, Crawl4AI, OpenAI). (Optional) Run the provided SQL to create scrape_queue and documents. Set your sitemap URL in the HTTP Request node. Execute the workflow (manual trigger) and monitor Supabase statuses. Query your documents table or vector store from your app/RAG stack. Potential Use Cases This automation is ideal for: Market research teams collecting competitive data Content creators monitoring web trends SEO specialists tracking website content updates Analysts gathering structured data for insights Anyone needing reliable, structured web content for analysis Need help customizing? Contact me for consulting and support: LinkedIn

SEO Content Generation Workflow - n8n Template Instructions Who's it for This workflow is designed for SEO professionals, content marketers, digital agencies, and businesses who need to generate optimized meta tags, H1 headings, and content briefs at scale. Perfect for teams managing multiple clients or large keyword lists who want to automate competitor analysis and SEO content creation while maintaining quality and personalization. How it works The workflow automates the entire SEO content creation process by analyzing your target keywords against top competitors, then generating optimized meta elements and comprehensive content briefs. It uses AI-powered analysis combined with real competitor data to create SEO-friendly content that's tailored to your specific business context. The system processes keywords in batches, performs Google searches, scrapes competitor content, analyzes heading structures, and generates personalized SEO content using your company's database information for maximum relevance. Requirements Required Services and Credentials Google Sheets API**: For reading configuration and updating results Anthropic API**: For AI content generation (Claude Sonnet 4) OpenAI API**: For embeddings and vector search Apify API**: For Google search results Firecrawl API**: For competitor website scraping Supabase**: For vector database (optional but recommended) Template Spreadsheet Copy this template spreadsheet and configure it with your information: Template Link How to set up Step 1: Copy and Configure Template Make a copy of the template spreadsheet Fill in the Client Information sheet: Client name: Your company or client's name Client information: Brief business description URL: Website address Supabase database: Database name (prevents AI hallucination) Tone of voice: Content style preferences Restrictive instructions: Topics or approaches to avoid Complete the SEO sheet with your target pages: Page: Page you're optimizing (e.g., "Homepage", "Product Page") Keyword: Main search term to target Awareness level: User familiarity with your business Page type: Category (homepage, blog, product page, etc.) Step 2: Import Workflow Import the n8n workflow JSON file Configure all required API credentials in n8n: Google Sheets OAuth2 Anthropic API key OpenAI API key Apify API key Firecrawl API key Supabase credentials (if using vector database) Step 3: Test Configuration Activate the workflow Send your Google Sheets URL to the chat trigger Verify that all sheets are readable and credentials work Test with a single keyword row first Workflow Process Overview Phase 0: Setup and Configuration Copy template spreadsheet Configure client information and SEO parameters Set up API credentials in n8n Phase 1: Data Input and Processing Chat trigger receives Google Sheets URL System reads client configuration and SEO data Filters valid keywords and empty H1 fields Initiates batch processing Phase 2: Competitor Research and Analysis Searches Google for top 10 results per keyword Scrapes first 5 competitor websites Extracts heading structures (H1-H6) Analyzes competitor meta tags and content organization Phase 3: Meta Tags and H1 Generation AI analyzes keyword context and competitor data Accesses client database for personalization Generates optimized meta title (65 chars max) Creates compelling meta description (165 chars max) Produces user-focused H1 (70 chars max) Phase 4: Content Brief Creation Analyzes search intent percentages Develops content strategy based on competitor analysis Creates detailed MECE page structure Suggests rich media elements Provides writing recommendations and detail level scoring Phase 5: Data Integration and Updates Combines all generated content into unified structure Updates Google Sheets with new SEO elements Preserves existing data while adding new content Continues batch processing for remaining keywords How to customize the workflow Adjusting AI Models