🎣 AI-Powered Contact Intelligence & Enrichment with OpenAI/Anthropic and Supabase

⚡ 58 views · 🎣 Lead Generation & Enrichment

💡 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.

View All Scrapers

Description

AI Contact Enrichment

📋 Template Description

Overview

Automatically enhance and enrich contact data using AI to fill in missing information, generate insights, and create detailed buyer personas. Supports multiple AI providers (OpenAI, Anthropic, etc.) with automatic logging to Supabase.

Description

This workflow transforms incomplete contact records into rich, actionable profiles. By leveraging AI, it can infer job roles, company information, likely pain points, communication preferences, and buying motivations from minimal input data. Perfect for sales and marketing teams looking to improve data quality and personalize outreach.

Key Benefits:

Use Cases:

⚙️ Setup Instructions

Prerequisites

  1. n8n instance (cloud or self-hosted)
  2. AI Provider account (OpenAI, Anthropic, or custom)
  3. Supabase account with database access

Step 1: Configure Environment Variables

Add these to your n8n environment settings:

AI_PROVIDER=openai              # or 'anthropic', 'custom'
AI_API_KEY=your_api_key_here
AI_MODEL=gpt-3.5-turbo         # or 'gpt-4', 'claude-3-sonnet-20240229'
AI_ENDPOINT=                    # Only for custom providers

Recommended Models:

How to set environment variables:

Step 2: Set Up Supabase Database

Create the logging table in your Supabase database:

CREATE TABLE workflow_logs (
  id BIGSERIAL PRIMARY KEY,
  workflow_name TEXT NOT NULL,
  data JSONB NOT NULL,
  ai_response JSONB NOT NULL,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

CREATE INDEX idx_workflow_logs_created_at ON workflow_logs(created_at);
CREATE INDEX idx_workflow_logs_workflow_name ON workflow_logs(workflow_name);

-- Optional: Create a view for enriched contacts
CREATE VIEW enriched_contacts AS
SELECT 
  id,
  data->>'email' as email,
  data->>'name' as name,
  data->>'company' as company,
  ai_response as enrichment_data,
  created_at
FROM workflow_logs
WHERE workflow_name = 'AI Contact Enrichment'
ORDER BY created_at DESC;

To run this SQL:

  1. Open your Supabase project dashboard
  2. Go to the SQL Editor
  3. Paste the SQL above and click “Run”

Step 3: Configure Supabase Credentials in n8n

  1. Go to Settings → Credentials
  2. Click Add Credential → Supabase API
  3. Enter your Supabase URL and API key (found in Project Settings → API)
  4. Name it Supabase API
  5. Click Save

Step 4: Activate the Webhook

  1. Import this workflow into n8n
  2. Click the Activate toggle in the top-right corner
  3. Click on the “Webhook Trigger” node
  4. Copy the Production URL (this is your webhook endpoint)
  5. Save this URL for integration with your applications

Step 5: Test the Workflow

Send a test POST request to the webhook:

curl -X POST https://your-n8n-instance.com/webhook/contact-enrichment \
  -H "Content-Type: application/json" \
  -d '{
    "email": "john.doe@acmecorp.com",
    "name": "John Doe",
    "company": "Acme Corporation",
    "linkedin_url": "https://linkedin.com/in/johndoe"
  }'

Successful Response:

{
  "success": true,
  "workflow": "AI Contact Enrichment",
  "timestamp": "2025-01-14T12:00:00.000Z"
}

📥 Expected Payload Format

The webhook accepts JSON with basic contact information:

Minimal Input

{
  "email": "string (required or name required)",
  "name": "string (required or email required)"
}
{
  "email": "string",
  "name": "string",
  "company": "string",
  "job_title": "string",
  "linkedin_url": "string",
  "phone": "string",
  "location": "string",
  "website": "string"
}

Complete Input Example

{
  "email": "sarah.chen@techstartup.io",
  "name": "Sarah Chen",
  "company": "TechStartup Inc.",
  "job_title": "VP of Marketing",
  "linkedin_url": "https://linkedin.com/in/sarahchen",
  "phone": "+1-555-0123",
  "location": "San Francisco, CA",
  "website": "https://techstartup.io",
  "industry": "B2B SaaS",
  "company_size": "50-200 employees",
  "notes": "Met at SaaS conference 2024"
}

Field Guidelines:

🔄 Workflow Flow

  1. Webhook Trigger: Receives basic contact information from your application, form, or CRM
  2. Process Data: Adds unique ID and timestamp to the incoming data
  3. Prepare AI Request: Configures AI provider settings from environment variables
  4. Call AI API: Sends contact data to AI with enrichment prompt
  5. Save to Supabase: Archives original data and enrichment results
  6. Format Response: Returns success confirmation

🎯 Customization Tips

Enhance AI Prompts for Better Enrichment

Modify the “Prepare AI Request” node to customize enrichment:

// Enhanced prompt for contact enrichment
const systemPrompt = `You are an expert sales intelligence analyst. 
Analyze the provided contact information and generate a comprehensive enrichment including:

1. INFERRED DETAILS: Fill in missing information based on available data
   - Full job title and seniority level
   - Department and reporting structure
   - Years of experience (estimated)
   - Professional background

2. COMPANY INSIGHTS: If company name provided
   - Industry and sub-industry
   - Company size and revenue (estimated)
   - Key products/services
   - Recent news or developments

