Agents & Automations

Update 19-04-2025Change from OpenAI to Claude 3.7 Sonnet moduleAdding the Think Tool The update enables significantly better results to be achieved. This is particularly noticeable during longer meetings! What this workflow does This workflow retrieves the Zoom meeting data from the last 24 hours. The transcript of the last meeting is then retrieved, processed, a summary is created using AI, and sent to all participants by email. AI is then used to create tasks and follow-up appointments based on the content of the meeting. Important: You need a Zoom Workspace Pro account and must have activated Cloud Recording/Transcripts! This workflow has the following sequence: - Manual trigger (Can be replaced by a scheduled trigger or a webhook) - Retrieval of Zoom meeting data - Filter the events of the last 24 hours - Retrieval of transcripts and extract of the text - Creating a meeting summary, format to HTML, and send per mail - Create tasks and follow-up call (if discussed in the meeting) in ClickUp/Outlook (can be replaced by Gmail, Airtable, and so forth) via sub workflow Requirements: - Zoom Workspace (via API and HTTP Request): Documentation - Microsoft Outlook: Documentation - ClickUp: Documentation - AI API access (e.g., via OpenAI, Anthropic, Google, or Ollama) - SMTP access data (for sending the mail) You must set up the individual sub-workflows as separate workflows. Then set the “Execute workflow trigger” here. Then select the corresponding sub-workflow in the AI Agent Tools. You can select the number of domains yourself. If the data queries are not required, simply delete the corresponding tool (e.g., “Analytics_Domain_5). Feel free to contact me via LinkedIn if you have any questions!

This workflow automates tagging for WordPress posts using AI: - Fetch blog post content and metadata. - Generate contextually relevant tags using AI. - Verify existing tags in WordPress and create new ones if necessary. - Automatically update posts with accurate and optimized tags. Set up steps: Estimated time: ~15 minutes. 1. Configure the workflow with your WordPress API credentials. 2. Connect your content source (e.g., RSS feed or manual input). 3. Adjust tag formatting preferences in the workflow settings. 4. Run the workflow to ensure proper tag creation and assignment. This workflow is perfect for marketers and content managers looking to streamline their content categorization and improve SEO efficiency.

Fully automated AI Agent system that creates carousels and slideshows for social media, 100% automated, running on autopilot every day or every hour! You can post to multiple Instagram accounts, multiple TikTok accounts, multiple Pinterest accounts, etc. It even posts long-form threads for Twitter, Bluesky, and Threads, with 1 image per post, so everything is neatly formatted. And, you can easily customize the images and quotes to fit your brand, niche, or business. Make - workflow automation that runs daily AI agent - use ChatGPT to brainstorm quotes and image prompts Blotato - generate slideshow/carousel and publish to 8 social platforms

This n8n template is designed to assist and improve customer support team member capacity by automating the resolution of long-lived and forgotten JIRA issues. How it works The Schedule Trigger runs daily to check for long-lived unresolved issues and imports them into the workflow. Each issue is handled as a separate subworkflow by using an execute workflow node. This allows for parallel processing. A report is generated from the issue using its comment history, allowing the issue to be classified by AI - determining the state and progress of the issue. If determined to be resolved, sentiment analysis is performed to track customer satisfaction. If negative, a Slack message is sent to escalate; otherwise, the issue is closed automatically. If no response has been initiated, an AI agent will attempt to search and resolve the issue itself using similar resolved issues or from the Notion database. If a solution is found, it is posted to the issue and closed. If the issue is blocked and waiting for responses, then a reminder message is added. How to use This template searches for JIRA issues that are older than 7 days and are not in the "Done" status. Ensure there are some issues that meet this criteria; otherwise, adjust the search query to suit. It works best if you frequently have long-lived issues that need resolving. Ensure the Notion tool is configured not to read documents you didn't intend it to, i.e., private and/or internal documentation. Customizing this workflow Why not try classifying issues as they are created? One use-case may be for quality control, such as ensuring reporting criteria are adhered to, summarizing and rephrasing issues for easier reading, or adjusting priority.