Replace Anthropic Claude with other LLM providers Modify system prompts for different content styles Adjust character limits for meta elements Modifying Competitor Analysis Change number of competitors analyzed (currently 5) Adjust scraping parameters in Firecrawl nodes Modify heading extraction logic in JavaScript nodes Customizing Output Format Update Google Sheets column mapping in Code node Modify structured output parser schema Change batch processing size in Split in Batches node Adding Quality Controls Insert validation nodes between phases Add error handling and retry logic Implement content quality scoring Extending Functionality Add keyword research capabilities Include image optimization suggestions Integrate social media content generation Connect to CMS platforms for direct publishing Best Practices Test with small batches before processing large keyword lists Monitor API usage and costs across all services Regularly update system prompts based on output quality Maintain clean data in your Google Sheets template Use descriptive node names for easier workflow maintenance Troubleshooting API Errors**: Check credential configuration and usage limits Scraping Failures**: Firecrawl nodes have error handling enabled Empty Results**: Verify keyword formatting and competitor availability Sheet Updates**: Ensure proper column mapping in final Code node Processing Stops**: Check batch processing limits and timeout settings

SEO Content Generation Workflow (Basic Version) - n8n Template Instructions Who's it for This workflow is designed for SEO professionals, content marketers, digital agencies, and businesses who need to generate optimized meta tags, H1 headings, and content briefs at scale. Perfect for teams managing multiple clients or large keyword lists who want to automate competitor analysis and SEO content creation without the complexity of vector databases. How it works The workflow automates the entire SEO content creation process by analyzing your target keywords against top competitors, then generating optimized meta elements and comprehensive content briefs. It uses AI-powered analysis combined with real competitor data to create SEO-friendly content that's tailored to your specific business context. The system processes keywords in batches, performs Google searches, scrapes competitor content, analyzes heading structures, and generates personalized SEO content using your company information for maximum relevance. Requirements Required Services and Credentials Google Sheets API**: For reading configuration and updating results Anthropic API**: For AI content generation (Claude Sonnet 4) Apify API**: For Google search results Firecrawl API**: For competitor website scraping Template Spreadsheet Copy this template spreadsheet and configure it with your information: Template Link How to set up Step 1: Copy and Configure Template Make a copy of the template spreadsheet Fill in the Client Information sheet: Client name: Your company or client's name Client information: Brief business description URL: Website address Tone of voice: Content style preferences Restrictive instructions: Topics or approaches to avoid Complete the SEO sheet with your target pages: Page: Page you're optimizing (e.g., "Homepage", "Product Page") Keyword: Main search term to target Awareness level: User familiarity with your business Page type: Category (homepage, blog, product page, etc.) Step 2: Import Workflow Import the n8n workflow JSON file Configure all required API credentials in n8n: Google Sheets OAuth2 Anthropic API key Apify API key Firecrawl API key Step 3: Test Configuration Activate the workflow Send your Google Sheets URL to the chat trigger Verify that all sheets are readable and credentials work Test with a single keyword row first Workflow Process Overview Phase 0: Setup and Configuration Copy template spreadsheet Configure client information and SEO parameters Set up API credentials in n8n Phase 1: Data Input and Processing Chat trigger receives Google Sheets URL System reads client configuration and SEO data Filters valid keywords and empty H1 fields Initiates batch processing Phase 2: Competitor Research and Analysis Searches Google for top 10 results per keyword using Apify Scrapes first 5 competitor websites using Firecrawl Extracts heading structures (H1-H6) from competitor pages Analyzes competitor meta tags and content organization Processes markdown content to identify heading hierarchies Phase 3: Meta Tags and H1 Generation AI analyzes keyword context and competitor data using Claude Incorporates client