Workflow Templates

✍️🌄 WordPress + AI Content Creator This workflow automates the creation and publishing of multi-reading-level content for WordPress blogs. It leverages AI to generate optimized articles, automatically creates featured images, and provides versions of the content at different reading levels (Grade 2, 5, and 9). How It Works Content Generation & Processing 🎯 Starts with a manual trigger and a user-defined blog topic Uses AI to create a structured blog post with proper HTML formatting Separates and validates the title and content components Saves a draft version to Google Drive for backup Multi-Reading Level Versions 📚 Automatically rewrites the content for different reading levels: Grade 9: Sophisticated language with appropriate metaphors Grade 5: Simplified with light humor and age-appropriate examples Grade 2: Basic language with simple metaphors and child-friendly explanations WordPress Integration 🌐 Creates a draft post in WordPress with the Grade 9 version Generates a relevant featured image using Pollinations.ai Automatically uploads and sets the featured image Sends success/error notifications via Telegram Setup Steps Configure API Credentials 🔑 Set up WordPress API connection Configure OpenAI API access Set up Google Drive integration Add Telegram bot credentials for notifications Customize Content Parameters ⚙️ Adjust reading level prompts as needed Modify image generation settings Set WordPress post parameters Test and Deploy 🚀 Run a test with a sample topic Verify all reading level versions Check WordPress draft creation Confirm notification system This workflow is perfect for content creators who need to maintain a consistent blog presence while catering to different audience reading levels. It's especially useful for educational content, news sites, or any platform that needs to communicate complex topics to diverse audiences.

Summarize YouTube Videos & Chat About Content with GPT-4o-mini via Telegram Description This n8n workflow automates the process of summarizing YouTube video transcripts and enables users to interact with the content through AI-powered question answering via Telegram. It leverages the GPT-4o-mini model to generate summaries and provide insights based on the video’s transcript. How It Works Input: The workflow starts by receiving a YouTube video URL. This can be submitted through: A Telegram chat message. A webhook (e.g., triggered by a shortcut on Apple devices). Transcript Extraction: The URL is processed to extract the video transcript using the custom youtubeTranscripter community node (available here). The transcript is concatenated into a single text and stored in a Google Docs document. Summarization: The GPT-4o-mini AI model analyzes the transcript and generates a structured summary, including: A general overview. Key moments. Instructions (if applicable). The summary is then sent back to the user via Telegram. Interactive Q&A: Users can ask questions about the video content via Telegram. The AI retrieves the stored transcript from Google Docs and provides accurate, context-based answers, which are sent back through Telegram. Setup Instructions To configure this workflow, follow these steps: Import the Workflow: Download the provided JSON template and import it into your n8n instance. Install the Community Node: Install the youtubeTranscripter community node via npm: npm install n8n-nodes-youtube-transcription-kasha Important: This node requires a self-hosted n8n instance due to its external dependencies. Configure Nodes: Webhook: Set up the webhook to receive YouTube URLs. Alternatively, configure the Telegram node if using Telegram as the input method. Google Docs: Provide valid credentials to enable writing the transcript to a Google Docs document. AI Model: Set up the GPT-4o-mini model for summarization and Q&A functionality. Test the Workflow: Send a YouTube URL via your chosen input method (Telegram or webhook) and confirm that the summary is generated and delivered correctly. Customization Language**: Adjust the AI prompts to generate summaries and answers in any desired language. Output Format**: Modify the summary structure by editing the prompt in the summarization node. Input Methods**: Replace the Telegram node with another messaging or input node to adapt the workflow to different platforms. Who Can Benefit? This template is perfect for: Content Creators**: Quickly summarize video content for repurposing or review. Students and Researchers**: Extract key insights from educational or informational videos efficiently. General Users**: Interact with video content via AI without needing to watch the full video. Problem Solved This workflow simplifies video content consumption by: Automating the extraction and summarization of key points. Enabling interactive Q&A to address specific questions without rewatching the video. Additional Notes Disclaimer**: The youtubeTranscripter community node is required and only works on self-hosted n8n instances due to its reliance on external services. Apple Users**: Enhance your experience with a custom shortcut to share YouTube videos directly to the workflow. Download the shortcut here.

