Back to Catalog

AI-powered ServiceNow chat triage with GPT-4 — incident & request manager

RiteshRitesh
456 views
2/3/2026
Official Page

Automated Incident and Request Management in ServiceNow

Who’s it for

This workflow is designed for IT teams, service desk agents, and operations managers who use ServiceNow. It reduces manual effort by automatically classifying chat messages as Incidents or Requests, creating/updating them in ServiceNow, and summarizing ticket updates.


What it does

  • Receives incoming chat messages.

  • Classifies the message as one of:

    • Incident (something broken, unavailable, or a complaint)
    • Request (access, provisioning, product/order related)
    • Follow-ups (incident or request update checks)
    • Update action (user wants to add info to an existing ticket)
    • Everything else (knowledge search / general query).
  • Creates Incidents in ServiceNow via the ServiceNow node.

  • Creates Requests in ServiceNow using the Service Catalog API.

  • Updates existing Incidents with new work notes when the user provides an update.

  • Pulls existing incident/request work notes for summaries.

  • Optionally uses SerpAPI for general queries (if enabled).

  • Returns a concise summary back to the user through the webhook.


Requirements

  • ServiceNow account with API access (Basic Auth)
  • OpenAI API key (used by the classifier and summarizer)
  • SerpAPI key (optional – for general web lookups)

Credentials needed

You will need to set up the following credentials in n8n:

  1. ServiceNow Basic Auth (username, password, instance URL).
  2. OpenAI API (API key).
  3. SerpAPI (optional – only if you want web search enabled).

How to set up

  1. Import the workflow JSON into your n8n instance.

  2. Create the credentials mentioned above and assign them to the corresponding nodes:

    • Create an incidentServiceNow Basic Auth
    • HTTP Request1 (for Service Catalog requests) → ServiceNow Basic Auth
    • OpenAI Chat Model / OpenAI Chat Model1 / OpenAI Chat Model2 / OpenAI Chat Model3OpenAI API
    • SerpAPI node (optional) → SerpAPI key
  3. Adjust the ServiceNow instance URL in the HTTP Request node to match your environment.

  4. Deploy the workflow.

  5. Send a test chat message to trigger the workflow.


How to customize

  • Update the classification rules in the Text Classifier node if your organization uses different definitions for incidents vs. requests.
  • Edit the summary prompt in the Summarization Chain to include or exclude specific fields.
  • Add additional notification nodes (Slack, Teams, or Email) if you want updates pushed to other channels.

Notes & Limitations

  • This workflow creates general requests in ServiceNow using the catalog API. For production, update the Service Catalog item ID to match your environment.
  • “Everything else” category uses SerpAPI. If not configured, those queries will not return results.
  • This workflow requires OpenAI GPT-4.1 mini (or another supported model) for classification and summarization.

AI-Powered ServiceNow Chat Triage with GPT-4 (Incident/Request Manager)

This n8n workflow leverages AI to intelligently triage incoming chat messages, classify them as either an incident or a service request, summarize the conversation, and then create the appropriate record in ServiceNow. It streamlines the support process by automating initial assessment and ticket creation.

What it does

  1. Listens for Chat Messages: The workflow is triggered by an incoming chat message, acting as the initial point of contact for a user seeking support.
  2. Classifies Request Type: An AI Text Classifier analyzes the chat message to determine if it's an "Incident" (something broken) or a "Service Request" (something new or a standard request).
  3. Summarizes Chat Conversation: The AI Summarization Chain condenses the chat conversation into a concise summary, capturing the essence of the user's issue or request.
  4. Extracts Key Information (AI Transform): An AI Transform node extracts critical details from the chat, such as the user's name, contact information, and specific problem descriptions, to enrich the ServiceNow record.
  5. Generates ServiceNow Description (AI Agent): An AI Agent, equipped with a Google Search tool, formulates a comprehensive description for the ServiceNow ticket based on the classification, summary, and extracted information. This agent can potentially search for relevant knowledge base articles or common solutions to provide a more informed description.
  6. Creates ServiceNow Record: Based on the AI classification, the workflow creates either a new Incident or a new Service Request in ServiceNow, populating it with the AI-generated description and other extracted details.
  7. Responds to Webhook: The workflow sends a response back to the originating chat system, likely confirming the ticket creation and providing the new ticket number.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: For the OpenAI Chat Model, Text Classifier, Summarization Chain, and AI Agent.
  • SerpAPI API Key: For the Google Search tool used by the AI Agent.
  • ServiceNow Account: With appropriate API access to create Incidents and Service Requests.
  • Chat Platform Integration: A chat platform that can send messages to n8n via a webhook (e.g., Slack, Microsoft Teams, custom chat application). The "Chat Trigger" node will need to be configured for your specific chat platform.

