Personalized Email Outreach with LinkedIn & Crunchbase Data and Gemini AI Review
VerifiedAutomates personalized cold email creation using LinkedIn, Crunchbase data, and Gemini AI review.
What this workflow does
This automation enriches lead lists with LinkedIn and Crunchbase data through HTTP requests, then uses Gemini-powered AI Agents to draft and judge personalized emails before writing approved results back to the source.
It is designed for sales teams and growth operators who need consistent, research-backed outreach without manual data gathering or copy review.
Who is this for?
Sales development reps, founders, and growth marketers running outbound campaigns who maintain leads in a data table and want AI-assisted personalization at scale.
What problem it solves
Manually researching leads across LinkedIn and Crunchbase then drafting and QA’ing cold emails is slow and inconsistent. This workflow automates enrichment, drafting, and approval so only high-quality emails are written back.
What it automates
SaaS founder outreach
Enrich a list of target startup founders with company funding data and recent LinkedIn activity, then generate and judge personalized emails before writing approved drafts back to the table.
Agency lead gen
Process a weekly batch of marketing-agency prospects by pulling Crunchbase revenue signals and LinkedIn job changes to create tailored service-offer emails that pass the judge agent.
Recruiter sourcing
Take a candidate list, enrich each profile with LinkedIn experience and Crunchbase company headcount, then produce personalized recruiting messages that are auto-reviewed before saving.
How the workflow works
The 4 nodes in this automation, in order.
- 1HTTP RequesthttpRequest
- 2AI Agent@n8n/n8n-nodes-langchain.agent
- 3Structured Output Parser@n8n/n8n-nodes-langchain.outputParserStructured
- 4Google Gemini Chat Model@n8n/n8n-nodes-langchain.lmChatGoogleGemini
Apps & integrations used
How to set up Personalized Email Outreach with LinkedIn & Crunchbase Data and Gemini AI Review
- 1Import the workflow JSON into n8n and open the canvas.
- 2Add your RapidAPI key for LinkedIn and Crunchbase endpoints in the HTTP Request nodes.
- 3Connect your Google Gemini API key to the Chat Model node.
- 4Point the first node at your data table containing “unprocessed” lead rows.
- 5Run the workflow once in test mode to verify one row completes enrichment → draft → judge → write-back.
- 6Activate the workflow and schedule it or trigger via webhook for new rows.
How to customize this workflow
- →Swap Google Gemini for another supported model in the Chat Model node.
- →Change the trigger from polling the data table to a webhook or schedule node.
- →Add an extra Structured Output Parser step if you need additional fields validated.
- →Insert a Wait node after HTTP calls to respect any API rate limits.
Personalized Email Outreach with LinkedIn & Crunchbase Data and Gemini AI Review: pros & cons
Pros
- +Hands-off enrichment and two-agent QA pattern
- +Only approved drafts are written back to the source table
- +Uses native n8n Structured Output Parser for reliable JSON
- +Beginner-friendly with clear node roles
Cons
- –Depends on paid RapidAPI credits for LinkedIn/Crunchbase calls
- –Judge agent can reject too many drafts if prompts are not tuned
- –Async Wait nodes add latency on large batches
Frequently asked questions
It reads unprocessed leads, enriches them via HTTP calls, drafts emails with Gemini, runs a judge agent, and writes only approved emails back to the same row.
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