📽️ What this workflow does This workflow turns a user-submitted form with country or animal names into a cinematic video with animated scenes and immersive ambient audio. Using GPT-4 for prompt generation, Dumpling AI for visual creation,& Replicate for motion animation, ElevenLabs for sound generation, and Creatomate for video stitching, it fully automates video production — from raw idea to rendered file. 🎯 What problem is this solving? Creating engaging multimedia content can take hours. This workflow automates the entire process of ideation, design, and rendering of high-quality cinematic clips, eliminating the need for manual video editing or audio production. 👥 Who is this for? Content creators and educators Digital artists and storytellers Marketers or YouTubers creating short-form visual content No-code/AI automation enthusiasts ⚙️ Setup Instructions ✅ Step 1: Google Sheet Create a Google Sheet with two columns: Title Generated videos Update the Sheet ID and tab name in the final node. ✅ Step 2: Google Drive Create two folders: One for ambient audio tracks One for final generated videos Update the folder IDs in both Google Drive nodes. ✅ Step 3: Credentials Setup Make sure all your API tokens are saved as credentials in n8n. This workflow uses the following integrations: OpenAI (GPT-4) Dumpling AI (via HTTP header) Replicate.com ElevenLabs Google Drive Google Sheets Creatomate ✅ Step 4: Form Fields Ensure your trigger form includes these fields: Title Country 1, Country 2, Country 3, Country 4 Style (e.g., cinematic, epic, fantasy, noir, etc.) 🧩 How it works User Form Submission Kicks off the workflow with the required inputs. Format Inputs Combines all 4 countries/animals into a single array. GPT-4: Generate Visual Prompts Uses GPT-4 to create rich cinematic descriptions per animal/country. Dumpling AI: Create Images Each description becomes a high-quality visual. GPT-4: Create Motion Prompts Each image prompt is rewritten into motion-based video prompts. Replicate: Animate Prompts and images are sent to Replicate’s model for animation. GPT-4: Generate Sound Prompt Based on the style, GPT-4 creates an ambient sound idea. ElevenLabs: Create Ambient Audio Audio is generated and uploaded to Google Drive. Creatomate: Stitch All Media All 4 motion videos and the audio track are stitched into one cinematic output. Upload to Google Drive + Log to Sheet Final video is saved in Drive and logged in Sheets with its title and link. 🛠️ How to Customize 🎨 Modify GPT prompts for different themes (e.g., horror, fantasy, sci-fi). 🧠 Swap animals for characters, objects, or locations. 🎧 Replace ambient sound with ElevenLabs voiceovers or music. 📂 Add metadata logging (generation time, duration, tags). 🧪 Try using alternative video tools like Pika Labs or Runway ML. ✅ Requirements n8n self-hosted or cloud instance Active accounts for: OpenAI, Dumpling AI, Replicate, ElevenLabs, Creatomate Google credentials set up for Drive + Sheets This is a perfect end-to-end automation that showcases the power of AI + automation for video storytelling.

How it works This template uses a slack app to connect with your google calendar, generate an instant google meet link and post it as a message in a slack channel Setup steps Firstly, you'll need to create a slack app Authenticate and connect your slack account Connect and choose the Google calendar you want to generate Google meet links for Customize your slack message Then using a /meet command in slack, you can instantly generate and post your Google meet links

This template monitors a Google Drive folder, converts PDF documents into clean text chunks with Unstructured, generates OpenAI embeddings, and upserts vectors into Pinecone. It’s a practical, production-ready starting point for Retrieval-Augmented Generation (RAG) that you can plug into a chatbot, semantic search, or internal knowledge tools. How it works 1) Google Drive Trigger detects new files in a selected folder and downloads them. 2) The files are sent to Unstructured where they are split into smaller pieces (chunks). 3) The chunks are prepared to be sent to OpenAI where they are converted into vectors (embeddings). 4) The embeddings are recombined with their original data and the payload is prepared for upsert into the Pinecone index. Set up steps 1) In Pinecone, create an index with 1536 dimensions and configure it for text-embedding-3-small. 2) Copy the host url and paste it on the 'Pinecone Upsert' node. It should look something like this: https://{your-index-name}.pinecone.io/vectors/upsert. 3) Add Google Drive, OpenAI and Pinecone credentials in n8n. 4) Point the trigger to your ingest folder (you can use this article for demo). 5) Click the 'Open chat' button and enter the following: Which Git provider do the authors use?

