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Intelligent Support Triage & Auto-Response Engine with Jotform, Airtable, Gemini

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Created by: Zain Khan || zain
Zain Khan

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Last update 3 hours ago

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AI-Driven Support Triage and Auto-Response Engine

This n8n workflow establishes a sophisticated, multi-stage support system. It automatically validates user identity, analyzes ticket severity and sentiment, and attempts to resolve lower-priority issues using an AI agent connected to a knowledge base (Pinecone). It ensures high-priority or complex issues are immediately escalated to the team via Slack.

Phase 1: Intake and User Validation

The workflow triggers whenever a new support ticket is submitted, ensuring that only registered users receive automated assistance.

  1. Jotform Trigger: The process begins when a customer submits a support request via your designated Jotform.
  2. Airtable User Check: The workflow immediately searches your Airtable "Users" table to verify if the provided email matches an existing account.
  3. Validation Logic (If Node): * If User Found: The workflow proceeds to Phase 2 for analysis.
    • If User Not Found: It sends an automated Gmail message asking the user for the correct account email and updates the Airtable ticket status to "Need User Input"

Phase 2: AI Triage and Severity Analysis

Once validated, the issue is analyzed by a Senior Support AI to determine how it should be handled.

  1. AI Agent (Triage): Powered by Google Gemini, this agent analyzes the "Issue Type" and "Description." It uses a Structured Output Parser to categorize the ticket by:
    • Severity: High, Medium, or Low.
    • Sentiment: Frustrated or Normal.
  2. Airtable Logging: The analysis (Severity/Sentiment) is immediately appended to the support record in Airtable.
  3. Escalation Path (If High Severity): If the issue is flagged as "High," the workflow bypasses automated response attempts and triggers a Slack alert to the team for immediate manual intervention.

Phase 3: Automated Resolution and Escalation

For non-critical issues, the system attempts to solve the problem using your internal documentation.

  1. AI Agent (Resolution): This agent is equipped with a Pinecone Vector Store (containing your Support FAQs) and Gmail Tools.
  2. Vector Search: The agent searches the FAQ database for a solution.
    • Success (>90% confidence): The agent automatically drafts and sends a polite solution via Gmail and updates the Airtable status to "Responded."
    • Failure (No solution found): The agent returns "MANUAL_REVIEW_REQUIRED," which triggers a Slack notification to the support channel, including deep links to the Airtable record and specific user details (Plan type, Start date).

Phase 4: Verification Loop (Email Reply Detection)

The workflow also includes a secondary logic branch to handle incoming replies from users who were previously asked for identity verification.

  1. Gmail Trigger (Polling): Monitors for new unread replies from users with correct emails.
  2. Verification Check: Searches Airtable for tickets currently in "Require User Input" status.
  3. Status Restoration: Once the user replies, it notifies the team via Slack that a ticket has been "Verified" and moves the status back to "Pending" for regular processing.

Requirements

1. Automation & AI Platforms

  • n8n Instance: Cloud or self-hosted.
  • Google Gemini API: Required for the Triage and Resolution AI agents.
  • Pinecone: A vector database containing your FAQ embeddings for AI retrieval.

2. Service Credentials

  • Airtable Personal Access Token: To search users and update support entries.
  • Gmail OAuth2: To send automated resolutions and monitor for user replies.
  • Slack OAuth2: To send real-time alerts and escalation blocks to the team.
  • Jotform API Key: To trigger the workflow upon form submission.

3. External Configurations

  • Airtable Schema: Two tables are required:
    • Users: Containing Email, First Name, Last Name, and Current Plan.
    • Support Entries: Containing Submission ID, Status, Severity, Sentiment, and Message id.
  • Pinecone Index: An index named support-faqs populated with relevant documentation embeddings.