by ConvertAPI
Who is this for? For developers and organizations that need to convert DOCX files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the DOCX file from the web. Converts the DOCX file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.
by ConvertAPI
Who is this for? For developers and organizations that need to convert image files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the JPG file from the web. Converts the JPG file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.
by ConvertAPI
Who is this for? For developers and organizations that need to convert PDF files to PDFA for long term archiving. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the PDF file from the web. Converts the PDF file to PDFA. Stores the PDFA file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.
by Angel Menendez
Introducing the Qualys Scan Slack Report Subworkflow—a robust solution designed to automate the generation and retrieval of security reports from the Qualys API. This workflow is a sub workflow of the Qualys Slack Shortcut Bot workflow. It is triggered when someone fills out the modal popup in slack generated by the Qualys Slack Shortcut Bot. When deploying this workflow, use the Demo Data node to simulate the data that is input via the Execute Workflow Trigger. That data flows into the Global Variables Node which is then referenced by the rest of the workflow. It includes nodes to Fetch the Report IDs and then Launch a report, and then check the report status periodically and download the completed report, which is then posted to Slack for easy access. For Security Operations Centers (SOCs), this workflow provides significant benefits by automating tedious tasks, ensuring timely updates, and facilitating efficient data handling. How It Works Fetch Report Templates:** The "Fetch Report IDs" node retrieves a list of available report templates from Qualys. This automated retrieval saves time and ensures that the latest templates are used, enhancing the accuracy and relevance of reports. Convert XML to JSON:** The response is converted to JSON format for easier manipulation. This step simplifies data handling, making it easier for SOC analysts to work with the data and integrate it into other tools or processes. Launch Report:** A POST request is sent to Qualys to initiate report generation using specified parameters like template ID and report title. Automating this step ensures consistency and reduces the chance of human error, improving the reliability of the reports generated. Loop and Check Status:** The workflow loops every minute to check if the report generation is complete. Continuous monitoring automates the waiting process, freeing up SOC analysts to focus on higher-priority tasks while ensuring they are promptly notified when reports are ready. Download Report:** Once the report is ready, it is downloaded from Qualys. Automated downloading ensures that the latest data is always available without manual intervention, improving efficiency. Post to Slack:** The final report is posted to a designated Slack channel for quick access. This integration with Slack ensures that the team can promptly access and review the reports, facilitating swift action and decision-making. Get Started Ensure your Slack and Qualys integrations are properly set up. Customize the workflow to fit your specific reporting needs. Link to parent workflow Link to Vulnerability Scan Trigger Need Help? Join the discussion on our Forum or check out resources on Discord! Deploy this workflow to streamline your security report generation process, improve response times, and enhance the efficiency of your security operations.
by Jenny
Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection 1. This is the first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification 1. This is the first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. [This workflow] Batch Uploading Images Dataset to Qdrant This template imports dataset images from Google Could Storage, creates Voyage AI embeddings for them in batches, and uploads them to Qdrant, also in batches. In this particular template, we work with crops dataset. However, it's analogous to uploading lands dataset, and in general, it's adaptable to any dataset consisting of image URLs (as the following pipelines are). First, check for an existing Qdrant collection to use; otherwise, create it here. Additionally, when creating the collection, we'll create a payload index, which is required for a particular type of Qdrant requests we will use later. Next, import all (dataset) images from Google Cloud Storage but keep only non-tomato-related ones (for anomaly detection testing). Create (per batch) embeddings for all imported images using the Voyage AI multimodal embeddings API. Finally, upload the resulting embeddings and image descriptors to Qdrant via batch upload.
