Agents & Automations

In this AI Tools 101 course template, learn how to scrape web content efficiently using AI and Google Sheets. Extract, structure, and save data seamlessly for enhanced information management and analysis.

Welcome to my Automated Image Metadata Tagging Workflow! This workflow automatically analyzes the image content with the help of AI and writes it directly back into the image file as keywords. This workflow has the following sequence: - Google Drive trigger (scan for new files added in a specific folder) - Download the added image file - Analyze the content of the image and extract the file as Base64 code - Merge Metadata and Base64 Code - Code Node to write the Keywords into the Metadata (dc:subject) - Convert to file and update the original file in the Google Drive folder The following accesses are required for the workflow: - Google Drive: Documentation - AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) You can contact me via LinkedIn if you have any questions.

Automatically send data to Google Sheets using ChatGPT after converting the HTML to text received from HTTP. Add rows for streamlined data management and analysis.

This template creates a Telegram AI Assistant that answers questions based on your documents, powered by Google Gemini and Supabase. Key features include Intelligent HTML Post-processing for rich formatting in Telegram and Adaptive Message Chunking to handle long text responses. 📹 Watch the Bot in Action This video provides a live demonstration of the bot's core features and how it interacts. See a quick walkthrough of its capabilities and user flow. How it works: 1. User uploads a PDF document to a Telegram bot. 2. The workflow processes the PDF, creates embeddings using Google Gemini, and stores these embeddings in a Supabase vector table. 3. Users then ask questions to the bot. 4. The workflow performs a vector search in Supabase to find relevant document chunks based on the user's query. 5. Google Gemini uses the retrieved relevant chunks to generate an intelligent answer. 6. The bot sends the formatted answer back to the user on Telegram, utilizing HTML markup for enhanced presentation. Set up steps: Setup should take approximately 15-20 minutes. - Import the workflow into your n8n instance. - Configure credentials for Telegram, Google Gemini, and Supabase. - Set up your Supabase vector table using the provided SQL script. - Activate the workflow. Detailed setup instructions, including how to get API keys and configure nodes, are available in the sticky notes within the workflow itself.

