Case Study 2: AI-Powered Support Command Center

An AI-powered Support Command Center built using n8n, OpenAI, Supabase, and Retool that transforms raw support ticket data into real-time executive insights, KPI tracking, and operational visibility.

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Overview

Support teams generate massive amounts of data — but most organizations struggle to turn that data into clear, actionable insight.

Common challenges:

• Metrics exist, but are fragmented across tools
• Leadership lacks real-time visibility into support health
• Trends are identified too late
• Data is reactive, not proactive
• No centralized “source of truth” for support performance

This case study demonstrates how to build an end-to-end Support Intelligence Command Center that:

• Aggregates support KPIs automatically
• Tracks week-over-week trends
• Uses AI to generate executive summaries
• Centralizes insights into a real-time dashboard

The result is a system that transforms support from data-heavy → insight-driven.

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Architecture

This system is built as a multi-layered data and intelligence pipeline:

1. Data Source (Support Tickets)
2. Automation + AI Processing (n8n)
3. Data Storage (Supabase / Postgres)
4. Visualization Layer (Retool Command Center)
5. Business Outcome (Actionable Intelligence)

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Problem

Support organizations struggle to operationalize their data:

• KPI tracking is manual or inconsistent
• No standardized way to compare performance over time
• Executive reporting is time-consuming
• Insights are disconnected from raw data
• No real-time visibility into operational risk

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Solution

This project introduces an AI-powered Support Command Center that:

• Automatically processes ticket data
• Calculates key support KPIs
• Tracks week-over-week changes
• Generates AI-driven executive insights
• Surfaces everything in a centralized dashboard

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What This System Does

• Aggregates support metrics:

  • Open / Pending Tickets
  • SLA Risk
  • Escalation Risk
  • Top Risk Area

• Compares performance across time periods

• Generates structured executive summaries using AI

• Identifies:

  • Immediate risks
  • Key observations
  • Recommended actions

• Stores outputs in a structured database

• Powers a real-time executive dashboard

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How It Works

1. Data Ingestion

• Source: Simulated support ticket datasets

• Files:

  • command_center_tickets_1000.csv
  • week_2_and_3_tickets_combined.csv

• Includes:

  • Status
  • Priority
  • Product Area
  • Ticket Age / SLA exposure

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2. KPI Aggregation (n8n)

• Calculates:
  • Open / Pending ticket volume
  • SLA risk (aging tickets)
  • Escalation risk (urgent tickets)
  • Top risk area (highest concentration of issues)

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3. Week-over-Week Comparison

• Compares current vs prior week
• Calculates KPI deltas
• Prepares structured comparison payload

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4. AI-Driven Executive Analysis

• Uses OpenAI to:
  • Interpret trends
  • Identify operational risks
  • Generate executive summary
  • Recommend actions
  • Assign support health score

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5. Data Storage (Supabase)

Stores two key datasets:

• tickets (raw data)
• daily_summary (AI-generated insights)

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6. Visualization (Retool Command Center)

Displays:

• KPI cards
• Week-over-week changes
• Executive summary
• Immediate risk
• Key observations
• Recommended actions
• Support health indicator

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Example Outputs

Command Center Dashboard

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Architecture Diagram

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Supabase Payload Example

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Workflow File

• n8n workflow export:
  Case Study 2_ AI-Powered Support Command Center.json

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Data Files

• command_center_tickets_1000.csv
• week_2_and_3_tickets_combined.csv

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Queries Used

• SQL queries for KPI calculation and aggregation:
  Queries Used.docx

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Key Outcomes

• Real-time visibility into support performance (simulated)
• Automated KPI tracking and trend analysis
• AI-generated executive insights
• Centralized command center for leadership
• Scalable architecture for production environments

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Why This Matters

Most support teams track metrics.

This system demonstrates how to:

Turn support data into a centralized, real-time decision-making system.

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Tech Stack

• n8n (workflow orchestration)
• OpenAI API (analysis + summarization)
• Supabase / Postgres (data storage)
• Retool (dashboard / command center)
• CSV datasets (simulated input data)

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Case Study Series

This project builds on:

• Case Study 1: AI-Powered RCA & Action Engine

Together, these demonstrate a broader vision of:

End-to-end AI-powered Support Operations systems

Future projects will expand into:

• AI agent-based support systems
• Self-service deflection
• Voice of customer intelligence
• Predictive support operations

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Additional Documentation

• Case study prompt:
  Case Study Prompt.docx

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Author

Built by Jesse Snow
Focused on Support Operations, AI Automation, and Scalable Systems Design