information for personalization Generates optimized meta title (65 characters maximum) Creates compelling meta description (165 characters maximum) Produces user-focused H1 (70 characters maximum) Uses structured output parsing for consistent formatting Phase 4: Content Brief Creation Analyzes search intent percentages (informational, transactional, navigational) Develops content strategy based on competitor analysis Creates detailed MECE page structure with H2 and H3 sections Suggests rich media elements (images, videos, infographics, tables) Provides writing recommendations and detail level scoring (1-10 scale) Ensures SEO optimization while maintaining user relevance Phase 5: Data Integration and Updates Combines all generated content into unified structure Updates Google Sheets with new SEO elements Preserves existing data while adding new content Continues batch processing for remaining keywords Key Differences from Advanced Version This basic version focuses on core SEO functionality without additional complexity: No Vector Database**: Removes Supabase integration for simpler setup Streamlined Architecture**: Fewer dependencies and configuration steps Essential Features Only**: Core competitor analysis and content generation Faster Setup**: Reduced time to deployment Lower Costs**: Fewer API services required How to customize the workflow Adjusting AI Models Replace Anthropic Claude with other LLM providers in the agent nodes Modify system prompts for different content styles or languages Adjust character limits for meta elements in the structured output parser Modifying Competitor Analysis Change number of competitors analyzed (currently 5) by adding/removing Scrape nodes Adjust scraping parameters in Firecrawl nodes for different content types Modify heading extraction logic in JavaScript Code nodes Customizing Output Format Update Google Sheets column mapping in the final Code node Modify structured output parser schema for different data structures Change batch processing size in Split in Batches node Adding Quality Controls Insert validation nodes between workflow phases Add error handling and retry logic to critical nodes Implement content quality scoring mechanisms Extending Functionality Add keyword research capabilities with additional APIs Include image optimization suggestions Integrate social media content generation Connect to CMS platforms for direct publishing Best Practices Setup and Testing Always test with small batches before processing large keyword lists Monitor API usage and costs across all services Regularly update system prompts based on output quality Maintain clean data in your Google Sheets template Content Quality Review generated content before publishing Customize system prompts to match your brand voice Use descriptive node names for easier workflow maintenance Keep competitor analysis current by running regularly Performance Optimization Process keywords in small batches to avoid timeouts Set appropriate retry policies for external API calls Monitor workflow execution times and optimize bottlenecks Troubleshooting Common Issues and Solutions API Errors Check credential configuration in n8n settings Verify API usage limits and billing status Ensure proper authentication for each service Scraping Failures Firecrawl nodes have error handling enabled to continue on failures Some websites may block scraping - this is normal behavior Check if competitor URLs are accessible and valid Empty Results Verify keyword formatting in Google Sheets Ensure competitor websites contain the expected content structure Check if meta tags are properly formatted in system prompts Sheet Update Errors Ensure proper column mapping in final Code node Verify Google Sheets permissions and sharing settings Check that target sheet names match exactly Processing Stops Review batch processing limits and timeout settings Check for errors in individual nodes using execution logs Verify all required fields are populated in input data Template Structure Required Sheets Client Information: Business details and configuration SEO: Target keywords and page information Results Sheet: Where generated content will be written Expected Columns Keywords**: Target search terms Description**: Brief page description Type de page**: Page category Awareness level**: User familiarity level title, meta-desc, h1, brief**: Generated output columns This streamlined version provides all essential SEO content generation capabilities while being easier to set up and maintain than the advanced version with vector database integration.