This n8n workflow is a proof-of-concept template exploring how we might work with multimodal LLMs and their multi-image analysis capabilities. In this demo, we compare 2 screenshots of a webpage taken at different timestamps and pass both to our multimodal LLM for a visual comparison of differences. Handling multiple binary inputs (ie. images) in an AI request is supported by n8n's basic LLM node. How it works This template is intended to run as 2 parts: first to generate the base screenshots and next to run the visual regression test which captures fresh screenshots. Starting with a list of webpages captured in a Google sheet, base screenshots are captured for each using a external web scraping service called Apify.com (I prefer Apify but feel free to use whichever web scraping service available to you) These base screenshots are uploaded to Google Drive and will be referenced later when we run our testing. Phase 2 of the workflow, we'll use a scheduled trigger to fire sometime in the future which will reuse our web scraping service to generate fresh screenshots of our desired webpages. Next, re-download our base screenshots in parallel and with both old and new captures, we'll pass these to our LLM node. In the LLM node's options, we'll define 2 "user message" inputs with the type of binary (data) for our images. Finally, we'll prompt our LLM with our testing criteria and capture the regressions detected. Note, results will vary depending on which LLM you use. A final report can be generated using the LLM's output and is uploaded to Linear. Requirements Apify.com API key for web screenshotting service Google Drive and Sheets access to store list of webpages and captures Customising this workflow Have your own preferred web screenshotting service? Feel free to swap out Apify with your service of choice. If the web screenshot is too large, it may prove difficult for the LLM to spot differences with precision. Try splitting up captures into smaller images instead.

What this workflow does This (set of) workflow(s) shows how to start multiple sub-workflows, asynchronously, in parallel, and then wait for all of them to complete. Normally sub-workflows would need to be run synchronously, in series, or, if they are executed asynchronously (to run concurrently, in parallel), there is no easy way to merge/wait for an arbitrary number of them to complete. This is a "design pattern" template to show one approach for running multiple, data-driven instances of a sub-workflow "asynchronously," in parallel (instead of running them one at a time in series), but still prevent the later steps in the workflow from continuing until all of the sub-workflows have reported back that they are finished, via callback URL. There are other techniques involving messaging services, database tables, or other external "flow manager" helpers, but this technique accomplishes the goal fully within n8n. Setup To implement this pattern, examine the nodes in the template and modify the incoming data leading to: A split-out loop to acynchronously execute a sub-workflow multiple times, in parallel. For instance, each sub-workflow might process one of a list of incoming documents. The resumeUrl for the main/parent workflow is provided to all of the sub-workflow executions, along with a unique identifier that can be counted later (e.g. a document file-name). A "wait-for-all" loop that checks whether all sub-workflows have reported back (if node) and builds a unique list of identifiers from the callbacks received from each execution of the sub-workflow. The sub-workflow should be designed to respond immediately (async) and later send a callback request when it has finished processing. The callback request should include the unique identifier value received when the sub-workflow it was started. This is meant to be a possible answer to questions like this one about running things in parallel, maybe this one about waiting for things to finish, this one about managing sub-batches of things by waiting for each batch, or this one about running things in parallel. The topic of how to do this comes up A LOT, and this is one of the only techniques that (so far) seems to work.

Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering via WhatsApp. What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI via WhatsApp messaging. What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to import product documentation from Google Docs. Automatically splits large documents into chunks for efficient searching. Generates vector embeddings for each chunk using OpenAI embeddings. Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search. Workflow 2: AI-Powered Query & Response via WhatsApp Listens for incoming WhatsApp user messages, supporting various types: Text messages: Plain text queries from users. Audio messages: Voice notes transcribed into text for processing. Image messages: Photos or screenshots analyzed to provide contextual answers. Document messages: PDFs, spreadsheets, or other files parsed for relevant content. Converts incoming queries to vector embeddings and performs similarity search on the MongoDB vector store. Uses OpenAI’s GPT-4o-mini model with retrieval-augmented generation to produce concise, context-aware answers. Maintains conversation context across multiple turns using a memory buffer node. Routes different message types to appropriate processing nodes to maximize answer quality. Setup Setting up vector embeddings Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index. Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. Ensure the index name matches the one configured in n8n (data_index). See the example MongoDB search index template below for reference. Setting up chat Authenticate the WhatsApp node with your Meta account credentials to enable message receiving and sending. Connect the MongoDB collection containing embedded product documentation to the MongoDB Vector Search node used for similarity queries. Set up the system prompt in the Knowledge Base Agent node to reflect your company’s tone, answering style, and any business rules, ensuring it references the connected MongoDB collection for context retrieval. Make sure Both MongoDB nodes (in ingestion and chat workflows) are connected to the same collection with: An embedding field storing vector data, Relevant metadata fields (e.g., document ID, source), and The same vector index name configured (e.g., data_index). Search Index Example: { "mappings": { "dynamic": false, "fields": { "_id": { "type": "string" }, "text": { "type": "string" }, "embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "cosine" }, "source": { "type": "string" }, "doc_id": { "type": "string" } } } }

Automatically backs up your workflows to Github and generates documentation in a Notion database. Weekly run, uses the "internal-infra" tag to look for new or recently modified workflows Uses a Notion database page to hold the workflow summary, last updated date, and a link to the workflow Uses OpenAI's 4o-mini to generate a summarization of what the workflow does Stores a backup of the workflow in GitHub (recommend a private repo) Sends notification to Slack channel for new or updated workflows Who is this for Anyone seeking backup of their most important workflows Anyone seeking version control for their most important workflows Credentials required N8N: You will need an N8N credential created so the workflow can query the N8N instance to find all active workflows with the "internal-infra" tag Notion: You will need an Notion credential created OpenAI: You will need an OpenAI credential, unless you intend on rewiring this with your AI of choice (ollama, openrouter, etc.) GitHub: You will need an GitHub credential Slack: You will require an Slack credential, recommend a Bot / access token configuration Setup Notion Create a database with the following columns. Column type is specified in [type]. Workflow Name [text] isActive (dev) [checkbox] Error workflow setup [checkbox] AI Summary [text] Record last update [date/time] URL (dev) [text/url] Workflow created at [date/time] Workflow updated at [date/time] Slack Create a channel for updates to be posted into Github Create a private repo for your workflows to be exported into N8N Download & install the template Configure the blocks to use your N8N, Notion, OpenAI & Slack credentials for your own Edit the "Set Fields" block and change the URL to that of your N8N instance (cloud or self-hosted) Edit the "Add to Notion" action and specify the Database page you wish to update Edit the Slack actions to specify the Channel you want slack notifications posted to Edit the GitHub actions to specify the Repository Owner & Repository Name Sample output in Notion Workflow diagram

⚠️ Important Disclaimer: This template is only compatible with a self-hosted n8n instance using a community node. Who is this for? This workflow is ideal for digital content creators, marketers, social media managers, and automation enthusiasts who want to produce fully automated vertical video content featuring inspirational or motivational quotes. Specifically tailored for Thai language, it effectively demonstrates integration of AI-generated imagery, video, ambient sound, and visually appealing quote overlays. What problem is this workflow solving? Manually creating high-quality, vertically formatted quote videos is often repetitive, time-consuming, and involves multiple tedious steps like selecting suitable visuals, editing audio tracks, and correctly overlaying text. Additionally, manual uploading to platforms like YouTube and maintaining accurate content records are prone to errors and inefficiencies. What this workflow does: Fetches a quote, author, and scenic background description from a Google Sheet. Automatically generates a vertical background image using the Flux AI (txt2img) API. Transforms the AI-generated image into a subtly animated cinematic vertical video using the Kling video-generation API. Generates an immersive, ambient background sound using ElevenLabs’ sound generation API. Dynamically overlays the selected Thai-language