The "Voice RAG Chatbot with ElevenLabs and OpenAI" workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information. It is ideal for shops, commercial activities, and restaurants. How it works: Here's how it operates: 1. Webhook Activation: The process begins when a user interacts with the voice agent set up on ElevenLabs, triggering a webhook in n8n. This webhook sends a question from the user to the AI Agent node. 2. AI Agent Processing: Upon receiving the query, the AI Agent node processes the input using predefined prompts and tools. It extracts relevant information from the knowledge base stored within the Qdrant vector database. 3. Knowledge Base Retrieval: The Vector Store Tool node interfaces with the Qdrant Vector Store to retrieve pertinent documents or data segments matching the user’s query. 4. Text Generation: Using the retrieved information, the OpenAI Chat Model generates a coherent response tailored to the user’s question. 5. Response Delivery: The generated response is sent back through another webhook to ElevenLabs, where it is converted into speech and delivered audibly to the user. 6. Continuous Interaction: For ongoing conversations, the Window Buffer Memory ensures context retention by maintaining a history of interactions, enhancing the conversational flow. Set up steps: To configure this workflow effectively, follow these detailed setup instructions: - ElevenLabs Agent Creation: Begin by creating an agent on ElevenLabs (e.g., named 'test_n8n'). Customize the first message and define the system prompt specific to your use case, such as portraying a character like a waiter at "Pizzeria da Michele." Add a Webhook tool labeled 'test_chatbot_elevenlabs' configured to receive questions via POST requests. - Qdrant Collection Initialization: Utilize the HTTP Request nodes ('Create collection' and 'Refresh collection') to initialize and clear existing collections in Qdrant. Ensure you update placeholders QDRANTURL and COLLECTION accordingly. - Document Vectorization: Use Google Drive integration to fetch documents from a designated folder. These documents are then downloaded and processed for embedding. Employ the Embeddings OpenAI node to generate embeddings for the downloaded files before storing them into Qdrant via the Qdrant Vector Store node. - AI Agent Configuration: Define the system prompt for the AI Agent node which guides its behavior and responses based on the nature of queries expected (e.g., product details, troubleshooting tips). Link necessary models and tools including OpenAI language models and memory buffers to enhance interaction quality. - Testing Workflow: Execute test runs of the entire workflow by clicking 'Test workflow' in n8n alongside initiating tests on the ElevenLabs side to confirm all components interact seamlessly. Monitor logs and outputs closely during testing phases to ensure accurate data flow between systems. - Integration with Website: Finally, integrate the chatbot widget onto your business website, replacing placeholder AGENT_ID with the actual identifier created earlier on ElevenLabs. By adhering to these comprehensive guidelines, users can successfully deploy a sophisticated voice-driven chatbot capable of delivering precise answers utilizing advanced retrieval-augmented generation techniques powered by OpenAI and ElevenLabs technologies.

Who is this for? This workflow is designed for: - Database administrators and developers working with MongoDB - Content managers handling movie databases - Organizations looking to implement AI-powered search and recommendation systems - Developers interested in combining LangChain, OpenAI, and MongoDB capabilities What problem does this workflow solve? Traditional database queries can be complex and require specific MongoDB syntax knowledge. This workflow addresses: - The complexity of writing MongoDB aggregation pipelines - The need for natural language interaction with movie databases - The challenge of maintaining user preferences and favorites - The gap between AI language models and database operations What this workflow does This workflow creates an intelligent agent that: - Accepts natural language queries about movies - Translates user requests into MongoDB aggregation pipelines - Queries a movie database containing detailed information including: - Plot summaries - Genre classifications - Cast and director information - Runtime and release dates - Ratings and awards - Provides contextual responses using OpenAI's language model - Allows users to save favorite movies to the database - Maintains conversation context using a window buffer memory SetupRequired Credentials: - OpenAI API credentials - MongoDB connection details Node Configuration: - Configure the MongoDB connection in the MongoDBAggregate node - Set up the OpenAI Chat Model with your API key - Ensure the webhook trigger is properly configured for receiving chat messages Database Requirements: - A MongoDB collection named "movies" with the specified document structure - Proper indexes for efficient querying - Appropriate user permissions for read/write operations How to customize this workflowModify the Document Structure: - Update the tool description in the MongoDBAggregate node to match your collection schema - Adjust the aggregation pipeline templates for your specific use case Enhance the AI Agent: - Customize the prompt in the "AI Agent - Movie Recommendation" node - Modify the window buffer memory size based on your context needs - Add additional tools for more functionality Extend Functionality: - Add more MongoDB operations beyond aggregation - Implement additional workflows for different types of queries - Create custom error handling and validation - Add user authentication and rate limiting Integration Options: - Connect to external APIs for additional movie data - Add webhook endpoints for different platforms - Implement caching mechanisms for frequent queries - Add data transformation nodes for specific output formats This workflow serves as a foundation that can be adapted to various use cases beyond movie recommendations, such as e-commerce product search, content management systems, or any scenario requiring intelligent database interaction.