by darrell_tw
This workflow automates the process of fetching agricultural transaction data from the Taiwan Agricultural Products Open Data Platform and storing it in a Google Sheets document for further analysis. Key Features Manual Trigger: Allows manual execution of the workflow to control when data is fetched. HTTP Request: Sends a request to the Open Data Platform's API to retrieve detailed transaction data, including: Pricing (Upper, Middle, Lower, Average) Transaction quantities Crop and market details Split Out Node: Processes each record individually, ensuring accurate handling of every data entry. Google Sheets Integration: Appends the data into a structured Google Sheets document for easy access and analysis. Node Configurations 1. Manual Trigger Purpose**: Start the workflow manually. Configuration**: No setup needed. 2. HTTP Request Purpose**: Fetch agricultural data. Configuration**: URL: https://data.moa.gov.tw/api/v1/SheepQuotation Query Parameters: Start_time: 2024/12/01 End_time: 2024/12/31 MarketName: 台北二 api_key: <your_api_key> Headers: accept: application/json 3. Split Out Purpose**: Split the API response data array into individual items. Configuration**: Field to Split Out: Data 4. Google Sheets Purpose**: Append the data to Google Sheets. Configuration**: Operation: Append Document ID: <your_document_id> Sheet Name: Sheet1 Mapped Fields: TransDate, TcType, CropCode, CropName, MarketCode, MarketName Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity 此 Workflow 從 台灣農業產品開放資料平臺 獲取農產品交易數據,並將其儲存到 Google Sheets 文件 中進行進一步分析。 主要功能 Manual Trigger:允許手動執行工作流程,以控制數據獲取的時間。 HTTP Request:向開放資料平臺的 API 發送請求,獲取詳細的交易數據,包括: 價格 (Upper, Middle, Lower, Average) 交易數量 作物和市場詳細資料 Split Out Node:逐筆處理每一筆記錄,確保數據準確無誤。 Google Sheets Integration:將數據追加到結構化的 Google Sheets 文件中,方便存取和分析。 節點設定 1. Manual Trigger 用途**:手動啟動工作流程。 設定**:無需額外設定。 2. HTTP Request 用途**:抓取農產品數據。 設定**: URL: https://data.moa.gov.tw/api/v1/SheepQuotation 查詢參數 (Query Parameters): Start_time: 2024/12/01 End_time: 2024/12/31 MarketName: 台北二 api_key: <your_api_key> 標頭 (Headers): accept: application/json 3. Split Out 用途**:將 API 回應的數據陣列分解為個別項目。 設定**: Field to Split Out: Data 4. Google Sheets 用途**:將數據追加至 Google Sheets。 設定**: Operation:Append Document ID:<your_document_id> Sheet Name:Sheet1 映射欄位 (Mapped Fields): TransDate, TcType, CropCode, CropName, MarketCode, MarketName Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity 請多利用 Curl Import 功能 例如 curl -X GET "https://data.moa.gov.tw/api/v1/AgriProductsTransType/?Start_time=114.01.01&End_time=114.01.01&MarketName=%E5%8F%B0%E5%8C%97%E4%BA%8C" -H "accept: application/json" 農業資料開放平台 文件
by Harshil Agrawal
This workflow allows you to receive updates from Wise and add information of a transfer to a base in Airtable. Wise Trigger node: This node will trigger the workflow when the status of your transfer changes. Wise node: This node will get the information about the transfer. Set node: We use the Set node to ensure that only the data that we set in this node gets passed on to the next nodes in the workflow. We set the value of Transfer ID, Date, Reference, and Amount in this node. Airtable node: This node will append the data that we set in the previous node to a table.
by Davide
Drive-to-Store is a multi-channel marketing strategy that includes both the web and the physical context, with the aim of increasing the number of customers and sales in physical stores. This strategy guides potential customers from the online world to the physical point of sale through the provision of a coupon that can be spent in the store or on an e-commerce site. The basic idea is to have a landing page with a form and a series of unique coupons to assign to leads as a "reward" for filling out the form. This workflow is ideal for businesses looking to automate lead generation and management, especially when integrating with CRM systems like SuiteCRM and using Google Sheets for data tracking. How It Works Form Submission: The workflow starts with the On form submission node, which triggers when a user submits a form on a landing page. The form collects the user's name, surname, email, and phone number. Form Data Processing: The Form Fields node extracts and sets the form data (name, surname, email, and phone) for use in subsequent steps. Duplicate Lead Check: The Duplicate Lead? node checks if the submitted email already exists in a Google Sheets document. If the email is found, the workflow responds with a "duplicate lead" message (Respond KO node) and stops further processing. Coupon Retrieval: If the email is not a duplicate, the Get Coupon node retrieves a coupon code from the Google Sheets document based on the lead's email. Lead Creation in SuiteCRM: The Create Lead SuiteCRM node creates a new lead in SuiteCRM using the form data and the retrieved coupon code. The lead includes: First name, last name, email, phone number, and coupon code. Google Sheets Update: The Update Sheet node updates the Google Sheets document with the newly created lead's details, including: Name, surname, email, phone, coupon code, lead ID, and the current date and time. Response to Webhook: The Respond OK node sends a success response back to the webhook, indicating that the lead was created successfully. Set Up Steps Configure Form Trigger: Set up the On form submission node to collect user data (name, surname, email, and phone) via a web form. Set Up Google Sheets Integration: Configure the Duplicate Lead?, Get Coupon, and Update Sheet nodes to interact with the Google Sheets document. Ensure the document contains columns for email, coupon, lead ID, and other relevant fields. Set Up SuiteCRM Authentication: Configure the Token SuiteCRM node with the appropriate client credentials (client ID and client secret) to obtain an access token from SuiteCRM. Set Up Lead Creation in SuiteCRM: Configure the Create Lead SuiteCRM node to send a POST request to SuiteCRM's API to create a new lead. Include the form data and coupon code in the request body. Set Up Webhook Responses: Configure the Respond OK and Respond KO nodes to send appropriate JSON responses back to the webhook based on whether the lead was created or if it was a duplicate. Test the Workflow: Submit a test form to ensure the workflow correctly checks for duplicates, retrieves a coupon, creates a lead in SuiteCRM, and updates the Google Sheets document. Activate the Workflow: Once tested, activate the workflow to automate the process of handling form submissions and lead creation. Key Features Duplicate Lead Check**: Prevents duplicate leads by checking if the email already exists in the Google Sheets document. Coupon Assignment**: Retrieves a coupon code from Google Sheets and assigns it to the new lead. SuiteCRM Integration**: Automatically creates a new lead in SuiteCRM with the form data and coupon code. Data Logging**: Logs all lead details in a Google Sheets document for tracking and analysis. Webhook Responses**: Provides immediate feedback on whether the lead was created successfully or if it was a duplicate.