quote and author text onto the generated video using FFmpeg, ensuring visually appealing typography (e.g., Kanit font). Automatically uploads the final video to YouTube. Updates the resulting YouTube video URL back to the Google Sheet, keeping your content records current and well-organized. Setup Requirements: This workflow requires a self-hosted n8n instance, as the execution of FFmpeg commands is not supported on n8n Cloud. Ensure FFmpeg is installed on your self-hosted environment. API keys and accounts setup for Flux, Kling, ElevenLabs, Google Sheets, Google Drive, and YouTube. Google Sheets Setup: Your Google Sheet must include these columns: Index** Unique identifier for each quote Quote (Thai)** Quote text in Thai language (or your chosen language) Pen Name (Thai)** Author or pen name of the quote's creator Background (EN)** Short English description of the scene (e.g., "sunrise over mountains") Prompt (EN)** Detailed English prompt describing the image/video scene (e.g., "peaceful sunrise with misty mountains") Background Image** URL of AI-generated image (updated automatically) Background Video** URL of generated video (updated automatically) Music Background** URL of generated ambient audio (updated automatically) Video Status** YouTube URL (updated automatically after upload) A ready-to-use Google Sheets template is provided [here (provide your actual link)]. To help you get started quickly, you can use this template spreadsheet. Next steps: Authenticate Google Sheets, Google Drive, YouTube API, Flux AI, Kling API, and ElevenLabs API within n8n. Ensure FFmpeg supports fonts compatible with your chosen language (for Thai, "Kanit" font is recommended). Prepare your Google Sheets with desired quotes, authors, and image/video prompts. How to customize this workflow to your needs: Fonts:** Adjust font type, size, color, and positioning within the provided FFmpeg commands in the workflow’s code nodes. Verify that selected fonts properly support your target language. Media Customization:** Customize the scene descriptions in your Google Sheet to change image/video backgrounds automatically generated by AI. Quote Management:** Easily manage, add, or update quotes and associated details directly via Google Sheets without workflow modifications. Audio Ambiance:** Customize or adjust the ambient sound prompt for ElevenLabs within the workflow’s HTTP Request node to match your video's desired mood. Benefits of using AI-generated content and localized fonts: Leveraging AI-generated visual and audio elements along with localized fonts greatly enhances audience engagement by creating visually appealing, professional-quality content tailored specifically for your target audience. This automated workflow drastically reduces production time and manual effort, enabling rapid, consistent content creation optimized for platforms such as YouTube Shorts, Instagram Reels, and TikTok.

AI Facebook Ad Spy Tool with Apify, OpenAI, Gemini & Google Sheets Categories: Competitive Intelligence, Marketing Automation, AI Analysis This workflow creates a comprehensive Facebook ad spy tool that scrapes competitor ads from Facebook's ad library and generates detailed analysis with rewritten versions. The system processes text, image, and video ads using different AI models, providing strategic intelligence for PPC agencies and marketers. Built to be sold as a premium service for $2,000+, this tool combines web scraping, multi-modal AI analysis, and competitor intelligence into one powerful automation. Benefits Complete Competitive Intelligence** - Analyze competitor strategies across all ad formats (text, image, video) Multi-Modal AI Analysis** - Uses GPT-4 Vision for images and Gemini for video content understanding Automated Ad Rewriting** - Generates inspired variations of successful competitor ads Quality Filtering** - Targets high-performing advertisers with significant page likes Scalable Processing** - Handle hundreds of competitor ads with detailed strategic analysis Premium Service Potential** - Easily sold to agencies and marketers for $2,000+ implementations How It Works Facebook Ad Library Scraping: Connects to Facebook's public ad library through Apify's specialized scraper Searches for active ads using customizable keywords and targeting parameters Extracts comprehensive ad data including creative assets, targeting info, and engagement metrics Filters results to focus on high-quality advertisers with substantial page followings Intelligent Content Routing: Automatically categorizes ads into text-only, image-based, or video content types Routes each ad type to specialized processing pipelines optimized for that content format Ensures appropriate AI models are used for each type of creative analysis Maintains data integrity while processing different content formats simultaneously Advanced Video Analysis Pipeline: Downloads video ads directly from Facebook's content delivery network Uploads videos to Google Drive for temporary storage and processing Initiates Gemini AI video upload sessions for multi-modal analysis Uses Gemini's advanced video understanding to generate detailed content descriptions Processes video narrative, visual elements, messaging strategy, and target audience insights Image and Text Processing: Analyzes image ads using GPT-4 Vision for comprehensive visual content understanding Processes text-only ads using GPT-4 for messaging strategy and copywriting analysis Identifies key persuasion techniques, target demographics, and messaging frameworks Generates detailed competitive intelligence reports for each ad format Strategic Intelligence Generation: Creates comprehensive summaries analyzing competitor messaging strategies and target audiences Generates rewritten ad copy that captures successful elements while avoiding direct copying Produces recreation prompts for images and videos that can be used with AI generation tools