How it works This workflow enriches and personalizes your lead profiles by integrating HubSpot contact data, scraping social media information, and using AI to generate tailored outreach emails. It streamlines the process from contact capture to sending a personalized email — all automatically. The system fetches new or updated HubSpot contacts, verifies and enriches their Twitter/LinkedIn data via Phantombuster, merges the profile and engagement insights, and finally generates a customized email ready for outreach. Step-by-step 1. Trigger & Input HubSpot Contact Webhook: Fires when a contact is created or updated in HubSpot. Fetch Contact: Pulls the full contact details (email, name, company, and social profiles). Update Google Sheet: Logs Twitter/LinkedIn usernames and marks their tracking status. 2. Validation Validate Twitter/LinkedIn Exists: Checks if the contact has a valid social profile before proceeding to scraping. 3. Social Media Scraping (via Phantombuster) Launch Profile Scraper & 🎯 Launch Tweet Scraper: Triggers Phantombuster agents to fetch profile details and recent tweets. Wait Nodes: Ensures scraping completes (30–60 seconds). Fetch Profile/Tweet Results: Retrieves output files from Phantombuster. Extract URL: Parses the job output to extract the downloadable .json or .csv data file link. 4. Data Download & Parsing Download Profile/Tweet Data: Downloads scraped JSON files. Parse JSON: Converts the raw file into structured data for processing. 5. Data Structuring & Merging Format Profile Fields: Maps stats like bio, followers, verified status, likes, etc. Format Tweet Fields: Captures tweet data and associates it with the lead’s email. Merge Data Streams: Combines tweet and profile datasets. Combine All Data: Produces a single, clean object containing all relevant lead details. 6. AI Email Generation & Delivery Generate Personalized Email: Feeds the merged data into OpenAI GPT (via LangChain) to craft a custom HTML email using your brand details. Parse Email Content: Cleans AI output into structured subject and body fields. Sends Email: Automatically delivers the personalized email to the lead via Gmail. Benefits Automated Lead Enrichment — Combines CRM and real-time social media data with zero manual research. Personalized Outreach at Scale — AI crafts unique, relevant emails for each contact. Improved Engagement Rates — Targeted messages based on actual social activity and profile details. Seamless Integration — Works directly with HubSpot, Google Sheets, Gmail, and Phantombuster. Time & Effort Savings — Replaces hours of manual lookup and email drafting with an end-to-end automated flow.

Categories: Business Automation, Customer Support, AI, Knowledge Management This comprehensive workflow enables businesses to build and deploy a custom-trained AI Chatbot in minutes. By combining a sophisticated data scraping engine with a RAG-based (Retrieval-Augmented Generation) chat interface, it allows you to transform website content into a high-performance support agent. Powered by Google Gemini and Pinecone, this system ensures your chatbot provides accurate, real-time answers based exclusively on your business data. Benefits Instant Knowledge Sync** - Automatically crawls sitemaps and URLs to keep your AI up-to-date with your latest website content. Embeddable Anywhere** - Features a ready-to-use chat trigger that can be integrated into the bottom-right of any website via a simple script. High-Fidelity Retrieval** - Uses vector embeddings to ensure the AI "searches" your documentation before answering, reducing hallucinations. Smart Conversational Memory** - Equipped with a 10-message window buffer, allowing the bot to handle complex follow-up questions naturally. Cost-Efficient Scaling** - Leverages Gemini’s efficient API and Pinecone’s high-speed indexing to manage thousands of customer queries at a low cost. How It Works Dual-Path Ingestion: The process begins with an n8n Form where you provide a sitemap or individual URLs. The workflow automatically handles XML parsing and URL cleaning to prepare a list of pages for processing. Clean Content Extraction: Using Decodo, the workflow fetches the HTML of each page and uses a specialized extraction node to strip away code, ads, and navigation, leaving only the high-value text content. SignUp using: dashboard.decodo.com/register?referral_code=55543bbdb96ffd8cf45c2605147641ee017e7900. Vectorization & Storage: The cleaned text is passed to the Gemini Embedding model, which converts the information into 3076-dimensional vectors. These are stored in a Pinecone "supportbot" index for instant retrieval. RAG-Powered Chat Agent: When a user sends a message through the chat widget, an AI Agent takes over. It uses the user's query to search the Pinecone database for relevant business facts. Intelligent Response Generation: The AI Agent passes the retrieved facts and the current chat history to Google Gemini, which generates a polite, accurate, and contextually relevant response for the user. Requirements n8n Instance:** A self-hosted or cloud instance of n8n. Google Gemini API Key:** For text embeddings and chat generation. Pinecone Account:** An API key and a "supportbot" index to store your knowledge base. Decodo Access:** For high-quality website content extraction. How to Use Initialize the Knowledge Base: Use the Form Trigger to input your website URL or Sitemap. Run the ingestion flow to populate your Pinecone index. Configure Credentials: Authenticate your Google Gemini and Pinecone accounts within n8n. Deploy the Chatbot: Enable the Chat Trigger node. Use the provided webhook URL to connect the backend to your website's frontend chat widget. Test & Refine: Interact with the bot to ensure it retrieves the correct data, and update your knowledge base by re-running the ingestion flow whenever your website content changes. Business Use Cases Customer Support Teams** - Automate answers to 80% of common FAQs using your existing documentation. E-commerce Sites** - Help customers find product details, shipping policies, and return information instantly. SaaS Providers** - Build an interactive technical documentation assistant to help users navigate your software. Marketing Agencies** - Offer "AI-powered site search" as an add-on service for client websites. Efficiency Gains Reduce Ticket Volume** by providing instant self-service options. Eliminate Manual Data Entry** by scraping content directly from the live website. Improve UX** with 24/7 availability and zero wait times for customers. Difficulty Level: Intermediate Estimated Setup Time: 30 min Monthly Operating Cost: Low (variable based on AI usage and Pinecone tier)

Generate AI voice receptionist agents for local businesses using VAPI Automate the creation of personalized AI phone receptionists for local businesses by scraping Google Maps, analyzing websites, and deploying voice agents to VAPI. Who is this for? Agencies** offering AI voice solutions to local businesses Consultants** helping SMBs modernize their phone systems Developers** building lead generation tools for voice AI services Entrepreneurs** launching AI receptionist services at scale What this workflow does This workflow automates the entire process of creating customized AI voice agents: Collects business criteria through a form (city, keywords, quantity) Scrapes Google Maps for matching local businesses using Apify Fetches and analyzes each business website Generates tailored voice agent prompts using Claude AI Automatically provisions voice assistants via VAPI API Logs all created agents to Google Sheets for tracking The AI adapts prompts based on business type (salon, restaurant, dentist, spa) with appropriate tone, services, and booking workflows. Setup requirements Apify account** with Google Maps Scraper actor access Anthropic API key** for prompt generation OpenRouter API key** for website analysis VAPI account** with API access Google Sheets** connected via OAuth How to set up Import the workflow template Add your Apify credentials to the scraping node Configure Anthropic and OpenRouter API keys Replace YOUR_VAPI_API_KEY in the HTTP Request node header Connect your Google Sheets account Create a Google Sheet with columns: Business Name, Category, Address, Phone, Agent ID, Agent URL Update the Sheet URL in both Google Sheets nodes Activate the workflow and submit the form Customization options Business templates**: Edit the prompt in "Generate Agent Messages" to add new business categories Voice settings**: Modify ElevenLabs voice parameters (stability, similarity boost) LLM model**: Switch between GPT-4, Claude, or other models via OpenRouter Output format**: Customize the results page HTML in the final Form node

Intelligent Invoice Processing with AI Classification and XML Export Summary Automated invoice processing pipeline that extracts data from PDF invoices, uses AI Agent for intelligent expense categorization, generates XML for accounting systems, and routes high-value invoices for approval. Detailed Description A comprehensive accounts payable automation workflow that monitors for new PDF invoices, extracts text content, uses AI to classify expenses and detect anomalies, converts to XML format for accounting system integration, and implements approval workflows for high-value or unusual invoices. Key Features PDF Text Extraction**: Extract from File node parses invoice PDFs automatically AI-Powered Classification**: AI Agent categorizes expenses, suggests GL codes, detects anomalies XML Export**: Convert structured data to accounting-compatible XML format Approval Workflow**: Route invoices over $5,000 or low confidence for human review Multi-Trigger Support**: Google Drive monitoring or manual webhook upload Comprehensive Logging**: Archive all processed invoices to Google Sheets Use Cases Accounts payable automation Expense report processing Vendor invoice management Financial document digitization Audit trail generation Required Credentials Google Drive OAuth (for PDF source folder) OpenAI API key Slack Bot Token Gmail OAuth Google Sheets OAuth Node Count: 24 (19 functional + 5 sticky notes) Unique Aspects Uses Extract from File node for PDF text extraction (rarely used) Uses XML node for JSON to XML conversion (very rare) Uses AI Agent node for intelligent classification Uses Google Drive Trigger for file monitoring Implements approval workflow with conditional routing Webhook response** mode for API integration Workflow Architecture [Google Drive Trigger] [Manual Webhook] | | +----------+-----------+ | v [Filter PDF Files] | v [Download Invoice PDF] | v [Extract PDF Text] | v [Parse Invoice Data] (Code) | v [AI Invoice Classifier] <-- [OpenAI Chat Model] | v [Parse AI Classification] | v [Convert to XML] | v [Format XML Output] | v [Needs Approval?] (If) / \ Yes (>$5000) No (Auto) | | [Email Approval] [Slack Notify] | | +------+-------+ | v [Archive to Google Sheets] | v [Respond to Webhook] Configuration Guide Google Drive: Set folder ID to monitor in Drive Trigger node Approval Threshold: Default $5,000, adjust in "Needs Approval?" node Email Recipients: Configure finance-approvers@example.com Slack Channel: Set #finance-notifications for updates GL Codes: AI suggests codes; customize in AI prompt if needed Google Sheets: Configure document for invoice archive

Quick Overview This workflow makes unreliable HTTP requests safer by using Google Gemini to classify failures, retry only transient errors with exponential backoff, and log non-retriable or exhausted failures to a Google Sheets dead-letter register that is replayed nightly. How it works Receives inputs from another workflow (job name, URL, method, body, and retry budget) or runs nightly on a schedule to sweep a Google Sheets dead-letter register. Initializes an attempt counter and calls the target endpoint using an HTTP request with error output captured as data. When the call fails, sends the error details to Google Gemini to classify the failure (transient, permanent, or needs_human) and recommend a backoff time. Tracks attempts and either waits for the suggested backoff before retrying the HTTP request or stops retrying when the budget is exhausted or the failure is not transient. Appends failed jobs to a Google Sheets “Dead Letters” tab with the payload, diagnosis, and any required human action, and returns a structured failure response to the caller. On successful responses, returns a structured success result and, if the success came from a nightly replay, updates the corresponding Google Sheets entry to close it. Setup Add Google Gemini (PaLM) API credentials for the Gemini chat model used to triage failures. Add Google Sheets OAuth2 credentials and select the spreadsheet and “Dead Letters” sheet used as the dead-letter register. Create the “Dead Letters” sheet columns expected by the workflow (for example: Failed At, Job, Endpoint, Class, Attempts Used, Diagnosis, Someone Must, Payload, Status, Execution). In the calling workflow, use Execute Workflow to trigger this workflow and pass jobName, url, method, body, and maxAttempts. Activate the workflow so the nightly 2am sweep runs and can replay eligible transient failures from the register.

Quick Overview This workflow detects potential duplicate invoices when new rows are added in Google Sheets, scores matches against historical invoices, uses Google Gemini to classify risk, and then alerts Slack, calls an ERP hold API, and logs outcomes back to Google Sheets. How it works Triggers when a new invoice row is added to the “New_Invoices” sheet in Google Sheets. Reads the “Invoice_History” sheet and aggregates both the new invoices and historical invoices into two lists for comparison. Compares each new invoice against history using exact and fuzzy matching on invoice number, supplier name, amount, and invoice date to produce a duplicate flag, risk score, and best match details. Sends the detection results to Google Gemini to classify the invoice as Exact Duplicate, Near Duplicate, or Legitimate Invoice and to provide confidence, reasoning, and a recommended action. If Google Gemini classifies the invoice as an Exact Duplicate or Near Duplicate, posts a detailed alert to a Slack channel and sends a POST request to an ERP payment-hold endpoint. If Google Gemini classifies the invoice as legitimate, appends an approval record to the “Safe Invoices” sheet, and in all cases logs the investigation outcome to the “Fraud Cases” sheet in Google Sheets. Setup Connect Google Sheets OAuth credentials for the trigger and read/write access, and confirm the spreadsheet and sheet tabs “New_Invoices”, “Invoice_History”, “Safe Invoices”, and “Fraud Cases” exist. Add a Google Gemini (PaLM) API credential to the Google Gemini Chat Model node. Add Slack OAuth2 credentials and select the target channel (for example, #ap-fraud-alerts) for duplicate alerts. Set the ERP payment hold API URL in the HTTP request node and ensure the endpoint accepts the provided JSON body fields (invoice_id, supplier_name, amount, risk_score, reason).

