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Automate company ICP scoring with Explorium data and Claude AI analysis

ItamarItamar
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2/3/2026
TypeformAutomationContent ManagementHTTP RequestSendGrid
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🧠 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 (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."
}

Automate Company ICP Scoring with Explorium Data and Claude AI Analysis

This n8n workflow automates the process of scoring companies against an Ideal Customer Profile (ICP) by leveraging external data sources and advanced AI analysis. It allows you to define your ICP criteria and receive a comprehensive score and explanation for each company, streamlining your sales and marketing efforts.

What it does

This workflow simplifies the ICP scoring process through the following steps:

  1. Triggers on Form Submission: The workflow is initiated when a new company is submitted via an n8n form.
  2. Retrieves Company Data: It makes an HTTP request to an external service (likely Explorium, based on the directory name) to fetch comprehensive data for the submitted company.
  3. Analyzes with AI Agent: An AI Agent, powered by LangChain and the Anthropic Chat Model (Claude AI), processes the retrieved company data.
  4. Scores against ICP: The AI Agent scores the company against predefined Ideal Customer Profile (ICP) criteria.
  5. Provides Explanation: The AI Agent generates a detailed explanation for the given ICP score.
  6. Outputs Results: The final ICP score and explanation are made available for further use, potentially through an MCP Client Tool for integration with other systems.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • External Data Source (e.g., Explorium): Access to an API that provides company data (e.g., firmographics, technographics, intent data). You will need the API endpoint and any necessary authentication.
  • Anthropic API Key: An API key for Anthropic's Claude AI model to be used with the LangChain Anthropic Chat Model node.
  • n8n Form: An n8n Form configured to trigger the workflow with company submission data.
  • MCP Client (Optional): If you intend to integrate the output with a Model Context Protocol (MCP) compatible system, you will need the MCP Client Tool configured.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure the n8n Form Trigger:
    • Open the "On form submission" node.
    • Configure the form fields that will be used to submit company information (e.g., company name, website).
    • Activate the workflow to generate the webhook URL for the form.
  3. Configure the HTTP Request Node:
    • Open the "HTTP Request" node.
    • Set the URL to your external data source's API endpoint (e.g., Explorium API).
    • Configure the Method (e.g., GET, POST) and Authentication as required by your data source.
    • Map the incoming company data from the "On form submission" node to the request body or query parameters as needed by the external API.
  4. Configure the Anthropic Chat Model Node:
    • Open the "Anthropic Chat Model" node.
    • Select or create a new "Anthropic API" credential, providing your Anthropic API Key.
    • Choose the desired Claude model (e.g., claude-3-sonnet-20240229).
  5. Configure the AI Agent Node:
    • Open the "AI Agent" node.
    • Define the System Message and User Message to instruct the AI on how to score the company against your ICP. This is where you will provide your ICP criteria and ask for a score and explanation.
    • Ensure the AI Agent is configured to use the "Anthropic Chat Model" as its Language Model.
  6. Configure the MCP Client Tool (Optional):
    • If you are using the "MCP Client" node, configure it to send the AI-generated ICP score and explanation to your desired MCP-compatible system.
  7. Activate the Workflow: Once all nodes are configured, activate the workflow to start automating your ICP scoring.

Now, whenever a new company is submitted via your n8n form, the workflow will automatically fetch data, analyze it with Claude AI, provide an ICP score, and an explanation.

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