🧠 ICP Scoring Agent (n8n + Explorium + LLM)

This workflow automates Ideal Customer Profile (ICP) scoring for any company using a combination of Explorium data and an LLM-driven evaluation framework.

🔧 How It Works

1. Input: Company name is submitted via form.
2. Data Enrichment: Explorium's MCP Server is used to fetch firmographic, hiring, and tech data about the company.
3. Scoring Logic: An AI agent (LLM) applies a 3-pillar framework to assess and score the company.
4. Output: A structured JSON or Google Doc summary is generated using the AgentGeeks formatter.

📊 Scoring System (100 points total)

🧠 Scoring Criteria

• Strategic Fit: Industry, size, use case, buyer roles
• Tech Readiness: AI maturity, hiring trends, stack visibility
• Reachability: Geography, contactability, data quality

🎯 Verdict Scale

• 🟩 90–100: Ideal ICP
• ✅ 70–89: Good Fit
• 🟨 40–69: Medium Fit
• ❌ < 40: Poor Fit

📦 Workflow Components

• Trigger: Form submission via webhook
• MCP Client: Pulls enriched company data via Explorium's MCP API
• AI Agent: Uses Anthropic Claude (or other LLM) to calculate scores
• Output: Results are posted to a structured endpoint (e.g. Google Doc or JSON API)

🧰 Dependencies

• n8n (self-hosted or cloud)
• Explorium MCP credentials and access
• LLM API (e.g., Anthropic Claude, OpenAI, etc.)
• Optional: AgentGeeks formatter or similar doc generator

💼 Use Case

This ICP scoring system is designed for GTM and sales teams to:

• Automate lead prioritization
• Qualify accounts before outbounding
• Sync ICP data into CRMs, routing systems, or reporting layers

📈 Example Output in Google Doc

{
  "company": "Acme Inc.",
  "score": 87,
  "verdict": "Good Fit",
  "pillars": {
    "strategic_fit": 35,
    "tech_readiness": 37,
    "reachability": 15
  },
  "summary": "Acme Inc. is a mid-sized SaaS company with strong AI hiring activity and a buyer profile aligned to enterprise IT. Moderate reachability via firmographic signals."
}