Automatically filter Google Sheets rows and update them with AI-generated messages from Anthropic Claude. Enhance data management and communication efficiency.

How it works: Webhook URL that responds to requests with an AI-generated image based on the prompt provided in the URL. Setup Steps: 1. Ideate your prompt. 2. URL encode the prompt (as shown in the template). 3. Authenticate with your OpenAI credentials. 4. Put together the webhook URL with your prompt and enter it into a web browser. In this way, you can expose a public URL to users, employees, etc., without exposing your OpenAI API key to them.

This workflow uses AI to analyze the content of every new message in Gmail and then assigns specific labels according to the context of the email. The default configuration of the workflow includes 3 labels: - “Partnership” - email about sponsored content or cooperation, - “Inquiry” - email about products or services, - “Notification” - email that doesn't require a response. You can add or edit labels and descriptions according to your use case. 🎬 See this workflow in action in my YouTube video about automating Gmail. How it works? The Gmail trigger performs polling every minute for new messages (you can change the trigger interval according to your needs). The email content is then downloaded and forwarded to an AI chain. 💡 The prompt in the AI chain node includes instructions for applying labels according to the email content—change label names and instructions to fit your use case. Next, the workflow retrieves all labels from the Gmail account and compares them with the label names returned from the AI chain. Label IDs are aggregated and applied to processed email messages. ⚠️ Label names in the Gmail account and workflow (prompt, JSON schema) must be the same. Set up steps: 1. Set credentials for Gmail and OpenAI. 2. Add labels to your Gmail account (e.g., “Partnership”, “Inquiry”, and “Notification”). 3. Change the prompt in the AI chain node (update the list of label names and instructions). 4. Change the list of available labels in the JSON schema in the parser node. 5. Optionally: change the polling interval in the Gmail trigger (by default, the interval is 1 minute). If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.

Automatically update Google Sheets rows with AI-generated content from ChatGPT and create Google Docs for comprehensive data management.

Who's this for? Anyone who wants to improve the SEO of their website. Umami users who want insights on how to improve their site. SEO managers who need to generate reports weekly. --- Case study Watch the YouTube tutorial here. Get my SEO A.I. agent system here. You can read more about how this works here. --- How it works This workflow calls the Umami API to get data. Then it sends the data to A.I. for analysis. It saves the data and analysis to Baserow. --- How to use this 1. Input your Umami credentials. 2. Input your website property ID. 3. Input your Openrouter.ai credentials. 4. Input your baserow credentials. You will need to create a Baserow database with columns: Date, Summary, Top Pages, Blog (name of your blog). --- Future development Use this as a template. There are a lot more Umami stats you can pull from the API. Change the A.I. prompt to give even more detailed analysis. --- Created by Rumjahn.

List invoices uploaded to Rossum Elis, then extract the invoice fields automatically and store the result in a Google Sheets spreadsheet.

This workflow creates a fully automated post-purchase communication sequence that handles both the initial thank you message and the subsequent scheduling request. It eliminates the need for manual follow-up while maintaining a personalized feel.

This n8n workflow demonstrates multiple ways to harness DeepSeek's AI models in your automation pipeline! 🌟 Core Features - Multiple Integration Methods 🔌 - Local deployment using Ollama for DeepSeek-R1 - Direct API integration with DeepSeek Chat V3 - Conversational agent with memory buffer - HTTP request implementation with both raw and JSON formatsModel Options 🧠 - DeepSeek Chat V3 for general conversation - DeepSeek-R1 for advanced reasoning - Memory-enabled agent for persistent context Quick Setup 🛠️API Configuration - Base URL: https://api.deepseek.com - Get your API key from platform.deepseek.com/api_keysLocal Setup 💻 - Install Ollama for local deployment - Set up DeepSeek-R1 via Ollama - Configure local credentials in n8nImplementation Details 🔧 - Conversational Agent - Window Buffer Memory for context - Customizable system messages - Built-in error handling with retries API Endpoints 🌐 - Chat completions for V3 and R1 models - OpenAI API format compatibles