by Jan Oberhauser
Trigger on new Typeform form submission Write data to Google Sheet Check severity of problem If very severe post message to Slack If not so severe just send an email Assumptions Google Sheet Sheet in Spreadsheet called "Problems". Columns Names: Name Email Severity Problem Example Sheet: https://docs.google.com/spreadsheets/d/17fzSFl1BZ1njldTfp5lvh8HtS0-pNXH66b7qGZIiGRU Typeform Typeform formular with questions named exactly like the columns of the Google Sheet.
by Angel Menendez
CallForge - AI Gong Sales Call Processing Workflow Automate your Gong.io sales call analysis with AI-driven insights, real-time tracking, and structured CRM integration. Who is This For? This workflow is designed for: ✅ Sales teams looking to automate sales call processing. ✅ Revenue operations (RevOps) professionals managing high volumes of call data. ✅ AI-driven sales intelligence teams using Gong.io for data-driven insights. What Problem Does This Workflow Solve? Manually managing and analyzing large volumes of Gong call data is time-consuming and error-prone. With CallForge, you can: ✔ Automate call processing to scale AI-driven insights. ✔ Integrate with Notion to track and organize sales call data efficiently. ✔ Get real-time Slack updates to stay informed on call processing progress. ✔ Handle API failures gracefully, allowing easy reruns if a rate limit is hit. ✔ Ensure AI-ready analysis, feeding structured call data into an AI-powered system. What This Workflow Does 1. Triggers on New Gong Calls Captures new Gong calls and retrieves metadata, call summaries, and participant details. 2. Compares Calls Against Notion Database Checks whether the call has already been processed and stored in Notion. Prevents duplicate entries** from being added. 3. Creates a Parent Notion Record for AI Processing Stores call details such as date, title, URL, company name, sales rep, and opportunity details in Notion. Links calls to Salesforce Opportunity (SF Opp) data. Assigns sales representatives and customer information to each call. 4. Loops Through Calls for Processing Ensures resilience* by allowing failed runs to *restart where they left off**. Processes calls one at a time to prevent Notion rate limits. 5. Sends Call Data to an AI Processor Extracts structured call details and sends them to an AI-powered analysis workflow. Allows multiple AI agents to process and extract structured data from calls. 6. Provides Real-Time Slack Alerts Posts a progress update in Slack when the queue starts processing. Sends real-time call progress notifications. Sends a completion alert once all calls are processed. How to Set Up This Workflow 1. Connect Your APIs 🔹 Gong API Credentials – Ensure you have valid Gong API credentials in n8n. 🔹 Notion Database – Provide access to a Notion database for storing call insights. 🔹 Slack Integration – Configure a Slack channel for progress alerts. 🔹 AI Processing Workflow – Connect an AI-powered call processing workflow for final analysis. CallForge - 01 - Filter Gong Calls Synced to Salesforce by Opportunity Stage CallForge - 02 - Prep Gong Calls with Sheets & Notion for AI Summarization CallForge - 03 - Gong Transcript Processor and Salesforce Enricher CallForge - 04 - AI Workflow for Gong.io Sales Calls CallForge - 05 - Gong.io Call Analysis with Azure AI & CRM Sync CallForge - 06 - Automate Sales Insights with Gong.io, Notion & AI CallForge - 07 - AI Marketing Data Processing with Gong & Notion CallForge - 08 - AI Product Insights from Sales Calls with Notion How to Customize This Workflow 💡 Modify Call Storage – Swap Notion for a different CRM or database (e.g., HubSpot, Airtable, Salesforce). 💡 Change AI Processing – Integrate a custom AI model for analyzing sales conversations. 💡 Customize Slack Notifications – Adjust Slack messages or send alerts via email instead. 💡 Expand with More Integrations – Connect with Salesforce, Pipedrive, or HubSpot for further enrichment. Why Use CallForge? 🚀 Automate Gong call tracking for seamless sales intelligence. 📊 Improve sales operations with structured, AI-powered insights. ⚡ Get real-time updates and keep your team informed instantly. Start optimizing your Gong call processing today!
