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." } ```
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
- Input: Company name is submitted via form.
- Data Enrichment: Explorium's MCP Server is used to fetch firmographic, hiring, and tech data about the company.
- Scoring Logic: An AI agent (LLM) applies a 3-pillar framework to assess and score the company.
- 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 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
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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