Introduction The "High-Level Service Page SEO Blueprint Report" workflow is a powerful, AI-driven solution designed to generate comprehensive SEO content strategies for service-based businesses. By analyzing competitor websites and user intent, this workflow creates a detailed blueprint that outlines the optimal structure, content, and conversion elements for a service page. The workflow leverages the JINA Reader API to extract content from competitor websites and uses Google Gemini AI to perform deep analysis across multiple dimensions: competitor content structure, user intent, strategic opportunities, and conversion optimization. The final output is a professionally formatted Markdown document that provides actionable guidance for creating a high-performing service page that satisfies both user needs and search engine requirements. This workflow eliminates the time-consuming process of manually analyzing competitors and developing content strategies, providing a data-driven foundation for service page creation that would typically require hours of expert analysis. Who is this for? This workflow is designed for digital marketers, SEO specialists, content strategists, and web developers who need to create or optimize service pages for businesses. It's particularly valuable for marketing agencies and freelancers who regularly develop content strategies for clients across various industries. Users should have a basic understanding of SEO concepts, content marketing, and website structure. While technical SEO knowledge is beneficial, the workflow is designed to provide comprehensive guidance even for those with intermediate-level expertise. The ideal user is someone who wants to streamline their content planning process and ensure their service pages are built on data-driven insights rather than guesswork. What problem is this workflow solving? Creating effective service pages that rank well in search engines while converting visitors is a complex challenge that typically requires extensive competitive research, content planning, and conversion optimization expertise. This workflow addresses several key pain points: - Time-consuming competitor analysis: Manually analyzing multiple competitor websites to identify content patterns, heading structures, and meta tag strategies can take hours. - Difficulty identifying content gaps: Determining what topics competitors are missing that could provide a competitive advantage requires deep analysis and industry knowledge. - Balancing SEO and conversion elements: Creating content that satisfies both search engines and user needs while driving conversions is a delicate balance that many struggle to achieve. - Lack of structured approach: Many content creators work without a comprehensive blueprint, leading to inconsistent results and missed opportunities. - Difficulty translating analysis into actionable recommendations: Even when analysis is performed, turning those insights into a concrete content plan can be challenging. This workflow automates these processes, providing a structured, data-driven approach to service page creation that saves hours of research and planning time. What this workflow doesOverview The workflow takes a list of competitor URLs and a target keyword as input, then performs a multi-stage analysis to generate a comprehensive service page blueprint. It extracts and analyzes competitor content, evaluates user intent, identifies strategic opportunities, and creates detailed recommendations for page structure, content, and conversion elements. The final output is a professionally formatted Markdown document that serves as a complete roadmap for creating an effective service page. Process 1. Data Collection: The workflow begins with a form that collects essential information: competitor URLs, target keyword, services offered, brand name, and whether the page is a homepage. 2. Competitor Content Extraction: The workflow processes each competitor URL, using the JINA Reader API to extract the HTML content from each site. 3. Content Structure Analysis: For each competitor site, the workflow extracts and analyzes heading structures, meta tags, schema markup, and recurring phrases (n-grams). 4. Competitor Analysis Report: The AI synthesizes the competitive data to identify patterns in meta titles/descriptions, common outline sections, key heading concepts, and structural elements. 5. User Intent Analysis: The workflow analyzes the target keyword to determine primary and secondary user intents, user personas, and their position in the buyer's journey. 6. Gap Analysis: The AI identifies content overlaps ("table stakes"), content gaps (opportunities), SEO keyword priorities, and potential UX/conversion advantages. 7. Page Outline Generation: Based on the previous analyses, the workflow creates an optimal page structure with H1, H2s, H3s, and potentially H4s, with justifications for each section. 8. UX & Conversion Recommendations: The workflow adds detailed recommendations for calls-to-action, trust signals, copywriting tone, visual elements, and risk reversal strategies. 9. Final Blueprint Creation: All analyses and recommendations are compiled into a comprehensive, well-structured Markdown document that serves as a complete service page blueprint. How to customize this workflow to your needs - Adjust AI parameters: Modify the temperature settings in the Google Gemini Chat Model nodes to control creativity vs. precision in the AI outputs. - Customize extraction logic: Edit the "Extract HTML Elements" code node to focus on specific HTML elements that are most relevant to your industry or content type. - Modify analysis prompts: Customize the prompts in the various analysis nodes to focus on specific aspects of SEO or content strategy that are most important for your use case. - Add industry-specific guidance: Enhance the prompts with industry-specific instructions or examples to make the output more relevant to particular sectors. - Integrate with content management systems: Extend the workflow to automatically send the blueprint to content management systems, project management tools, or document storage platforms. - Add competitor scoring: Implement a scoring system to evaluate and rank competitors based on specific criteria relevant to your strategy. - Expand the analysis: Add additional analysis nodes to evaluate other aspects of competitor websites, such as page speed, mobile-friendliness, or backlink profiles.

Automatically update Google Docs with AI-generated content. Monitor document changes, retrieve content, generate text with ChatGPT, and create new documents.

Boost your customer support experience by reducing response time using ChatGPT! Categorize tickets seamlessly, allocate them to agents with relevant expertise, and revamp your customer care efficiency.

