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Personalized email outreach with LinkedIn & Crunchbase data and Gemini AI review

Johnny RafaelJohnny Rafael
2679 views
2/3/2026
Data ExtractionNotionDatabaseAPI IntegrationJSON Formatting
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AI-Enriched Cold Outreach: Research → Draft → QA → Write-back

What this template does

Automates cold email drafting from a lead list by:

  1. Enriching each lead with LinkedIn profile, LinkedIn company, and Crunchbase data
  2. Generating a personalized subject + body with Gemini
  3. Auto-reviewing with a Judge agent and writing back only APPROVED drafts to your Data Table

Highlights

  • Hands-off enrichment via RapidAPI; raw JSON stored back on each row
  • Two-agent pattern: Creative Outreach Agent (draft) + Outreach Email Judge (QA)
  • Structured outputs guaranteed by LangChain Structured Output Parsers
  • Data Table–native: reads “unprocessed” rows, writes results to the same row
  • Async polling with Wait nodes for scraper task results

How it works (flow)

  1. Trigger: Manual (replace with Cron if needed)
  2. Fetch leads: Data Table “Get row(s)” filters rows where email_subject is empty (pending)
  3. Loop: Split in Batches iterates rows
  4. Enrichment (runs in parallel):
    • LinkedIn profile: HTTP (company_url) → Wait → Results → Data Table update → linkedin_profile_scrape
    • LinkedIn company: HTTP (company_url) → Wait → Results → Data Table update → linkedin_company_scrape
    • Crunchbase company: HTTP (url_search) → Wait → Results → Data Table update → crunchbase_company_scrape
      (All calls use host cold-outreach-enrichment-scraper with a RapidAPI key.)
  5. Draft (Gemini): “Agent One” composes a concise, personalized email using row fields + enrichment + ABOUT ME block.
    • Structured Output Parser enforces: 
      {
  "email_subject": "text",
  "email_content": "text"
}
  6. Prep for QA: “Email Context” maps email_subject, email_content, and email for the judge.
  7. QA (Judge): “Judge Agent” returns APPROVED or REVISE (brief feedback allowed).
  8. Route:
    • If APPROVED → Data Table “Update row(s)” writes email_subject + email_body (a.k.a. email_content) back to the row.
    • If REVISE → Skipped; loop continues.

Required setup

Data Table: “email_linkedin_list” (or your own) with at least:

  • email, First_name, Last_name, Title, Location, Company_Name, Company_site,
    Linkedin_URL, company_linkedin (if used), Crunchbase_URL,
    email_subject, email_body,
    linkedin_profile_scrape, linkedin_company_scrape, crunchbase_company_scrape (string fields for JSON).

Credentials:

  • RapidAPI key for cold-outreach-enrichment-scraper (store securely as credential, not hardcoded)
  • Google Gemini (PaLM) API configured in the Google Gemini Chat Model node

ABOUT ME block:
Replace the sample persona (James / CEO / Company Sample / AI Automations) with your own.

Nodes used

  • Data Table
  • HTTP Request:
  • AI Agent:
  • Google Gemini Chat Model
  • Split in Batches: Main Loop
  • Set: RapidAPI-Key

Customization ideas

  • Process flags: Add email_generated_at or processed boolean to prevent reprocessing.
  • Human-in-the-loop: Send drafts to Slack/Email for spot check before write-back.
  • Delivery: After approval, optionally email the draft to the sender for review.

Quotas & costs

  • RapidAPI: Multiple calls per row (three tasks + result polls).
  • Gemini: Token usage for generator + judge per row.
    Tune batch size and schedule accordingly.

Privacy & compliance

You are scraping and storing person/company data.
Ensure lawful basis, respect ToS, and minimize stored data.

n8n Workflow: Basic Workflow Structure Example

This n8n workflow demonstrates a foundational structure for processing data, applying conditional logic, and interacting with external services, including an AI model. It includes elements for manual triggering, data manipulation, looping, and sub-workflow execution.

What it does

This workflow outlines a series of generic steps, providing a template for more complex automations:

  1. Manual Trigger: The workflow can be initiated manually, making it suitable for testing or on-demand execution.
  2. Sub-workflow Trigger: It can also be triggered by another n8n workflow, enabling modular and reusable workflow components.
  3. Data Table: Initializes or processes data within a data table, acting as a starting point for data manipulation.
  4. Edit Fields (Set): Modifies or adds fields to the incoming data, allowing for data enrichment or restructuring.
  5. Loop Over Items (Split in Batches): Processes data in batches, useful for handling large datasets efficiently and iteratively.
  6. Conditional Logic (If): Routes the workflow based on specified conditions, enabling branching logic.
  7. HTTP Request: Makes requests to external APIs or web services, demonstrating integration capabilities.
  8. Wait: Pauses the workflow for a specified duration, useful for rate limiting or timed operations.
  9. Execute Sub-workflow: Calls and executes another n8n workflow as a sub-process, promoting modularity.
  10. AI Agent (LangChain): Integrates with an AI agent, likely for advanced natural language processing, decision-making, or content generation.
  11. Google Gemini Chat Model: Specifically utilizes the Google Gemini AI model for chat-based interactions or content generation.
  12. Structured Output Parser: Processes and structures the output from the AI model, ensuring data consistency.
  13. Sticky Note: Provides a textual annotation within the workflow for documentation or explanations.

Prerequisites/Requirements

  • n8n Instance: An active n8n instance to import and run the workflow.
  • Google Gemini API Key: Required for the Google Gemini Chat Model node to function (if configured for actual use).
  • LangChain Integration: The n8n LangChain nodes (AI Agent, Structured Output Parser, Google Gemini Chat Model) are part of the n8n LangChain integration, which needs to be available in your n8n instance.

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials: If you intend to use the Google Gemini Chat Model or HTTP Request nodes with external services, ensure you configure the necessary credentials (e.g., API keys) within n8n.
  3. Adjust Nodes: Review each node's configuration:
    • Data table: Populate with your initial data or connect it to a data source.
    • Edit Fields (Set): Define the fields you want to modify or add.
    • Loop Over Items (Split in Batches): Adjust batch size as needed.
    • If: Set up your desired conditional logic for branching.
    • HTTP Request: Specify the URL, method, headers, and body for your API calls.
    • Wait: Configure the delay duration.
    • Execute Sub-workflow: Specify the workflow ID or name of the sub-workflow to execute.
    • AI Agent, Google Gemini Chat Model, Structured Output Parser: Configure prompts, models, and parsing rules according to your AI task.
  4. Execute:
    • To trigger manually, click "Execute workflow" in the n8n editor.
    • To trigger via another workflow, ensure the Execute Workflow Trigger node is properly linked.

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