Monitor LinkedIn profile URLs, enrich data via Airtop, analyze and synthesize an ICP with Claude AI, and create a Google Doc-ready ICP definition and scoring model.
The AI agent automates ICP creation by analyzing LinkedIn profiles of current high-fit customers and extracting common traits. It defines a practical ICP and a scoring model to rank future prospects. It outputs a Google Doc-ready ICP profile with search strings for finding similar prospects.
Performs ICP creation end-to-end with data enrichment and scoring.
Ingest LinkedIn profile URLs
Enrich profiles with Airtop data
Normalize and summarize attributes (title, company, experience, skills)
Synthesize ICP definition and scoring criteria
Generate a Google Boolean search string for similar prospects
Deliver a timestamped ICP document in Google Docs
before → The ICP process relies on disparate data sources, making it slow and inconsistent. after → The AI agent delivers a unified ICP with a clear scoring model and a ready-to-share document.
A simple three-step flow that non-technical users can follow.
Detects LinkedIn profile URLs provided by the user and queues them for processing.
Parses each URL and enriches the data with Airtop to extract structured attributes like title, company, seniority, experience, and skills.
Claude AI synthesizes the enriched data into a practical ICP and scoring model, then creates a timestamped Google Doc with the ICP definition.
A realistic scenario showing time, task, and outcome.
Scenario: A marketing team provides 5 high-fit LinkedIn profile URLs. The AI agent enriches each profile with Airtop data, identifies common ICP traits, and generates a scoring rubric. It then creates a timestamped Google Doc titled 'ICP — 2026-04-27' containing the ICP definition, scoring criteria, and a Google Boolean search string to locate similar prospects. Expected outcome: a ready-to-share ICP document in minutes, enabling targeted outreach and faster campaigns.
Roles that gain a concrete ICP artifact and streamlined targeting.
Needs a consistent ICP to harmonize messaging across campaigns.
Requires a clear prioritization framework to focus outreach.
Benefits from a repeatable ICP to fuel campaigns and tests.
Uses precise search strings to find matching prospects quickly.
Aligns experiments around a validated ICP and scoring model.
Maps ICP traits to messaging and positioning.
Tools that work together to enrich data and deliver the ICP artifact.
Enrich LinkedIn profiles with structured attributes via Airtop; extract title, company, experience, and skills.
Analyze enriched data and synthesize a practical ICP with a scoring model.
Create a timestamped ICP document and append final ICP details for sharing.
Provide LinkedIn profile URLs for enrichment and ICP derivation.
Practical scenarios that demonstrate concrete outcomes.
Practical answers to common concerns about using the AI agent.
In most cases, processing a handful of profiles completes in just a few minutes. The exact time depends on the number of URLs and the depth of Airtop enrichment. The system maintains state across inputs, so subsequent runs can reuse prior context to accelerate processing. The final Google Doc is generated with a timestamped title to ensure traceability.
The ICP is derived from real customer LinkedIn profiles provided by the user, enriched by Airtop for structured attributes, and synthesized by Claude AI into actionable criteria. The output includes a scoring model and a search string for finding similar prospects. Data handling follows your existing authentication and storage policies.
Yes. You can adjust the weighting of ICP attributes and modify scoring rules to reflect your go-to-market priorities. The agent preserves the changes in its state for subsequent runs, enabling consistent iterations. You can also export the ICP and scoring rubric for external use.
Access to the Google Doc is controlled by your OAuth and Google Drive permissions. The agent creates a timestamped document to support auditability and sharing controls. If you prefer, you can copy the ICP content out of the chat window and store it elsewhere. Always follow your company's data governance policies.
Simply provide new LinkedIn profile URLs. The AI agent re-enriches with Airtop, re-evaluates the ICP, and updates the scoring model accordingly. The system maintains memory between inputs to ensure continuity and avoid losing prior insights. You will receive an updated, timestamped ICP document reflecting the new data.
Yes. Create separate ICP definitions per campaign or region, or reuse a single ICP with region-specific weighting. The Google Doc output can be duplicated or updated per campaign. This supports parallel targeting streams while keeping a single source of truth for ICP criteria.
The agent handles partial profiles by relying on Airtop-enriched fields that are available. It flags missing attributes for manual review and adjusts scoring accordingly. If critical fields are unavailable, the ICP may be less precise, but the scoring can still guide prioritization. You can re-run enrichment once missing data becomes available to refine the ICP.
Monitor LinkedIn profile URLs, enrich data via Airtop, analyze and synthesize an ICP with Claude AI, and create a Google Doc-ready ICP definition and scoring model.