Organizes all insights in structured Google Sheets database for easy analysis and reporting Required Setup Configuration Apify Integration: Sign up for Apify account and obtain API key Replace <your-apify-api-key-here> in "Run Ad Library Scraper" node Customize Facebook Ad Library search URLs with your target keywords and regions AI Service Configuration: OpenAI API**: Set up for text analysis and image understanding with GPT-4 Vision Gemini API**: Configure for advanced video content analysis and description Replace <your-gemini-api-key-here> in all Gemini-related nodes Google Services Setup: Google Drive**: Configure OAuth for temporary video storage during Gemini processing Google Sheets**: Create results database with proper column structure for ad intelligence storage Facebook Ad Library Search Configuration: Customize the search parameters in the Apify scraper Google Sheets Database Structure: Create a sheet with these columns: ad_archive_id - Unique Facebook ad identifier page_id - Advertiser's Facebook page ID page_name - Advertiser's business name page_url - Link to advertiser's Facebook page type - Ad format (text, image, or video) date_added - When ad was analyzed summary - Detailed competitive intelligence analysis rewritten_ad_copy - AI-generated inspired version image_prompt - Description for recreating image ads video_prompt - Description for recreating video ads Business Use Cases PPC Agencies - Offer comprehensive competitor analysis services to clients for strategic advantage Marketing Teams - Research competitor strategies and messaging before launching new campaigns E-commerce Businesses - Analyze successful ads in your industry for creative inspiration SaaS Companies - Study how competitors position their products and target audiences Course Creators - Research educational content marketing approaches and messaging strategies Affiliate Marketers - Identify successful promotional strategies and high-converting ad formats Difficulty Level: Advanced Estimated Build Time: 3-4 hours Monthly Operating Cost: ~$200 (Apify + OpenAI + Gemini + Google Workspace APIs) Watch My Complete Build Process Want to see exactly how I built this entire Facebook ad spy system from scratch? I walk through the complete development process live, including API integrations, multi-modal AI setup, error handling, and the exact business strategy for selling this as a premium service. 🎥 Watch My Live Build: "Build A Facebook Ads Spy Tool With N8N (Sell for $2k+)" This comprehensive tutorial shows the real development process - including complex API orchestration, multi-modal AI integration, and proven strategies for monetizing competitive intelligence systems. Set Up Steps Apify Scraper Configuration: Set up Apify account and configure Facebook Ad Library scraper Customize search parameters for your target industries and regions Configure result limits and filtering parameters for quality control Test scraper with sample searches to verify data quality Multi-Modal AI Setup: Configure OpenAI API credentials for text and image analysis Set up Gemini API access for advanced video content understanding Configure appropriate rate limits and error handling for API stability Test AI analysis with sample ads to optimize prompt quality Google Services Integration: Set up Google Drive OAuth for temporary video storage during processing Create Google Sheets database with proper column structure for intelligence storage Configure sharing permissions and access controls for team collaboration Test complete data flow from scraping to final intelligence reports Quality Control and Filtering: Configure page likes threshold in "Filter For Likes" node (recommend 1,000+ for quality) Adjust content routing logic in Switch node based on your analysis needs Set up error handling and retry logic for reliable large-scale processing Test complete workflow with various ad types to ensure proper routing Advanced Customization: Customize AI prompts for your specific industry analysis needs Configure additional filtering criteria beyond page likes Set up automated scheduling for regular competitor monitoring Add custom fields to database for tracking specific competitive metrics Advanced Features Scale the system with additional capabilities: Industry-Specific Analysis - Customize prompts and filters for different verticals Trend Tracking - Monitor messaging changes over time for strategic insights Performance Correlation - Cross-reference ad engagement with business outcomes Alert Systems - Notify when competitors launch new campaign types Custom Reporting - Generate client-ready intelligence reports automatically Integration Extensions - Connect to CRM and marketing platforms for strategic workflow Important Considerations API Rate Limits - Built-in delays and error handling prevent service interruptions Content Rights - System generates inspired variations, not direct copies, for legal compliance Data Storage - Organize intelligence database for easy client reporting and analysis Scalability - Batch processing handles hundreds of ads efficiently without blocking Quality Assurance - Filtering logic ensures analysis focuses on successful, high-quality advertisers Why This System Works The competitive advantage lies in comprehensive multi-modal analysis: Complete format coverage - analyzes text, image, and video ads with appropriate AI models Strategic depth - goes beyond basic scraping to provide actionable intelligence Automation scale - processes competitor research that would take weeks manually Premium positioning - advanced AI analysis justifies higher service pricing Immediate value - clients receive actionable insights within hours of setup Check Out My Channel For more advanced automation systems that generate real business results and premium service opportunities, explore my YouTube channel where I share proven strategies for building profitable automation businesses.