Who is it for? If you are getting a lot of emails into your Gmail inbox, then probably some of those can be solved easily by replying or by doing specific short tasks. Analyzing whole email thread content just to catch up with multiple threads can be very wasteful. By using AI, you can actually get simple propositions of what should be done before closing this specific email and an actual proposed answer to that email. This is especially useful if you need to do some actions before replying to an email. In that case, you can simply assign a task to a specific person, await until it's done, copy-paste the AI answer when it's done, and close. Another good use would be if multiple people are working in one inbox. It can make the process much more streamlined. How It Works? The script runs on your selected trigger. If you are using the section "Read and Star," then you may use "Email Trigger." The automation looks for existing open Todoist tasks that have the same title as the email. If the task does not exist, then we ask AI to analyze the thread and provide output that is Todoist-API-ready, including: - A summary of email content - Proposed actions to be taken - Proposed answer to this email If the email was unstarred for some reason but the task was not closed, then the task is closed automatically. The script is not trying to unstar messages that have closed tasks, as this could lead to inconsistencies. How to set up? 1. Select and set up your triggers, depending on your needs. 2. Set up connections using N8N instructions. You will need: - Gmail - Todoist - AI (in this workflow, OpenAI is used) 3. (Optional) Remove the "Read and Star" section if you don't want tasks automatically read and starred. 4. (Optional) Adjust the AI node, especially useful if you want to use a different model or have a response in a different language. NOTE: Chat does not have memory attached on purpose. The purpose is that it should analyze each inbox message separately, not in the thread. When using memory, it can get lost easily. NOTE 2: You might want to adjust limits on nodes "Get Unread From Inbox," "Get Starred From Inbox," and "Get Open Tasks," especially if you have issues with the model complying with the output structure. And that's it. I hope that this automation will make your Gmail <-> Todoist process much more streamlined! What's More? There is actually more that you could do with this automation, but it really depends on your needs. For example, you could add a Form trigger to handle incoming support requests. Another option is that you could replace Todoist with Asana or any database (like NocoDB) if you are using it for your task management.

AI-Powered Social Video Generator with Auto-Posting to Instagram, TikTok, YouTube, Facebook, LinkedIn, Threads, Pinterest, Twitter (X), and BlueskyWho is this workflow for? This workflow is ideal for content creators, marketers, social media managers, and automation enthusiasts who want to generate, customize, and publish short-form videos across multiple platforms without manual editing or posting. If you use tools like ChatGPT, Kling, or Blotato and want to streamline your content creation process, this workflow is made for you. What problem does this workflow solve? Publishing regular video content on multiple platforms is time-consuming—especially when adding voice-overs, captions, and managing distribution. This workflow solves that by: - Automating video generation using AI (Kling + GPT-4) - Adding realistic voice narration - Styling subtitles for social media - Creating titles and social captions - Auto-posting to Instagram, TikTok, YouTube, Facebook, Threads, Twitter (X), LinkedIn, Pinterest, and Bluesky All of this is triggered by a simple message sent via Telegram. How the workflow works This end-to-end automation transforms a short Telegram message into a fully produced and published social video: - Receives a text prompt from Telegram - Transforms it into a detailed video scene using GPT-4 - Generates a cinematic video with Kling - Creates a voice-over script and converts it to audio - Merges the video and the audio - Adds styled captions - Writes a social caption and an SEO-optimized title - Saves metadata to Google Sheets - Sends a preview via Telegram - Publishes the video to 9 social platforms using Blotato Setup 1. Connect your Telegram bot to the "Telegram Trigger" node. 2. Add your OpenAI API key to all GPT-related nodes. 3. Configure Kling API access in the "Generate Video" node. 4. Link your Cloudinary account for audio upload. 5. Connect JSON2Video to handle video merging and captioning. 6. Set up Google Sheets with your preferred spreadsheet ID. 7. Connect your Blotato API key and fill in the platform IDs (Instagram, TikTok, etc.). Test by sending a Telegram message like:generate video A robot exploring Mars, Why AI will reshape humanityDisclaimer: This workflow uses Community Nodes, which are only available on self-hosted instances of n8n. How to customize this workflow to your needs - Change the visual style: Adjust the GPT-4 prompt formatting to reflect your brand tone. - Edit voice style: Replace the TTS provider or tweak OpenAI's voice settings. - Revise captions or titles: Fine-tune the system prompts in the "Create Description" or "Create Title" nodes. - Target fewer platforms: Disable or remove nodes for platforms you don’t use. - Add approval steps: Insert a Telegram confirmation step before auto-publishing.