This workflow is designed to generate SEO-friendly content with DeepSeek R1 (or V3), publish it on WordPress, and update a Google Sheets document with the details of the created post. Below is a detailed analysis of what each node in the workflow does: How It WorksTriggering the Workflow: The workflow starts with a Manual Trigger node, which is activated when the user clicks "Test workflow" in the n8n interface. Fetching Data: The Get Ideas node retrieves data from a Google Sheets document. It reads a specific sheet and filters the data based on the "ID POST" column, returning the first matching row. Setting the Prompt: The Set your prompt node extracts the PROMPT field from the Google Sheets data and assigns it to a variable for use in subsequent steps. Generating Content: The Generate article node uses an AI model (DeepSeek) to create an SEO-friendly article based on the prompt. The article includes an introduction, 2-3 chapters, and a conclusion, formatted in HTML. The Generate title node uses the same AI model to generate a concise, SEO-optimized title for the article. Publishing on WordPress: The Create post on WordPress node creates a new draft post on WordPress using the generated title and article content. Generating and Uploading an Image: The Generate Image node creates a photorealistic image based on the article title using an AI model (OpenAI). The Upload image node uploads the generated image to WordPress as a media file. The Set Image node assigns the uploaded image as the featured image for the WordPress post. Updating Google Sheets: The Update Sheet node updates the original Google Sheets document with the post details, including the title, post ID, creation date, and row number. Set Up StepsConfigure Google Sheets Integration: Set up the Google Sheets node to connect to your Google account and specify the document ID and sheet name to read from and update. Set Up AI Models: Configure the OpenAI nodes (for generating the article, title, and image) with the appropriate API credentials and model settings (e.g., deepseek-reasoner for text generation). Configure WordPress Integration: Set up the WordPress node with your WordPress site's API credentials to allow creating posts and uploading media. Define the Prompt and Content Structure: In the Set your prompt node, ensure the prompt variable is correctly mapped to the data from Google Sheets. In the Generate article and Generate title nodes, define the instructions for the AI model to generate the desired content. Set Up Image Generation: Configure the Generate Image node with the appropriate prompt and image settings (e.g., size, quality, style). Configure HTTP Requests for Media Upload: Set up the Upload image and Set Image nodes to use the WordPress REST API for uploading and assigning the featured image. Map Data for Google Sheets Update: In the Update Sheet node, map the relevant fields (e.g., title, post ID, date) to the appropriate columns in the Google Sheets document. Test and Activate the Workflow: Run the workflow manually to ensure all steps execute correctly. Once verified, activate the workflow for automated execution. Overall purpose of the workflow This workflow automates the creation of SEO-friendly content for a WordPress blog. Starting from a prompt extracted from a Google Sheets document, it generates an article, a title, and an image, publishes the post on WordPress, and updates the Google Sheets document with the details of the created post. This process is useful for blog managers who want to automate content creation and publishing. Need help customizing? Contact me for consulting and support or add me on LinkedIn.

This template creates a powerful Retrieval Augmented Generation (RAG) AI agent workflow in n8n. It monitors a specified Google Drive folder for new PDF files, extracts their content, generates vector embeddings using Cohere, and stores these embeddings in a Milvus vector database. Subsequently, it enables a RAG agent that can retrieve relevant information from the Milvus database based on user queries and generate responses using OpenAI, enhanced by the retrieved context. Functionality The workflow automates the process of ingesting documents into a vector database for use with a RAG system. - Watch New Files: Triggers when a new file (specifically targeting PDFs) is added to a designated Google Drive folder. - Download New: Downloads the newly added file from Google Drive. - Extract from File: Extracts text content from the downloaded PDF file. - Default Data Loader / Set Chunks: Processes the extracted text, splitting it into manageable chunks for embedding. - Embeddings Cohere: Generates vector embeddings for each text chunk using the Cohere API. - Insert into Milvus: Inserts the generated vector embeddings and associated metadata into a Milvus vector database. - When chat message received: Adapt the trigger tool to fit your needs. - RAG Agent: Orchestrates the RAG process. - Retrieve from Milvus: Queries the Milvus database with the user's chat query to find the most relevant chunks. - Memory: Manages conversation history for the RAG agent to optimize cost and response speed. - OpenAI / Cohere embeddings: Uses ChatGPT 4o for text generation. Requirements To use this template, you will need: - An n8n instance (cloud or self-hosted). - Access to a Google Drive account to monitor a folder. - A Milvus instance or access to a Milvus cloud service like Zilliz. - A Cohere API key for generating embeddings. - An OpenAI API key for the RAG agent's text generation. Usage 1. Set up the required credentials in n8n for Google Drive, Milvus, Cohere, and OpenAI. 2. Configure the "Watch New Files" node to point to the Google Drive folder you want to monitor for PDFs. 3. Ensure your Milvus instance is running and the target cluster is set up correctly. 4. Activate the workflow. 5. Add PDF files to the monitored Google Drive folder. The workflow will automatically process them and insert their embeddings into Milvus. 6. Interact with the RAG agent. The agent will use the data in Milvus to provide context-aware answers. Benefits - Automates document ingestion for RAG applications. - Leverages Milvus for high-performance vector storage and search. - Uses Cohere for generating high-quality text embeddings. - Enables building a context-aware AI agent using your own documents. Suggested improvements - Support for More File Types: Extend the "Watch New Files" node and subsequent extraction steps to handle various document types (e.g., .docx, .txt, .csv, web pages) in addition to PDFs. - Error Handling and Notifications: Implement robust error handling for each step of the workflow (e.g., failed downloads, extraction errors, Milvus insertion failures) and add notification mechanisms (e.g., email, Slack) to alert the user.