Quick overview This workflow lets you upload a tender/RFT PDF via an n8n form, extracts key fields into Google Sheets using LlamaParse, and makes the document chat-searchable by embedding section chunks with Google Gemini and storing them in Supabase for WhatsApp-based Q&A with Anthropic/DeepSeek. How it works Receives a tender PDF when a user submits the n8n form. Generates a normalized document ID from the uploaded filename and sends the PDF to LlamaParse to parse the full text, split it into predefined tender sections, and extract structured tender metadata. Normalizes the extracted fields, calculates days-until-deadline and a bid score/recommendation, and appends or updates a row in a Google Sheets tender tracker. Combines the parsed full text with the section/page mapping, then carves each section into paragraph-aligned text chunks with page references. Creates an embedding for each chunk using the Google Gemini Embeddings API and inserts the chunk text and vectors into a Supabase table for retrieval. Triggers on an incoming WhatsApp message, embeds the user’s question with Google Gemini, runs a Supabase RPC vector search scoped to a single doc_id, and builds a context payload from the top matches. Sends the context and question to Anthropic (DeepSeek model) to generate a strictly context-grounded answer, formats the reply with cited source sections, and sends the response back on WhatsApp. Setup Add LlamaParse API credentials and ensure the LlamaParse Platform nodes have access to your account. Add a Google Sheets OAuth2 credential, then select the target spreadsheet and sheet in the tender tracker node (and ensure columns exist for the extracted fields and scoring outputs). Create a Supabase project with pgvector enabled, create a tender_chunks table that matches the inserted fields (including an embedding vector column), and implement the match_tender_chunks RPC used for vector search. Replace YOUR_GEMINI_API_KEY in both Google Gemini embedding HTTP requests and keep outputDimensionality consistent with your Supabase vector size. Add an Anthropic API credential for the answer-generation node and update the system prompt placeholders (for example, your company name and any policy text). Configure WhatsApp Cloud credentials for the WhatsApp Trigger and Send Message nodes, and replace the hardcoded doc_id in the question extraction step (or extend it to select the correct document dynamically).

Quick overview Automatically retrieve recent PubMed articles, identify the three papers most worth reading first using AI, and generate structured clinical literature triage reports in Google Sheets and Google Docs. How it works Reads the first pending literature request from the Google Sheets "Search Requests" tab. Marks the request as “processing” in Google Sheets and builds the PubMed query parameters (keyword, max results, specialty, clinical question, and output language). Searches PubMed via the NCBI E-utilities API (esearch) and fetches the corresponding article abstracts and metadata as XML (efetch). Parses and normalizes the XML into a structured list of candidate articles including title, abstract, journal, publication year, DOI, and PubMed URL. Sends the aggregated candidate articles to Google Gemini to select and rank the best three papers and generate a structured clinical evaluation for each. Appends the three ranked papers with summaries and triage fields to a Google Sheets “Triage Results” tab. Generates a formatted triage report in Google Docs and updates the original request status to “completed” in Google Sheets. Setup Create a Google Sheets OAuth connection and point the workflow to your spreadsheet, ensuring it has a “Search Requests” sheet with columns Keyword, Clinical Question, Specialty, Max Results, Output Language, and Status. Create a “Triage Results” sheet with columns matching the fields the workflow writes (for example Rank, Title, Journal, Pub Date, Pubmed URL, Summary 30sec, and the other triage outputs). Add a Google Gemini (PaLM) API credential for the Google Gemini chat model used to rank and summarize articles. Add a Google Docs OAuth connection and set the target Drive folder ID for where the workflow creates the report document. (Optional) Adjust the schedule interval and PubMed search parameters (such as sort order and retmax) to fit your review cadence and desired breadth. Requirements n8n (latest stable version recommended) Google account Google Sheets Google Docs Google Gemini API credential Internet access PubMed (NCBI E-utilities) Customization AI evaluation prompt Number of retrieved PubMed articles Number of ranked papers Output language Google Docs report format Google Sheets column structure Additional info This workflow is Part 1 of the Evidence-Based Medical AI Workflow Series. Unlike traditional article summarization workflows, it helps clinicians prioritize what to read by identifying the papers most worth reading first.

