Automate Company ICP Scoring with Explorium Data and Claude AI Analysis

# 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) | Pillar | Max Points | |-----------------------|------------| | Strategic Fit | 40 | | AI / Tech Readiness | 40 | | Engagement & Reachability | 20 | ### 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](https://n8n.io/) (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 GM 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 ```json { "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." } ```

n8n

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)

PillarMax Points
Strategic Fit40
AI / Tech Readiness40
Engagement & Reachability20

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 GM 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."
}
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How to import this workflow into n8n

  1. 1Purchase or download the workflow to get the n8n workflow JSON file.
  2. 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
  3. 3Open each node marked with a credential warning and connect your own accounts and API keys.
  4. 4Run the workflow once manually to verify the data flow, then toggle it to Active.

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