Track your Google keyword rankings effortlessly with a prebuilt Browse AI robot. This template fetches the top 10 rankings, logs the data into a Google Sheet, and instantly emails the sheet to relevant stakeholders. Ensure your robot is configured to fetch 10 rankings for optimal performance.

LinkedIn lead generation workflow using HARPA and Make.com that collects leads from LinkedIn Groups and enriches your database with profile information. You can also extract employee data and profiles from LinkedIn Search results. All collected data is stored in a Google Sheet. You can connect it to your CRM, database, Airtable, or other systems.

🧠 This workflow is designed for one purpose only: to bulk-upload structured JSON articles from an FTP server into a Qdrant vector database for use in LLM-powered semantic search, RAG systems, or AI assistants. The JSON files are pre-cleaned and contain metadata and rich text chunks, ready for vectorization. This workflow handles: - Downloading from FTP - Parsing & splitting - Embedding with OpenAI-embedding - Storing in Qdrant for future querying JSON structure format for blog articles:json { "id": "article_001", "title": "reseguider", "language": "sv", "tags": ["london", "resa", "info"], "source": "alltomlondon.se", "url": "https://...", "embedded_at": "2025-04-08T15:27:00Z", "chunks": [ { "chunk_id": "article_001_01", "section_title": "Introduktion", "text": "Välkommen till London..." }, ... ] } 🧰 Benefits ✅ Automated Vector Loading Handles FTP → JSON → Qdrant in a hands-free pipeline. ✅ Clean Embedding Input Supports pre-validated chunks with metadata: titles, tags, language, and article ID. ✅ AI-Ready Format Perfect for Retrieval-Augmented Generation (RAG), semantic search, or assistant memory. ✅ Flexible Architecture Modular and swappable: FTP can be replaced with GDrive/Notion/S3, and embeddings can switch to local models like Ollama. ✅ Community Friendly This template helps others adopt best practices for vector DB feeding and LLM integration.

Important Notes: Check Legal Regulations: This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry! Workflow Description: 😮‍💨 Tired of struggling with XPath, CSS selectors, or DOM specificity when scraping? This AI-powered solution is here to simplify your workflow! With a vision-based AI Agent, you can extract data effortlessly without worrying about how the DOM is structured. This workflow leverages a vision-based AI Agent, integrated with Google Sheets, ScrapingBee, and the Gemini-1.5-Pro model, to extract structured data from webpages. The AI Agent primarily uses screenshots for data extraction but switches to HTML scraping when necessary, ensuring high accuracy. Key Features: - Google Sheets Integration: Manage URLs to scrape and store structured results. - ScrapingBee: Capture full-page screenshots and retrieve HTML data for fallback extraction. - AI-Powered Data Parsing: Use Gemini-1.5-Pro for vision-based scraping and a Structured Output Parser to format extracted data into JSON. - Token Efficiency: HTML is converted to Markdown to optimize processing costs. This template is designed for e-commerce scraping but can be customized for various use cases.

