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Create ideal customer profiles from LinkedIn data with Airtop and Claude AI

AirtopAirtop
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2/3/2026
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Define Your ICP from Customer LinkedIn Profiles

Use Case

This automation helps marketing and sales teams define their Ideal Customer Profile (ICP) using real LinkedIn profiles of current high-fit customers. By enriching and analyzing profile data, it generates a clear ICP definition and scoring methodology for future targeting.

What This Automation Does

This automation analyzes LinkedIn profiles of your existing customers and produces:

  • A structured ICP definition
  • A scoring model to evaluate future prospects
  • A Google Boolean search string to find similar prospects

Input:

  • LinkedIn profile URLs of existing high-fit customers (e.g., https://www.linkedin.com/in/amirashkenazi/)

Output:

  • A Google Doc containing the ICP analysis and scoring methodology

How It Works

  1. Trigger: Waits for a chat message containing one or more LinkedIn profile URLs.
  2. AI Agent: Parses and processes the URLs.
  3. Airtop Data Enrichment: Uses Airtop to extract structured information from each LinkedIn profile (e.g., job title, company, experience, skills).
  4. Memory: Maintains state between inputs for consistent analysis.
  5. LLM Analysis: Uses Claude 3.7 Sonnet to synthesize enriched data into a meaningful ICP.
  6. Google Docs: Automatically creates a new doc with a timestamped title and appends the ICP definition.

Setup Requirements

  1. Airtop Profile connected to LinkedIn, Insert the profile name in the Airtop Tool
  2. Airtop API credentials. Get it free here
  3. If you choose to activate saving the profiles in Google Docs you will need OAuth2 credentials (or just copy the ICP definition from the chat)

Next Steps

  • Use the ICP for Scoring: Feed new LinkedIn profiles through the same Airtop enrichment and use the scoring function to evaluate fit.
  • Automate Target Discovery: Plug the Boolean search output into LinkedIn, Google, or People Data Labs for ICP-matching lead generation.
  • Refine Continuously: Repeat the workflow as your customer base grows or segments evolve.

Read more about how to Define ICP from Customer Examples

n8n Workflow: AI Agent with Anthropic Chat and Simple Memory

This n8n workflow demonstrates a basic AI agent setup using LangChain nodes, featuring an Anthropic Chat Model and a simple memory buffer. It's designed to process incoming chat messages and respond using an AI agent.

What it does

This workflow sets up a foundational AI agent that can:

  1. Receive Chat Messages: It listens for incoming chat messages as its trigger.
  2. Process with an AI Agent: The received messages are fed into an AI Agent node.
  3. Utilize Anthropic Chat Model: The AI Agent uses an Anthropic Chat Model (e.g., Claude) for its language processing capabilities.
  4. Maintain Simple Memory: A Simple Memory buffer is integrated to give the AI agent a short-term memory of previous interactions within the conversation.

Prerequisites/Requirements

  • n8n Instance: A running instance of n8n.
  • Anthropic API Key: You will need an API key for Anthropic to use their chat models (e.g., Claude). This credential needs to be configured in the "Anthropic Chat Model" node.
  • LangChain Nodes: Ensure you have the LangChain nodes installed in your n8n instance.

Setup/Usage

  1. Import the Workflow:
    • Copy the provided JSON code.
    • In your n8n instance, click "New" in the workflows list, then "Import from JSON".
    • Paste the JSON code and click "Import".
  2. Configure Credentials:
    • Locate the "Anthropic Chat Model" node.
    • Click on the node and configure your Anthropic API Key credential. If you haven't created one, you'll need to add a new credential of type "Anthropic API".
  3. Activate the Workflow:
    • Once credentials are configured, save the workflow.
    • Activate the workflow by toggling the "Active" switch in the top right corner.
  4. Trigger the Workflow:
    • The "When chat message received" node acts as the trigger. You would typically integrate this with a chat platform (like Slack, Telegram, etc.) or a custom chat interface that can send messages to n8n's webhook.
    • When a chat message is received, the AI agent will process it and generate a response based on the Anthropic model and its memory.

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