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Who is this for? This workflow is designed for IT teams, service desk personnel, and incident management professionals who need a streamlined way to monitor and report on recent ServiceNow incidents directly within Slack. What problem is this workflow solving? / Use Case Manually monitoring incidents in ServiceNow can be time-consuming, and keeping teams updated about new or specific incidents often involves additional manual effort. This workflow automates the process of querying recent incidents from ServiceNow based on user-defined parameters and delivering formatted results directly to Slack. It ensures faster response times and improved incident visibility. What this workflow does This workflow integrates Slack and ServiceNow to provide an automated system for retrieving and presenting incident details. Slack User Interaction: Users initiate the workflow via a Slack modal form, selecting incident parameters like priority and state. ServiceNow Query: The workflow queries ServiceNow for incidents matching the selected criteria. Results Delivery: Incident results are sent back to Slack as a message formatted using Block Kit. If no results are found, the workflow notifies the user with a detailed message, either in a Slack channel or via direct message. Error Handling: If no channel is selected or any issues arise, the workflow ensures graceful fallback with appropriate notifications. Setup Instructions Slack Setup: Integrate Slack with n8n using a Slack app. Configure the modal form to accept parameters like priority and state. Check out this video for setting up a modal slack app on YouTube. ServiceNow Integration: Use ServiceNow credentials to connect with n8n. Ensure appropriate permissions for querying incidents. n8n Workflow Configuration: Import this workflow into n8n. Verify all node configurations, particularly those for ServiceNow API queries and Slack outputs. Set up webhook URLs for Slack event handling. Testing: Trigger the workflow from Slack to test modal inputs and incident queries. Confirm the output is correctly formatted and delivered to the intended Slack channel or user. How to Customize this Workflow to Your Needs Modify the ServiceNow query logic to include additional filters or fields. Adjust the Slack Block Kit formatting to match your organization’s preferred notification style. Use conditional logic to add more advanced handling for specific priorities or states. Expand the workflow to include escalation steps, such as notifying a specific team or creating follow-up tasks. Workflow Highlights Slack Modal Form**: Allows users to specify search criteria for incidents interactively. Dynamic Results Delivery**: Automatically sends results to a Slack channel or direct message based on user input. Error Handling**: Provides fallback notifications when no incidents are found or user inputs are incomplete. Customizable Integration**: Easily adaptable to fit different organizational needs, including advanced filtering and formatting options.

What this workflow does This workflow retrieves Online Marketing data (Google Analytics for several domains, Google Ads, Meta Ads) from the last 7 days and the same period in the previous year. The data is then prepared by AI as a table, analyzed and provided with a small summary. The summary is then sent by email to a desired address and, shortened and summarized again, sent to a Telegram account. This workflow has the following sequence: time trigger (e.g. every Monday at 7 a.m.) retrieval of Online Marketing data from the last 7 days (via sub workflows) assignment and summary of the data retrieval of Online Marketing data from the same time period of the previous year allocation and summary of the data preparation in tabular form and brief analysis by AI. sending the report as an email preparation in short form by AI for Telegram (optional) sending as Telegram message. Requirements The following accesses are required for the workflow: Google Analytics (via Google Analytics API): Documentation Google Ads (via HTTP Request -> Google Ads API):Documentation Meta Ads (via Facebook Graph API): Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) SMTP access data (for sending the mail) Telegram access data (optional for sending as Telegram message): Documentation 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 n8n automation is designed to extract, process, and store content from Notion pages into a Pinecone vector store. Here's a breakdown of the workflow: Notion - Page Added Trigger: The automation starts by monitoring for newly added pages in a specific Notion database. It triggers whenever a new page is created, capturing the page's metadata. Notion - Retrieve Page Content: Once triggered, the automation fetches the full content of the newly added Notion page, including blocks like text, images, and videos. Filter Non-Text Content: The next step filters out non-text content (such as images and videos), ensuring only textual content is processed. Summarize - Concatenate Notion's blocks content: The remaining text content is concatenated into a single block of text for easier processing. Token Splitter: The concatenated text is then split into manageable tokens, which are chunks of text that can be used for embedding. Create metadata and load content: Metadata such as the page ID, creation time, and title are added to the content, making it easy to reference and track. Embeddings Google Gemini: The processed text is passed through a Google Gemini model to generate embeddings, which are numerical representations of the text that capture its semantic meaning. Pinecone Vector Store: Finally, the embeddings, along with the content and metadata, are stored in a Pinecone vector store, making it searchable and ready for use in applications like document retrieval or natural language processing tasks. This workflow ensures that every new page added to the Notion database is processed into a format that can be easily searched and used in machine learning applications. The automation runs every minute to capture new data in real-time, providing an up-to-date and searchable vector database of Notion content. Use Case: This automation converts Notion pages into vector embeddings and stores them in Pinecone for enhanced search and AI-driven insights. It’s ideal for teams using Notion for knowledge management, enabling semantic search and context-based content retrieval. For example, employees can easily find relevant information across documents, and data scientists can use AI models to analyze and summarize the content stored in Notion.