by Sami Abid
This workflow will trigger daily at 6am to retrieve your day's calendar events from Google Calendar and send them as a summary message to Slack. I've used a low-code method to filter the dates as I can't code much in JSON :) Contact me on https://twitter.com/sami_abid if you have any questions!
by Angel Menendez
CallForge - AI-Powered Marketing Insights Extraction from Sales Calls Automate marketing intelligence gathering from AI-analyzed sales calls and store insights in Notion. 🎯 Who is This For? This workflow is designed for: ✅ Marketing teams looking to extract trends and insights from sales conversations. ✅ Product managers who need direct customer feedback from sales calls. ✅ Revenue operations (RevOps) teams optimizing AI-driven call analysis. It streamlines AI-powered marketing intelligence, identifying customer pain points, competitor mentions, and recurring trends—all automatically stored in Notion. 🔍 What Problem Does This Workflow Solve? Manually reviewing sales call transcripts for marketing insights is time-consuming and inconsistent. With CallForge, you can: ✔ Extract key marketing insights from AI-analyzed sales calls. ✔ Track recurring discussion topics across multiple conversations. ✔ Generate actionable marketing recommendations for strategy and content. ✔ Store structured insights in Notion for seamless access. This automation eliminates manual work and ensures marketing teams get data-driven insights from real customer conversations. 📌 Key Features & Workflow Steps 🎙️ AI-Driven Marketing Insights Processing This workflow processes AI-generated sales call insights and organizes them in Notion databases: Triggers when AI sales call data is received. Identifies marketing-related data (trends, customer pain points, competitor mentions). Extracts key marketing insights, categorizing product discussions and recurring topics. Logs trends across multiple calls, ensuring marketing teams spot recurring themes. Processes actionable insights, capturing marketing strategy recommendations. Stores all findings in Notion, enabling structured, searchable insights. 📊 Notion Database Integration Marketing Insights** → Logs key trends and product mentions from sales calls. Recurring Topics** → Tracks frequently discussed themes across calls. Actionable Recommendations** → Stores AI-generated recommendations for marketing teams. 🛠 How to Set Up This Workflow 1. Prepare Your AI Call Analysis Data Ensure AI-generated sales call insights are available. Compatible with Gong, Fireflies.ai, Otter.ai, and other AI transcription tools. 2. Connect Your Notion Database Set up Notion databases for: 🔹 Marketing Insights (logs trends and product mentions) 🔹 Recurring Topics (tracks frequently discussed customer concerns) 🔹 Actionable Recommendations (stores marketing strategy insights) 3. Configure n8n API Integrations Connect your Notion API key** in n8n under “Notion API Credentials.” Set up webhook triggers** to receive AI-generated sales insights. Test the workflow** using a sample AI sales call analysis. 🔧 How to Customize This Workflow 💡 Modify Notion Data Structure – Adjust fields to match marketing strategy needs. 💡 Refine AI Data Processing Rules – Customize what insights are extracted and logged. 💡 Integrate with Slack or Email – Notify teams when key marketing trends emerge. 💡 Expand CRM Integration – Sync insights with HubSpot, Salesforce, or Pipedrive. CallForge - 01 - Filter Gong Calls Synced to Salesforce by Opportunity Stage CallForge - 02 - Prep Gong Calls with Sheets & Notion for AI Summarization CallForge - 03 - Gong Transcript Processor and Salesforce Enricher CallForge - 04 - AI Workflow for Gong.io Sales Calls CallForge - 05 - Gong.io Call Analysis with Azure AI & CRM Sync CallForge - 06 - Automate Sales Insights with Gong.io, Notion & AI CallForge - 07 - AI Marketing Data Processing with Gong & Notion CallForge - 08 - AI Product Insights from Sales Calls with Notion ⚙️ Key Nodes Used in This Workflow 🔹 If Nodes – Detect if marketing insights, recurring topics, or recommendations exist in AI data. 🔹 Notion Nodes – Create and update entries in Notion databases. 🔹 Split Out & Aggregate Nodes – Process multiple insights and consolidate AI-generated data. 🔹 Wait Nodes – Ensure smooth sequencing of API calls and database updates. 🚀 Why Use This Workflow? ✔ Eliminates manual sales call review for marketing teams. ✔ Provides structured, AI-driven insights for marketing and product strategy. ✔ Tracks competitor mentions and customer pain points automatically. ✔ Improves content marketing and campaign planning with real customer insights. ✔ Scalable for teams using n8n Cloud or self-hosted deployments. This workflow empowers marketing teams by transforming sales call data into actionable intelligence, streamlining strategy, content planning, and competitor analysis. 🚀