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Score LinkedIn companies with AI and sync to Google Sheets

Finds, scores, and imports 100+ LinkedIn prospects into a Google Sheets CRM automatically. Built with OpenAI for B2B sales teams.

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B2B sales teams waste hours manually researching LinkedIn companies, guessing at fit, and copy-pasting data into spreadsheets. This AI automation replaces that entire process with a system that finds, qualifies, and logs prospects without human intervention.

What This Automation Does

  • Searches LinkedIn for companies matching a defined set of criteria and retrieves detailed profile data for each one
  • Filters results by quality signals before passing them to AI, so only relevant companies consume scoring resources
  • Uses OpenAI to evaluate each company against an ideal customer profile and assign a fit score
  • Writes scored prospects directly into a Google Sheets CRM while checking for and preventing duplicate entries

Tools Used

  • OpenAI
  • Google Sheets
  • n8n

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Where to Get Hired for This Skill

On Contra, top freelancers across this stack have earned 113 combined verified reviews from real client projects.

Source: Contra freelancer search · refreshed 26 May 2026

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How To Build It

Connect the LinkedIn data source

Authenticate with the Ghost Genius API and configure the search parameters so the workflow pulls company records matching the client's target industry, size, and location filters.

Filter companies by quality signals

Define threshold rules around indicators like employee count, founded year, or profile completeness so that only companies worth scoring proceed further, reducing unnecessary API calls and noise in the final output.

Build the AI scoring prompt

Write a structured OpenAI prompt that receives each company's details and returns a numerical fit score alongside a short rationale, grounded in the client's specific ideal customer profile criteria.

Map scored data to the CRM columns

Format each scored company record so that company name, score, rationale, LinkedIn URL, and key firmographic fields land in the correct Google Sheets columns in a consistent, client-readable layout.

Prevent duplicates on every run

Add a lookup step that checks the existing Google Sheets rows before writing, so re-running the workflow against overlapping search results never creates duplicate prospect entries in the client's CRM.

Pitfalls

  • LinkedIn data APIs rate-limit aggressively, and sending too many requests in a short window will cause the workflow to stall mid-run, leaving the Google Sheet partially populated without any obvious error message for the client.
  • OpenAI scoring results drift when the prompt is vague, producing inconsistent fit scores across runs and making the output look unreliable to sales teams who compare batches over time.
  • Google Sheets OAuth tokens expire silently, meaning the workflow appears to run successfully but writes nothing, and the client only notices days later when no new prospects have appeared.

FAQ

Can I build this without coding?

Yes. The workflow uses HTTP request nodes and pre-built Google Sheets and OpenAI connectors, so configuration is mostly form-filling and prompt writing rather than custom code. Familiarity with API keys and JSON responses will help but is not required.

How long does it take?

Most freelancers complete a working version in four to six hours, including testing with a live LinkedIn search and validating that scores write correctly into Google Sheets. Allowing extra time for prompt refinement based on client feedback is recommended.

What can I charge?

Pricing is your decision based on your market and experience. The value proposition to a client is a ready-to-action, pre-qualified prospect list delivered into a tool they already use, which positions this as a high-value deliverable rather than a simple task.

Which tool is required vs optional?

OpenAI and Google Sheets are both core to the workflow as described. The Ghost Genius API is required for LinkedIn data access. The Google Sheets CRM destination could in principle be swapped for another spreadsheet or database, but OpenAI is harder to replace without rebuilding the scoring logic.

This is original DigiNo analysis. The underlying automation pattern is a community workflow template – view the original on n8n.

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