Enrich LinkedIn profiles and draft cold emails with Gemini

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Cold email campaigns fail when outreach feels copy-pasted. This AI automation pulls deep career data from LinkedIn profiles and uses that context to write genuinely personalised emails, so every prospect receives a message that references their actual professional history.

What This Automation Does

Reads a list of LinkedIn profile URLs from Google Sheets on a recurring schedule, skipping any leads already processed
Scrapes each profile via Apify to extract career history, role summaries, and the About section
Saves enriched profile data back into Google Sheets as a structured lead database, ready for review or export
Passes each profile to Gemini, which generates a unique, context-aware cold email grounded in that individual's career journey

Tools Used

Google Sheets
Apify
Google Gemini
n8n

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How To Build It

Connect Google Sheets as the lead source

Link Google Sheets so the automation reads each row containing a LinkedIn URL, filtering out any leads where the profile data column is already populated to avoid duplicate processing.

Pull live profile data via Apify

Configure an Apify LinkedIn scraper actor to visit each profile URL and return structured data including the About section, job titles, company names, tenures, and role descriptions.

Write enriched career data back to the sheet

Map the scraped profile fields into a dedicated column in the same Google Sheets row, creating a single enriched record per lead that persists between scheduled runs.

Generate a personalised email with Gemini

Send the full enriched profile to Gemini with a prompt that instructs it to reference specific career milestones and craft an opening line unique to that prospect, returning a structured output with subject line and body.

Store finished emails alongside profile records

Write the Gemini-generated subject and email body into dedicated columns on the same Google Sheets row, giving clients a campaign-ready sheet where each lead has a matched, personalised message ready to send.

Pitfalls

Apify scraping actors break whenever LinkedIn updates its page structure, so build in an alert for empty or malformed profile responses rather than silently writing blank data to the sheet.
Gemini can drift toward generic phrasing when profile data is thin, especially for leads with minimal LinkedIn activity, so add a conditional branch that flags low-data profiles for manual review instead of generating a weak email.
Google OAuth tokens expire and LinkedIn scraping quotas reset on their own schedules, so a long scheduled run that hits an auth failure mid-batch will leave half the sheet processed and half blank with no indication of where it stopped.

FAQ

Can I build this without coding?

Yes. The workflow uses n8n's visual interface and pre-built Apify and Gemini integrations, so no custom code is required. The only configuration that gets close to technical is structuring the Gemini prompt and mapping the Apify output fields to the correct sheet columns.

How long does it take?

Most freelancers complete an initial working version in four to six hours. Refinement time depends on how much you customise the Gemini prompt and how many edge cases you handle for incomplete LinkedIn profiles.

What can I charge?

Pricing is typically structured as a per-campaign fee based on the number of leads enriched, or as a monthly retainer for clients who run ongoing outreach. Factor in Apify actor usage costs when scoping any fixed-price engagement.

Which tool is required vs optional?

Apify and Google Sheets are required because the workflow is built around LinkedIn scraping and sheet-based data storage. Google Gemini can be swapped for another large language model if a client already has a preferred provider, though the structured output parsing will need to be reconfigured.

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