AI-Powered Customer Scorecard

AirGap Labs

IT Infrastructure & Security | Fortinet Engage Preferred Partner

TECHNICAL CASE STUDY

Internal CRM & Automation Suite

Stack: Shopify Liquid  •  n8n Cloud  •  Supabase  •  Chart.js  •  Harvest API

Role: Principal, AirGap Labs  •  Irvine, CA

CASE STUDY

AI-Powered Customer Scorecard

Automated relationship health scoring with live drill-down analytics

Industry

IT Infrastructure & Managed Security Services

Platform

Shopify Liquid + n8n AI Agent + Supabase + Chart.js

Timeline

Built from scratch; iterated with AI scoring layer added later

Audience

Sales team and account managers

The Problem

AirGap's account managers had no systematic way to identify which customers were thriving and which were at risk. Customer health was assessed ad hoc in weekly meetings — relying on memory, gut feel, and whoever spoke up. There was no scoring model, no trend visibility, and no way to prioritize outreach across a growing book of business.

The team needed a way to surface risk early, identify expansion opportunities, and walk into customer conversations with data instead of assumptions.

The Solution

A Customer Scorecard dashboard that combines structured CRM data with an AI-powered scoring agent. Each customer receives a composite health score across four categories, displayed in a visual dashboard with drill-down tables and an AI action plan generator.

Scoring Categories

  • Financial Health — invoice aging, payment history, open balance trends
  • Engagement — ticket volume, response time, meeting frequency
  • Project & Contract Activity — active SOWs, renewal proximity, project completion rate
  • Relationship Depth — stakeholder coverage, escalation history, NPS signals

Key Features Built

  • Stacked bar chart grouped by health tier (Healthy / At Risk / Critical) with per-category breakdown
  • Drill-down table that filters to customers in any selected tier when a chart bar is clicked
  • AI scoring agent in n8n: reads structured customer data, reasons across the four categories, and returns a scored JSON object with narrative justification
  • Action Plan modal: one click sends a customer record to the AI agent and returns a prioritized action plan for the account manager
  • Score history tracking — Supabase stores each scoring run so trend lines are available over time
  • Role-gated to admin and sales rep metafield values

4

Scoring dimensions

n8n + LLM

AI agent

Automated

At-risk customers surfaced

Yes

Action plans on demand

Technical Highlights

The AI scoring layer was the most architecturally interesting piece. Rather than a rules engine with fixed thresholds, the n8n agent receives a structured data payload and uses an LLM to reason across all four scoring categories simultaneously — weighting factors contextually rather than mechanically. The agent returns a normalized score plus a plain-English rationale that appears in the dashboard alongside the number.

The scoring pipeline handles varied n8n response wrapper shapes via a recursive extractor, which proved important as the workflow evolved through several iterations. The chart and table components share the same tier palette constant, keeping visual language consistent even as the scoring model was tuned.

Outcome

Account managers now open the scorecard at the start of each week to identify their highest-risk accounts. The AI action plan feature reduced the time to prepare for a difficult customer conversation from 30+ minutes of manual research to under two minutes. The health tier chart gives leadership an instant portfolio-level view that was previously impossible without a dedicated CRM system.

AirGap Labs  •  Irvine, CA  •  airgaplabs.com

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