This template streamlines your Instagram content posting workflow by connecting Google Drive for image storage, using OpenAI for AI-generated captions, and leveraging Facebook Graph API for automated publishing. Pre-requisites Before setting up this workflow, ensure you have: - A Google account with access to Google Drive - An OpenAI API key for AI caption generation - A Facebook Developer account with Instagram Graph API access - An Instagram Business or Creator account connected to a Facebook Page - n8n.io account with workflow access Setup InstructionsConfigure Data Source Create a Google Sheet with the following columns: - Name: Filename of your image in Google Drive - Caption: Optional custom caption (leave empty for AI-generated captions) - URL: your Video Reel or Image in Google Drive Connect Google Drive - Add your Google Drive credentials in the "Google Drive" node - Specify the folder path where your Instagram image/Video are stored - Configure the node to retrieve image files based on filenames from your Google Sheet Set Up OpenAI Integration - Add your OpenAI API key to the credentials - Configure the OpenAI node to generate engaging captions based on image content - Adjust temperature and model parameters for desired creativity level Configure Facebook Graph API - Connect your Facebook account with Instagram access - Set up the Facebook Graph API node to post to your Instagram Business/Creator account - Ensure proper image formatting (1:1, 4:5, or 16:9 aspect ratios supported by Instagram) Workflow Automation Setup - Configure the scheduler node to run at your preferred frequency - Set up error handling to notify you of any posting failures - Add conditional nodes to use either custom or AI-generated captions Execution Instructions After completing all connections, test the workflow with a single image. Monitor the execution in the n8n dashboard to ensure proper functioning. View the "Executions" tab to track successful posts and troubleshoot any errors. Adjust posting frequency and scheduling as needed. This template saves hours of manual Instagram posting work while maintaining an authentic presence. Perfect for social media managers, content creators, and businesses looking to maintain consistent Instagram activity without the daily manual effort. The workflow handles image retrieval, caption generation or customization, proper Instagram API formatting, scheduled posting, and execution tracking - all in one automated solution.

This n8n workflow implements a version of the Adaptive Retrieval-Augmented Generation (RAG) framework. It recognizes that the best way to retrieve information often depends on the type of question asked. Instead of a one-size-fits-all approach, this workflow adapts its strategy based on the user's query intent. 🌟 How it WorksReceive Query: Takes a user query as input (along with context like a chat session ID and Vector Store collection ID if used as sub-workflow). Classify Query: First, the workflow classifies the query into a predefined category. This template uses four examples: - Factual: For specific facts. - Analytical: For deeper explanations or comparisons. - Opinion: For subjective viewpoints. - Contextual: For questions relying on specific background. Select & Adapt Strategy: Based on the classification, it selects a corresponding strategy to prepare for information retrieval. The example strategies aim to: - Factual: Refine the query for precision. - Analytical: Break the query into sub-questions for broad coverage. - Opinion: Identify different viewpoints to look for. - Contextual: Incorporate implied or user-specific context. Retrieve Info: Uses the output of the selected strategy to search the specified knowledge base (Qdrant vector store - change as needed) for relevant documents. Generate Response: Constructs a response using the retrieved documents, guided by a prompt tailored to the original query type. By adapting the retrieval strategy, this workflow aims to provide more relevant results tailored to the user's intent. 🛠️ RequirementsCredentials: You will need API credentials configured in your n8n instance for: - Google Gemini (AI Models) - Qdrant (Vector Store)

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI but the decks are unparseable through conventional means? Then you're in luck! This n8n template uses Multimodal LLMs to parse and extract valuable data from even the most overly designed pitch decks in quick fashion. Not only that, it'll also create the foundations of a RAG chatbot at the end so you or your colleagues can drill down into the details if needed. With this template, you'll scale your capacity to find interesting companies you'd otherwise miss! How It Works Airtable is used as the pitch deck database and PDF decks are downloaded from it. An AI Vision model is used to transcribe each page of the pitch deck into markdown. An Information Extractor is used to generate a report from the transcribed markdown and update required information back into the pitch deck database. The transcribed markdown is also uploaded to a vector store to build an AI chatbot which can be used to ask questions on the pitch deck. How To Use This template depends on the availability of Airtable - make a duplicate of the Airtable and its columns before running the workflow. When a new pitch deck is received, enter the company name into the Name column and upload the PDF into the File column. Leave all other columns blank. If you have the Airtable trigger active, the execution should start immediately once the file is uploaded. Otherwise, click the manual test trigger to start the workflow. When manually triggered, all "new" pitch decks will be handled by the workflow as separate executions. Requirements - OpenAI for LLM - Airtable For Database and Interface - Qdrant for Vector Store Customising This Workflow Extend this starter template by adding more AI agents to validate claims made in the pitch deck, e.g., LinkedIn Profiles, Page visits, Reviews, etc.