Setup/Usage

  1. Import the Workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your OpenAI API Key credential for the OpenAI Chat Model, Text Classifier, Summarization Chain, and AI Agent nodes.
    • Set up your SerpAPI API Key credential for the SerpAPI node.
    • Set up your ServiceNow credential, providing your instance URL and authentication details.
  3. Configure Chat Trigger:
    • Open the "When chat message received" node.
    • Copy the webhook URL.
    • Configure your chat platform to send messages to this webhook URL when a new chat interaction occurs.
  4. Review and Customize AI Nodes:
    • Text Classifier: Ensure the classification categories ("Incident", "Service Request") align with your ServiceNow setup. You might want to refine the prompt for better accuracy.
    • Summarization Chain: Adjust the prompt if you need a specific style or length for summaries.
    • AI Transform: Customize the prompt to ensure it extracts all necessary fields for your ServiceNow records (e.g., user email, affected service, urgency).
    • AI Agent: Review the agent's prompt and available tools. The Google Search tool is included, but you might want to add other tools (e.g., internal knowledge base search) depending on your needs.
  5. Activate the Workflow: Once configured, activate the workflow in n8n.

This workflow provides a powerful foundation for automating your IT support triage, reducing manual effort, and speeding up resolution times.

Related Templates

AI-powered code review with linting, red-marked corrections in Google Sheets & Slack

Advanced Code Review Automation (AI + Lint + Slack) Who’s it for For software engineers, QA teams, and tech leads who want to automate intelligent code reviews with both AI-driven suggestions and rule-based linting — all managed in Google Sheets with instant Slack summaries. How it works This workflow performs a two-layer review system: Lint Check: Runs a lightweight static analysis to find common issues (e.g., use of var, console.log, unbalanced braces). AI Review: Sends valid code to Gemini AI, which provides human-like review feedback with severity classification (Critical, Major, Minor) and visual highlights (red/orange tags). Formatter: Combines lint and AI results, calculating an overall score (0–10). Aggregator: Summarizes results for quick comparison. Google Sheets Writer: Appends results to your review log. Slack Notification: Posts a concise summary (e.g., number of issues and average score) to your team’s channel. How to set up Connect Google Sheets and Slack credentials in n8n. Replace placeholders (<YOURSPREADSHEETID>, <YOURSHEETGIDORNAME>, <YOURSLACKCHANNEL_ID>). Adjust the AI review prompt or lint rules as needed. Activate the workflow — reviews will start automatically whenever new code is added to the sheet. Requirements Google Sheets and Slack integrations enabled A configured AI node (Gemini, OpenAI, or compatible) Proper permissions to write to your target Google Sheet How to customize Add more linting rules (naming conventions, spacing, forbidden APIs) Extend the AI prompt for project-specific guidelines Customize the Slack message formatting Export analytics to a dashboard (e.g., Notion or Data Studio) Why it’s valuable This workflow brings realistic, team-oriented AI-assisted code review to n8n — combining the speed of automated linting with the nuance of human-style feedback. It saves time, improves code quality, and keeps your team’s review history transparent and centralized.

higashiyama By higashiyama
90

Document RAG & chat agent: Google Drive to Qdrant with Mistral OCR

Knowledge RAG & AI Chat Agent: Google Drive to Qdrant Description This workflow transforms a Google Drive folder into an intelligent, searchable knowledge base and provides a chat agent to query it. It’s composed of two distinct flows: An ingestion pipeline to process documents. A live chat agent that uses RAG (Retrieval-Augmented Generation) and optional web search to answer user questions. This system fully automates the creation of a “Chat with your docs” solution and enhances it with external web-searching capabilities. --- Quick Implementation Steps Import the workflow JSON into your n8n instance. Set up credentials for Google Drive, Mistral AI, OpenAI, and Qdrant. Open the Web Search node and add your Tavily AI API key to the Authorization header. In the Google Drive (List Files) node, set the Folder ID you want to ingest. Run the workflow manually once to populate your Qdrant database (Flow 1). Activate the workflow to enable the chat trigger (Flow 2). Copy the public webhook URL from the When chat message received node and open it in a new tab to start chatting. --- What It Does The workflow is divided into two primary functions: Knowledge Base Ingestion (Manual Trigger) This flow populates your vector database. Scans Google Drive: Lists all files from a specified folder. Processes Files Individually: Downloads each file. Extracts Text via OCR: Uses Mistral AI OCR API for text extraction from PDFs, images, etc. Generates Smart Metadata: A Mistral LLM assigns metadata like documenttype, project, and assignedto. Chunks & Embeds: Text is cleaned, chunked, and embedded via OpenAI’s text-embedding-3-small model. Stores in Qdrant: Text chunks, embeddings, and metadata are stored in a Qdrant collection (docaiauto). AI Chat Agent (Chat Trigger) This flow powers the conversational interface. Handles User Queries: Triggered when a user sends a chat message. Internal RAG Retrieval: Searches Qdrant Vector Store first for answers. Web Search Fallback: If unavailable internally, the agent offers to perform a Tavily AI web search. Contextual Responses: Combines internal and external info for comprehensive answers. --- Who's It For Ideal for: Teams building internal AI knowledge bases from Google Drive. Developers creating AI-powered support, research, or onboarding bots. Organizations implementing RAG pipelines. Anyone making unstructured Google Drive documents searchable via chat. --- Requirements n8n instance (self-hosted or cloud). Google Drive Credentials (to list and download files). Mistral AI API Key (for OCR & metadata extraction). OpenAI API Key (for embeddings and chat LLM). Qdrant instance (cloud or self-hosted). Tavily AI API Key (for web search). --- How It Works The workflow runs two independent flows in parallel: Flow 1: Ingestion Pipeline (Manual Trigger) List Files: Fetch files from Google Drive using the Folder ID. Loop & Download: Each file is processed one by one. OCR Processing: Upload file to Mistral Retrieve signed URL Extract text using Mistral DOC OCR Metadata Extraction: Analyze text using a Mistral LLM. Text Cleaning & Chunking: Split into 1000-character chunks. Embeddings Creation: Use OpenAI embeddings. Vector Insertion: Push chunks + metadata into Qdrant. Flow 2: AI Chat Agent (Chat Trigger) Chat Trigger: Starts when a chat message is received. AI Agent: Uses OpenAI + Simple Memory to process context. RAG Retrieval: Queries Qdrant for related data. Decision Logic: Found → Form answer. Not found → Ask if user wants web search. Web Search: Performs Tavily web lookup. Final Response: Synthesizes internal + external info. --- How To Set Up Import the Workflow Upload the provided JSON into your n8n instance. Configure Credentials Create and assign: Google Drive → Google Drive nodes Mistral AI → Upload, Signed URL, DOC OCR, Cloud Chat Model OpenAI → Embeddings + Chat Model nodes Qdrant → Vector Store nodes Add Tavily API Key Open Web Search node → Parameters → Headers Add your key under Authorization (e.g., tvly-xxxx). Node Configuration Google Drive (List Files): Set Folder ID. Qdrant Nodes: Ensure same collection name (docaiauto). Run Ingestion (Flow 1) Click Test workflow to populate Qdrant with your Drive documents. Activate Chat (Flow 2) Toggle the workflow ON to enable real-time chat. Test Open the webhook URL and start chatting! --- How To Customize Change LLMs: Swap models in OpenAI or Mistral nodes (e.g., GPT-4o, Claude 3). Modify Prompts: Edit the system message in ai chat agent to alter tone or logic. Chunking Strategy: Adjust chunkSize and chunkOverlap in the Code node. Different Sources: Replace Google Drive with AWS S3, Local Folder, etc. Automate Updates: Add a Cron node for scheduled ingestion. Validation: Add post-processing steps after metadata extraction. Expand Tools: Add more functional nodes like Google Calendar or Calculator. --- Use Case Examples Internal HR Bot: Answer HR-related queries from stored policy docs. Tech Support Assistant: Retrieve troubleshooting steps for products. Research Assistant: Summarize and compare market reports. Project Management Bot: Query document ownership or project status. --- Troubleshooting Guide | Issue | Possible Solution | |------------|------------------------| | Chat agent doesn’t respond | Check OpenAI API key and model availability (e.g., gpt-4.1-mini). | | Known documents not found | Ensure ingestion flow ran and both Qdrant nodes use same collection name. | | OCR node fails | Verify Mistral API key and input file integrity. | | Web search not triggered | Re-check Tavily API key in Web Search node headers. | | Incorrect metadata | Tune Information Extractor prompt or use a stronger Mistral model. | --- Need Help or More Workflows? Want to customize this workflow for your business or integrate it with your existing tools? Our team at Digital Biz Tech can tailor it precisely to your use case from automation logic to AI-powered enhancements. We can help you set it up for free — from connecting credentials to deploying it live. Contact: shilpa.raju@digitalbiz.tech Website: https://www.digitalbiz.tech LinkedIn: https://www.linkedin.com/company/digital-biz-tech/ You can also DM us on LinkedIn for any help. ---

