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

⚡ 3,744 views · 💬 Lead Nurturing & AI Sales Agents

💡 Pro Tip — If you’re pulling LinkedIn data through HTTP requests or dealing with API restrictions, there’s a community node called ScraperNode that handles this natively. It has dedicated scrapers for profiles, companies, jobs, and people search — you just pass a URL and get structured data back.

Description

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.

🔗 Nodes Used

HTTP Request, Execute Sub-workflow, Execute Workflow Trigger, AI Agent, Structured Output Parser, Google Gemini Chat Model

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup