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

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

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 c...

Best for

Lead Generation automation workflows
AI Summarization automation workflows
intermediate n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatanthropic, @n8n/n8n-nodes-langchain.memorybufferwindow, n8n-nodes-base.stickynote, n8n-nodes-base.airtoptool, n8n-nodes-base.googledocs

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Airtop.

Original n8n.io source

1.1 Workflow description

Title: Create ideal customer profiles from LinkedIn data with Airtop and Claude AI
Workflow name: Create ideal customer profiles from LinkedIn data with Airtop and Claude AI

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

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

Setup Requirements

Airtop Profile connected to LinkedIn, Insert the profile name in the Airtop Tool
Airtop API credentials. Get it free here
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

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Block 2 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.9

Block 3 - Anthropic Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatAnthropic - lmChatAnthropic
Config choices: Version 1.3

Block 4 - Simple Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 5 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 6 - Airtop Data Enrichment

Type / Role: n8n-nodes-base.airtopTool - airtopTool
Config choices: Version 1

Block 7 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 8 - Google Docs

Type / Role: n8n-nodes-base.googleDocs - googleDocs
Config choices: Version 2

Block 9 - Google Docs1

Type / Role: n8n-nodes-base.googleDocs - googleDocs
Config choices: Version 2

Block 10 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

3. Summary Table

Workflow	Create ideal customer profiles from LinkedIn data with Airtop and Claude AI
Complexity	intermediate
Nodes	10
Categories	Lead Generation, AI Summarization
Author	Airtop
Published	18 May 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/4204/4204.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Lead Generation workflows AI Summarization workflows

Frequently asked questions

What does Create ideal customer profiles from LinkedIn data with Airtop and Claude AI do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Lead Generation, AI Summarization use case.

Airtop

40 workflows

Nodes

@n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatanthropic @n8n/n8n-nodes-langchain.memorybufferwindow n8n-nodes-base.stickynote n8n-nodes-base.airtoptool n8n-nodes-base.googledocs

Complexity

intermediate

Published 18 May 2025

Likes 0

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Install path: /data/workflows/4204/4204.json

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

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Define Your ICP from Customer LinkedIn Profiles

Use Case

What This Automation Does

How It Works

Setup Requirements

Next Steps

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When chat message received

Block 2 - AI Agent

Block 3 - Anthropic Chat Model

Block 4 - Simple Memory

Block 5 - Sticky Note

Block 6 - Airtop Data Enrichment

Block 7 - Sticky Note1

Block 8 - Google Docs

Block 9 - Google Docs1

Block 10 - Sticky Note2

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions