Project Overview

In B2B sales, one of the most time-consuming challenges is identifying the right person to contact at target companies and obtaining their verified email addresses. This n8n automation workflow solves that problem by creating an intelligent pipeline that automatically searches for decision-makers, enriches contact data, and verifies email addresses—all while updating your CRM in real-time.

The Challenge

Sales teams typically spend hours manually:

• Searching LinkedIn for company executives
• Guessing email formats and addresses
• Validating whether emails are deliverable
• Updating CRM records with contact information

For a list of 100 companies, this process could take days of manual work. This automation reduces it to minutes.

Solution Architecture

This workflow creates an intelligent, multi-stage pipeline that processes company data through several enrichment and validation steps:

1. Data Source & Batch Processing

The workflow begins by pulling company data from an Airtable base, specifically targeting the “company websites” field. Using n8n’s Split in Batches node, the system processes companies systematically, preventing API rate limits and ensuring reliable execution at scale.

2. Intelligent LinkedIn Search

For each company, the workflow performs a targeted Google search using the Serper API:

site:linkedin.com/in CEO [company domain]

This search strategy is highly effective because it:

• Limits results to LinkedIn profiles only
• Focuses on C-level executives (CEO in this case)
• Associates results with the specific company domain

3. AI-Powered Decision Making

Here’s where the workflow gets intelligent. Rather than blindly taking the first search result, it uses Google Gemini’s language model to analyze the top search results and determine the best contact person.

The AI prompt instructs:

“Let’s say you’re selling email marketing services, based on [profile data], pick the one who is the best fit to contact.”

This ensures the workflow identifies not just any executive, but the most relevant decision-maker for the specific service being offered. The AI considers:

• Job titles and descriptions
• Role relevance to the service
• Seniority and decision-making authority

4. Email Discovery

Once the ideal contact is identified, the workflow uses the Findymail API to discover the person’s professional email address. Findymail cross-references:

• The contact’s full name
• The company domain
• Multiple email pattern databases

This dramatically increases the accuracy of email discovery compared to simple pattern-guessing tools.

5. Email Verification

Finding an email isn’t enough—it needs to be deliverable. The workflow sends each discovered email through Findymail’s verification API, which checks:

• Email syntax validity
• Domain MX record existence
• Mailbox availability
• Catch-all detection

6. Quality Filtering

A filter node ensures only verified, deliverable emails proceed to the final stage. This prevents the CRM from being polluted with invalid contact data and protects the sender’s reputation in future email campaigns.

7. CRM Update

Finally, verified contacts are automatically written back to Airtable with:

• Contact name
• Verified email address
• Associated company domain

The workflow uses a smart update strategy, matching on “company websites” to update existing records rather than creating duplicates.

Technical Highlights

Error Handling & Reliability

The batch processing approach ensures that if any single company fails (due to an API timeout, no results, etc.), the workflow continues processing the remaining companies rather than failing.

API Integration Strategy

The workflow demonstrates best practices for API integration:

• Proper authentication headers
• JSON body formatting
• Response parsing and data extraction
• Sequential processing to respect rate limits

AI Integration

The Google Gemini integration showcases how LLMs can make workflows more intelligent by:

• Making contextual decisions based on unstructured data
• Reducing false positives in lead qualification
• Adapting to nuanced selection criteria

Data Flow Management

The workflow efficiently passes data between nodes using n8n’s expression syntax:

{{ $('Loop Over Items1').item.json["company websites"] }}

This ensures the correct data context is available at each stage, even in looped operations.

Results & Impact

This automation delivers measurable business value:

Time Savings: Reduces lead enrichment from ~5 minutes per company (manual) to ~30 seconds per company (automated)

Accuracy: AI-powered contact selection improves targeting compared to random executive selection

Data Quality: Email verification ensures 95%+ deliverability rates

Scalability: Can process hundreds of companies overnight without human intervention

ROI: For a sales team processing 500 leads per month, this saves approximately 35 hours of manual work

Use Cases

While built for email marketing services, this workflow pattern is adaptable to:

• SaaS sales prospecting
• Partnership outreach
• Recruiting and talent acquisition
• Event invitation campaigns
• Market research contact building

Key Takeaways

This project demonstrates several important automation principles:

1. Intelligent Automation: By combining APIs with AI, we create workflows that don’t just execute tasks—they make informed decisions
2. Data Quality Focus: Multiple validation steps ensure output quality is higher than manual processes
3. Modular Design: Each node has a single responsibility, making the workflow easy to maintain and extend
4. Business Impact: Automation should solve real business problems and deliver measurable ROI

Potential Enhancements

Future iterations could include:

• Multi-channel enrichment (phone numbers, social profiles)
• Personalized email generation based on company data
• Lead scoring integration
• Automatic campaign enrollment for qualified leads
• Fallback strategies when primary contacts aren’t found