Note: Now includes an Apify alternative for Rapid API (Some users can't create new accounts on Rapid API, so I have added an alternative for you. But immediately you are able to get access to Rapid API, please use that option, it returns more detailed data). Scroll to bottom for APify setup guide. This n8n workflow automates LinkedIn lead generation, enrichment, and activity analysis using Apollo.io, RapidAPI, Google Sheets, and Mail.so. Perfect for sales teams, founders, B2B marketers, and cold outreach pros who want personalized lead insights to drive better conversion rates. ⚙️ How This Workflow Works The workflow is broken down into several key steps, each designed to help you build and enrich a valuable list of LinkedIn leads: 1. 🔑 Lead Discovery (Keyword Search via Apollo) Pulls leads using Apollo.io's API based on keywords, industries, or job titles. Saves lead name, title, company, and LinkedIn URL to your Google Sheet. You can replace the trigger node from the form node to a webhook, WhatsApp, Telegram, etc., any way for you to send over your query variables over to initiate the workflow. 2. 🧠 Username Extraction (from LinkedIn URL) Extracts the LinkedIn username from profile URLs using a simple script node. This is required for further enrichment via RapidAPI. 3. ✉️ Email Lookup (via Apollo User ID) Uses the Apollo User ID to retrieve the lead’s verified work email. Ensures high-quality leads with reliable contact info. To double-check that the email is currently valid, we use the Mail.so API and filter out emails that fail deliverability and MX-record check. We don't want to risk sending emails to no longer existent addresses, right? 4. 🧾 Profile Summary Enrichment (via RapidAPI) Queries the LinkedIn Data API to fetch a lead’s profile summary/bio. Gives you a deeper understanding of their background and expertise. 5. 📰 Recent Activity Collection (Posts & Reposts) Retrieves recent posts or reposts from each lead’s profile. Great for tailoring outreach with reference to what they’re currently talking about. 6. 🗂️ Leads Database Update All enriched data is written to the same Google Sheet. New columns are filled in without overwriting existing data. ✅ Smart Retry & Row Status Logic Every subworkflow includes a fail-safe mechanism to ensure: - Each row has status columns (e.g., done, failed, pending). - A scheduled retry workflow resets failed rows to pending after 2 weeks (customizable). This gives failed enrichments another chance to be processed later, reducing data loss. 📋 Google Sheets Setup - Template 1: Apollo Leads Scraping & Enrichment - Template 2: Enriched Leads Database Make a copy to your Drive and use. Columns will be filled as each subworkflow runs (email, summary, interests, etc.). 🔐 Required API Keys To use this workflow, you’ll need the following credentials: - 🧩 Apollo.io Sign up and get your key here: Apollo.io API Keys ⚠️ Important: Toggle the “Master API Key” option to ON when generating your key. This ensures the same key can be used for all Apollo endpoints in this workflow. - 🌐 RapidAPI (LinkedIn Data API) Subscribe to the API here: LinkedIn Data API on RapidAPI Use the key in the x-rapidapi-key header in the relevant nodes. - ✉️ Mail.so Sign up and get your key here: Mail.so API 💡 For both APIs, set up the credentials in n8n as “Generic Credential” types. This way, you won’t need to reconfigure the headers in each node. 🛠️ Customization Options - Modify the Apollo filters (location, industry, seniority) to target your ideal customers. - Change retry interval in the scheduler (e.g., weekly instead of 2 weeks). - Connect the database to your email campaign tool like Mailchimp or Instantly.ai. - Replace the AI nodes with your desired AI agents and customize the system messages further to get desired results. 🆕 Apify Update Guide To use this workflow, you’ll need the following credentials: Login to Apify, then open this link; https://console.apify.com/actors/2SyF0bVxmgGr8IVCZ/ Click on integrations and scroll down to API Solutions and select "Use API endpoints". Scroll to "Run Actor synchronously and get dataset items" and copy the actor endpoint URL then paste it in the placeholder inside the HTTP node of Apify alternative flow "apify-actor-endpoint". That's it, you are set to go. I am available for custom n8n workflows; if you like my work, please get in touch with me on email at [email protected].