DIGITAL BIZ TECHBy DIGITAL BIZ TECH
1409

Track daily moods with AI analysis & reports using GPT-4o, Data Tables & Gmail

Track your daily mood in one tap and receive automated AI summaries of your emotional trends every week and month. Perfect for self-reflection, wellness tracking, or personal analytics. This workflow logs moods sent through a webhook (/mood) into Data Tables, analyzes them weekly and monthly with OpenAI (GPT-4o), and emails you clear summaries and actionable recommendations via Gmail. ⚙️ How It Works Webhook – Mood → Collects new entries (🙂, 😐, or 😩) plus an optional note. Set Mood Data → Adds date, hour, and note fields automatically. Insert Mood Row → Stores each record in a Data Table. Weekly Schedule (Sunday 20:00) → Aggregates the last 7 days and sends a summarized report. Monthly Schedule (Day 1 at 08:00) → Aggregates the last 30 days for a deeper AI analysis. OpenAI Analysis → Generates insights, patterns, and 3 actionable recommendations. Gmail → Sends the full report (chart + AI text) to your inbox. 📊 Example Auto-Email Weekly Mood Summary (last 7 days) 🙂 5 ██████████ 😐 2 ████ 😩 0 Average: 1.7 (Positive 🙂) AI Insights: You’re trending upward this week — notes show that exercise days improved mood. Try keeping short walks mid-week to stabilize energy. 🧩 Requirements n8n Data Tables enabled OpenAI credential (GPT-4o or GPT-4 Turbo) Gmail OAuth2 credential to send summaries 🔧 Setup Instructions Connect your credentials: Add your own OpenAI and Gmail OAuth2 credentials. Set your Data Table ID: Open the Insert Mood Row node and enter your own Data Table ID. Without this, new moods won’t be stored. Replace the email placeholder: In the Gmail nodes, replace your.email@example.com with your actual address. Deploy and run: Send a test POST request to /mood (e.g. { "mood": "🙂", "note": "productive day" }) to log your first entry. ⚠️ Before activating the workflow, ensure you have configured the Data Table ID in the “Insert Mood Row” node. 🧠 AI Analysis Interprets mood patterns using GPT-4o. Highlights trends, potential triggers, and suggests 3 specific actions. Runs automatically every week and month. 🔒 Security No personal data is exposed outside your n8n instance. Always remove or anonymize credential references before sharing publicly. 💡 Ideal For Personal mood journaling and AI feedback Therapists tracking client progress Productivity or self-quantification projects 🗒️ Sticky Notes Guide 🟡 Mood Logging Webhook POST /mood receives mood + optional note. ⚠️ Configure your own Data Table ID in the “Insert Mood Row” node before running. 🟢 Weekly Summary Runs every Sunday 20:00 → aggregates last 7 days → generates AI insights + emails report. 🔵 Monthly Summary Runs on Day 1 at 08:00 → aggregates last 30 days → creates monthly reflection. 🟣 AI Analysis Uses OpenAI GPT-4o to interpret trends and recommend actions. 🟠 Email Delivery Sends formatted summaries to your inbox automatically.

Jose CastilloBy Jose Castillo
105