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

Overview This workflow provides Retell agent builders with a simple way to populate dynamic variables using n8n. The workflow fetches user information from a Google Sheet based on the phone number and sends it back to Retell. It is based on Retell's Inbound Webhook Call. Retell is a service that lets you create Voice Agents that handle voice calls simply, based on a prompt or using a conversational flow builder. Who is it for For builders of Retell's Voice Agents who want to make their agents more personalized. Prerequisites - Have a Retell AI Account - Create a Retell agent - Purchase a phone number and associate it with your agent - Create a Google Sheets - for example, make a copy of this one. Your Google Sheet must have at least one column with the phone number. The remaining columns will be used to populate your Retell agent’s dynamic variables. All fields are returned as strings to Retell (variables are replaced as text). How it works The webhook call is received from Retell. We filter the call using their whitelisted IP address. It extracts data from the webhook call and uses it to retrieve the user from Google Sheets. It formats the data in the response to match Retell's expected format. Retell uses this data to replace dynamic variables in the prompts. How to use it See the description for screenshots! Set the webhook name (keep it as POST). Copy the Webhook URL (e.g., https://your-instance.app.n8n.cloud/webhook/retell-dynamic-variables) and paste it into Retell's interface. Navigate to "Phone Numbers", click on the phone number, and enable "Add an inbound webhook". In your prompt (e.g., "welcome message"), use the variable with this syntax: {{variable_name}} (see Retell's documentation). These variables will be dynamically replaced by the data in your Google Sheet. Notes In Google Sheets, the phone number must start with '+'. Phone numbers must be formatted like the example: with the +, extension, and no spaces. You can use any database—just replace Google Sheets with your own, making sure to keep the phone number formatting consistent. 👉 Reach out to us if you're interested in analysing your Retell Agent conversations.

This n8n template allows you to use AI to generate logos or images which mimic visual styles of other logos or images. The model used to generate the images is Google's Imagen 3.0. With this template, users will be able to automate design and marketing tasks such as creating variants of existing designs, remixing existing assets to validate different styles, and exploring a range of designs which would have been otherwise too expensive and time-consuming previously. How it works A form trigger is used to capture the source image to reference styles from and a prompt for the target image to generate. The source image is passed to Gemini 2.0 to be analyzed and its visual style and tone extracted as a detailed description. This visual style description is then combined with the user's initial target image prompt. This final prompt is given to Imagen 3.0 to generate the images. A quick webpage is put together with the generated images to present back to the user. If the user provided an email address, a copy of this HTML page will be sent. How to use Ensure the workflow is live to share the form publicly. The source image must be accessible to your n8n instance - either a public image on the internet or within your network. For best results, select a source image which has a strong visual identity as these will allow the LLM to better describe it. For your prompt, refer to the Imagen prompt guide. Requirements - Gemini for LLM and Imagen model. - Cloudinary for image CDN. - Gmail for email sending. Customizing this workflow Feel free to swap any of these out for tools and services you prefer. Want to fully automate? Switch the form trigger for a webhook trigger!