Overview Automatically generate viral short-form health videos using AI and publish them to social platforms with n8n and Veo 3. This workflow collects viral ideas, analyzes engagement patterns, generates AI video scripts, renders videos with Veo 3, and handles publishing and tracking fully automated, with no manual editing. Who is this for? This template is ideal for: Content creators building faceless health channels (Shorts, Reels, TikTok) Affiliate marketers promoting health products with video content AI marketers running high-volume short-form content funnels Automation builders combining LLMs, video AI, and n8n Teams that want a scalable, repeatable system for viral AI video production If you want to create health niche videos at scale without manually scripting, rendering, and uploading each video, this workflow is for you. What problem is this workflow solving? Creating viral short-form health videos usually involves many manual steps and disconnected tools, such as: Manually collecting and validating viral content ideas Writing hooks and scripts for each video Switching between AI tools for analysis and video generation Waiting for videos to render and checking status manually Uploading videos and tracking what has been published This workflow connects all these steps into a single automated pipeline and removes repetitive manual work. What this workflow does This automated AI health video workflow: Runs on a defined schedule Collects viral health content ideas from external sources Normalizes and stores ideas in Google Sheets Loads pending viral ideas for processing Analyzes each idea and generates AI-optimized video scripts Creates AI videos automatically using the Veo 3 API Waits for video rendering and checks completion status Retrieves the final rendered videos Optionally aggregates or merges video assets Publishes videos to social platforms Updates Google Sheets with processing and publishing results The entire process runs end-to-end with minimal human intervention. Setup 1. Prepare Google Sheets Create a Google Sheet to manage your content pipeline with columns such as: idea / topic – Viral idea or source content analysis – AI analysis or hook summary script – Generated video script status – pending / processing / completed / failed video_url – Final rendered video link publish_result – Publishing status or notes Only rows marked as pending will be processed by the workflow. 2. Connect Google Sheets Authenticate your Google Sheets account in n8n Select the spreadsheet in the load and update nodes Ensure the workflow can write status updates back to the same sheet 3. Configure AI & Veo 3 Add credentials for your AI model (e.g. Gemini or similar) Configure prompt logic for health niche content Add your Veo 3 API credentials Test video creation with a small number of ideas before scaling 4. Configure Publishing & Schedule Set up publishing credentials for your target social platforms Open the Schedule triggers and define how often the workflow runs The schedule controls how frequently new AI health videos are created and published How to customize this workflow to your needs You can adapt this workflow without changing the core structure: Replace viral idea sources with your own research or internal data Adjust AI prompts for different health sub-niches Add manual approval steps before video creation Disable publishing and use the workflow only for video generation Add retry logic for failed renders or API errors Extend the workflow with analytics or performance tracking Best practices Start with a small batch of test ideas Keep status values consistent in Google Sheets Focus on strong hooks for health-related content Monitor rendering and publishing nodes during early runs Adjust schedule frequency based on API limits Documentation For a full walkthrough and advanced customization ideas, see the Video Guide.