This template can be used to find the content gaps in PDF documents using the InfraNodus knowledge graph / GraphRAG text representation and then generate ideas / questions / AI prompts that bridge those gaps based on optimizing the knowledge graph's structure. Simply upload several PDF files (research papers, corporate or market reports, etc) and generate an idea in seconds. The template is useful for: generating ideas / questions for research generating content ideas based on competitors' discourse finding blind spots in any discourse and generating ideas that address them. avoiding the generic bias of LLM models and focusing on what's important in your particular context What are Content Gaps and Knowledge Graphs? Knowledge graphs represent any text as a network: the main concepts are the nodes, their co-occurrences are the connections between them. Based on this representation, we build a graph and apply network science metrics to rank the most important nodes (concepts) that serve as the crossroads of meaning and also the main topical clusters that they connect. Naturally, some of the clusters will be disconnected and will have gaps between them. These are the topics (groups of concepts) that exist in this context (the documents you uploaded) but that are not very well connected. Addressing those gaps can help you see which groups of concepts you could connect with your own ideas. This is exactly what InfraNodus does: builds the structure, finds the gaps, then uses the built-in AI to generate research questions and ideas that bridge those gaps. How it works 1) Step 1: First, you upload your PDF files using an online web form, which you can run from n8n or even make publicly available. 2) Steps 2-4: The documents are processed using the Code and PDF to Text nodes to extract plain text from them. 3) Step 5: This text is then sent to the InfraNodus GraphRAG node that creates a knowledge graph, identifies structural gaps in this graph, and then uses built-in AI to generate ideas or research questions / prompts (if you use the InfraNodus question module instead). 4) Step 6: The ideas are then shown to the user in the same web form. Optionally, you can hook this template to your own workflow and send the idea / question generated to your own AI model / agent for further processing. If you'd like to sync this workflow to PDF files in a Google Drive folder, you can copy our Google Drive PDF processing workflow for n8n. 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. Add this key into the InfraNodus GraphRAG HTTP node(s) you use in this workflow. You do not need any OpenAI keys for this to work. Optionally, you can change the settings in the Step 4 of this workflow and enforce it to always use the biggest gap it identifies. Requirements An InfraNodus account and API key Note: OpenAI key is not required. You will have direct access to the InfraNodus AI with the API key. Customizing this workflow You can use this same workflow with a Telegram bot or Slack (to be notified of the summaries and ideas). You can also hook up automated social media content creation workflows in the end of this template, so you can generate posts that are relevant (covering the important topics in your niche) but also novel (because they connect them in a new way). Check out our n8n templates for ideas at https://n8n.io/creators/infranodus/ Also check the full tutorial with a conceptual explanation at https://support.noduslabs.com/hc/en-us/articles/20454382597916-Beat-Your-Competition-Target-Their-Content-Gaps-with-this-n8n-Automation-Workflow Also check out the video introduction to InfraNodus to better understand how knowledge graphs and content gaps work: For support and help with this workflow, please, contact us at https://support.noduslabs.com