AI-Powered Tech Radar Advisor This project is built on top of the famous open source ThoughtWorks Tech Radar. You can use this template to build your own AI-Powered Tech Radar Advisor for your company or group of companies. Target Audience This template is perfect for: - Tech Audit & Governance Leaders: Those seeking to build a tech landscape AI platform portal. - Tech Leaders & Architects: Those aiming to provide modern AI platforms that help others understand the rationale behind strategic technology adoption. - Product Managers: Professionals looking to align product innovation with the company's current tech trends. - IT & Engineering Teams: Teams that need to aggregate, analyze, and visualize technology data from multiple sources efficiently. - Digital Transformation Experts: Innovators aiming to leverage AI for actionable insights and strategic recommendations. - Data Analysts & Scientists: Individuals who want to combine structured SQL analysis with advanced semantic search using vector databases. - Developers: Those interested in integrating RAG chatbot functionality with conversation storage. 1. Description Tech Constellation is an AI-powered Tech Radar solution designed to help organizations visualize and steer their technology adoption strategy. It seamlessly ingests data from a Tech Radar Google Sheet—converting it into both a MySQL database and a vector index—to consolidate your tech landscape in one place. The platform integrates an interactive AI chat interface powered by four specialized agents: - AI Agent Router: Analyzes and routes user queries to the most suitable processing agent. - SQL Agent: Executes precise SQL queries on structured data. - RAG Agent: Leverages semantic, vector-based search for in-depth insights. - Output Guardrail Agent: Validates responses to ensure they remain on-topic and accurate. This powerful template is perfect for technology leaders, product managers, engineering teams, and digital transformation experts looking to make data-driven decisions aligned with strategic initiatives across groups of parent-child companies. 2. Features - Data Ingestion: A Google Sheet containing tech radar data is used as the primary source. The data is ingested and converted into a MySQL database. Simultaneously, the data is indexed into a vector database for semantic (vector-based) search. - Interactive AI Chat: An AI-powered chat interface allows users to ask questions about the tech radar. - Customizable AI Agents: - AI Agent Router: Determines the query type and routes it to the appropriate agent. - SQL Agent: Processes queries using SQL on structured data. - RAG Agent: Performs vector-based searches on document-like data. - Output Guardrail Agent: Validates queries and ensures that the responses remain on-topic and accurate. Usage Examples - Tell me, is TechnologyABC adopted or on hold, and why? - List all the tools that are considered part of the strategic direction for company3 but are not adopted.

This workflow automates AI-based image generation using the Fal.ai Flux API. Define custom prompts, image parameters, and effortlessly generate, monitor, and save the output directly to Google Drive. Streamline your creative automation with ease and precision. Who is this for? This template is for content creators, developers, automation experts, and creative professionals looking to integrate AI-based image generation into their workflows. It’s ideal for generating custom visuals with the Fal.ai Flux API and automating storage in Google Drive. What problem is this workflow solving? Manually generating AI-based images, checking their status, and saving results can be tedious. This workflow automates the entire process — from requesting image generation, monitoring its progress, downloading the result, and saving it directly to a Google Drive folder. What this workflow does 1. Sets Custom Image Parameters: Allows you to define the prompt, resolution, guidance scale, and steps for AI image generation. 2. Sends a Request to Fal.ai: Initiates the image generation process using the Fal.ai Flux API. 3. Monitors Image Status: Checks for completion and waits if needed. 4. Downloads the Generated Image: Fetches the completed image once ready. 5. Saves to Google Drive: Automatically uploads the generated image to a specified Google Drive folder. Setup 1. Prerequisites: - Fal.ai API Key: Obtain it from the Fal.ai platform and set it as the Authorization header in HTTP Header Auth credentials. - Google Drive OAuth Credentials: Connect your Google Drive account in n8n. 2. Configuration: - Update the “Edit Fields” node with your desired image parameters: - Prompt: Describe the image (e.g., “Thai young woman net idol 25 yrs old, walking on the street”). - Width/Height: Define image resolution (default: 1024x768). - Steps: Number of inference steps (e.g., 30). - Guidance Scale: Controls image adherence to the prompt (e.g., 3.5). - Set your Google Drive folder ID in the “Google Drive” node to save the image. 3. Run the Workflow: - Trigger the workflow manually to generate the image. - The workflow waits, checks status, and saves the final output seamlessly. Customization - Modify Image Parameters: Adjust the prompt, resolution, steps, and guidance scale in the “Edit Fields” node. - Change Storage Location: Update the Google Drive node with a different folder ID. - Add Notifications: Integrate an email or messaging node to alert you when the image is ready. - Additional Outputs: Expand the workflow to send the generated image to Slack, Dropbox, or other platforms. This workflow streamlines AI-based image generation and storage, offering flexibility and customization for creative automation.