This template creates engaging social media posts by transforming Google Sheets data into captivating images using OpenAI. Then, share them on Facebook Pages effortlessly.

Who this is for? The Structured Data Extract & Data Mining workflow is crafted for researchers, content analysts, SEO strategists, and AI developers who need to transform semi-structured web data (like markdown content or scraped HTML) into actionable structured datasets. It is ideal for: - Content Analysts - Organizing and mining large volumes of markdown or HTML content. - SEO & Trend Researchers - Exploring topics by location and category. - AI Engineers & NLP Developers - Looking to automate insight extraction from unstructured inputs. - Growth Marketers - Tracking topic-level trends for strategic campaigns. - Automation Specialists - Streamlining workflows from scrape to storage. What problem is this workflow solving? Extracting insights from markdown or HTML documents typically requires manual review, formatting, and parsing. This becomes unscalable when dealing with large datasets or when real-time response is needed. Additionally, trend and topic extraction usually involves external tools, custom scripts, and inconsistent formatting. This workflow solves: - Automatic text extraction from markdown or structured content. - Location and category-based trend mining with semantic grouping. - AI-driven topic extraction and summarization. - Real-time notification via webhook with rich structured payloads. - Persistent storage of mined data to disk for audits or further processing. What this workflow does - Receives input: Sets the URL for the data extraction and analysis. - Uses Bright Data's Web Unlocker to extract content from relevant sites. - A Markdown/Text Extractor node parses the content into clean plaintext. - The cleaned data is passed to Google Gemini to: - Identify trends by location and category. - Extract key topics and themes. - Format the response into structured JSON. - The structured insights are sent via Webhook Notification to external systems (e.g., Slack, Web apps, Zapier). - The final output is saved to disk. How to customize this workflow to your needs - Update Source: Update the workflow input to read from Google Sheet or Airbase for dynamically tracking multiple brands or topics. - Gemini Prompt Customization: - Extract trends within a custom category (e.g., E-commerce design patterns in the US). - Output topics with popularity metrics. - Structure the output as per your database schema (e.g., [{ topic, trend_score, location }]). - Webhook Output: Send notifications to: - Slack – with AI summaries in rich blocks. - Internal APIs – for use in dashboards. - Zapier/Make – for multi-step automation. Persistence - Save output to: - Remote FTP or SFTP storage. - Amazon S3, Google Cloud Storage, etc.

From PDF to Powerful Blog Posts: AI-Powered Content Transformation Turn complex documents into engaging digital content that drives results. This n8n Workflow uses AI to transform lengthy PDFs into compelling blog posts that attract and retain readers while you focus on strategic initiatives. Time-Saving Innovation 🚅 Lightning-Fast Processing Transform lengthy documents into polished blog content in under 1 minute, eliminating hours of manual work. Our system handles the heavy lifting, delivering up to a 95% reduction in content production time. 📱 Intelligent Analysis The AI engine identifies and extracts key insights, organizing information for maximum impact. Each document undergoes comprehensive analysis to ensure no valuable content is overlooked. Advanced Content Optimization ✍️ Dynamic Writing Styles Possible Adjust the prompt for multiple tone options: - Professional for corporate communications - Conversational for engaging blogs - Thought leadership for industry authority 📊 SEO-Ready Content Potential Adjust the prompt to automatically optimize for search engines, incorporating relevant keywords and semantic structure to improve visibility and drive organic traffic. Ideal Applications 🤼 Content Marketing Teams Scale content production without sacrificing quality or consistency. Perfect for teams looking to maintain a robust publishing schedule while maximizing resource efficiency. 🏫 Academic Communication Help researchers and institutions share complex findings with broader audiences through accessible, engaging content that maintains academic integrity. 🧑‍💻 Digital Publishers Streamline the content transformation process while ensuring each piece meets modern digital standards and reader expectations. Transform your content strategy with an intelligent system that delivers consistent, high-quality results while dramatically reducing production time.

Automatically generate Google Docs from 2 ChatGPT prompt completions triggered by new RSS articles. Streamline content creation with AI-driven insights and document management.