3. BUYER PERSONA: Create a detailed profile
   - Primary responsibilities
   - Likely pain points and challenges
   - Key priorities and goals
   - Decision-making authority
   - Budget influence level

4. ENGAGEMENT STRATEGY: Provide outreach recommendations
   - Best communication channels
   - Optimal outreach timing
   - Key talking points
   - Personalization suggestions
   - Content interests

5. LEAD SCORE: Rate 1-10 based on:
   - Fit for product/service (specify your ICP)
   - Seniority and decision power
   - Company size and maturity
   - Engagement potential

Return as structured JSON with clear sections.`;

const userMessage = `Contact Information:\n${JSON.stringify($json.data, null, 2)}`;

const aiConfig = {
  provider: $env.AI_PROVIDER || 'openai',
  apiKey: $env.AI_API_KEY,
  model: $env.AI_MODEL || 'gpt-3.5-turbo',
  endpoint: $env.AI_ENDPOINT,
  messages: [
    { role: 'system', content: systemPrompt },
    { role: 'user', content: userMessage }
  ]
};

return { json: { aiConfig, data: $json } };

Add External Data Sources

Enhance enrichment with third-party APIs:

After “Process Data” node, add:

  1. Clearbit/Hunter.io Node: Get verified company data
  2. LinkedIn API: Pull professional information
  3. Company Database: Query internal customer data
  4. Web Scraping: Extract data from company websites

Then merge all data before AI enrichment for best results

Connect to Your CRM

Auto-update contacts after enrichment:

Salesforce Integration:

// Add after "Call AI API" node
// Update Salesforce contact with enriched data
const enrichedData = JSON.parse($json.ai_response);
return {
  json: {
    contactId: $json.data.salesforce_id,
    updates: {
      Description: enrichedData.buyer_persona,
      Custom_Score__c: enrichedData.lead_score,
      Pain_Points__c: enrichedData.pain_points
    }
  }
};

HubSpot Integration:

Pipedrive Integration:

Implement Lead Scoring

Add scoring logic after enrichment:

// Calculate lead score based on enrichment
const enrichment = JSON.parse($json.ai_response);

let score = 0;

// Job title scoring
if (enrichment.seniority === 'C-Level') score += 30;
else if (enrichment.seniority === 'VP/Director') score += 20;
else if (enrichment.seniority === 'Manager') score += 10;

// Company size scoring
if (enrichment.company_size === 'Enterprise') score += 25;
else if (enrichment.company_size === 'Mid-Market') score += 15;

// Decision authority scoring
if (enrichment.decision_authority === 'High') score += 25;
else if (enrichment.decision_authority === 'Medium') score += 15;

// Budget influence
if (enrichment.budget_influence === 'Direct') score += 20;

return { json: { ...enrichment, lead_score: score } };

Add Compliance Checks

Insert before AI processing:

// Check for opt-out or compliance flags
const email = $json.email.toLowerCase();

// Check against suppression list
const suppressedDomains = ['competitor.com', 'spam.com'];
const domain = email.split('@')[1];

if (suppressedDomains.includes(domain)) {
  throw new Error('Contact on suppression list');
}

// Verify email format
const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
if (!emailRegex.test(email)) {
  throw new Error('Invalid email format');
}

return { json: $json };

Batch Enrichment

Process multiple contacts:

  1. Add Spreadsheet File trigger instead of webhook
  2. Add Split In Batches node (process 10-20 at a time)
  3. Run enrichment for each contact
  4. Combine results and export to CSV

🛠️ Troubleshooting

Common Issues

Issue: “Enrichment is too generic”

Issue: “AI_API_KEY is undefined”

Issue: “Enrichment contradicts actual data”

Issue: “Too slow for real-time use”

Issue: “Supabase credentials not found”

Debugging Tips

  1. Test with known contacts first to validate accuracy
  2. Compare AI enrichment against actual data
  3. Check execution logs for API errors
  4. Start with minimal prompt, then enhance gradually
  5. Use “Execute Node” to test individual steps

📊 Analyzing Enriched Data

Query and analyze your enriched contacts:

-- Get all enriched contacts
SELECT * FROM enriched_contacts
ORDER BY created_at DESC;

-- Find high-value leads (assuming scoring implemented)
SELECT 
  email,
  name,
  company,
  ai_response->>'lead_score' as score
FROM enriched_contacts
WHERE (ai_response->>'lead_score')::int > 70
ORDER BY (ai_response->>'lead_score')::int DESC;

-- Analyze enrichment by company
SELECT 
  data->>'company' as company,
  COUNT(*) as contact_count,
  AVG((ai_response->>'lead_score')::int) as avg_score
FROM workflow_logs
WHERE workflow_name = 'AI Contact Enrichment'
  AND ai_response->>'lead_score' IS NOT NULL
GROUP BY data->>'company'
ORDER BY contact_count DESC;

-- Find contacts needing follow-up
SELECT 
  email,
  name,
  ai_response->>'engagement_strategy' as strategy,
  created_at
FROM enriched_contacts
WHERE created_at > NOW() - INTERVAL '7 days'
ORDER BY created_at DESC;

Export Enriched Data

-- Export to CSV
COPY (
  SELECT 
    data->>'email' as email,
    data->>'name' as name,
    data->>'company' as company,
    ai_response->>'job_title' as enriched_title,
    ai_response->>'seniority' as seniority,
    ai_response->>'lead_score' as score
  FROM workflow_logs
  WHERE workflow_name = 'AI Contact Enrichment'
) TO '/tmp/enriched_contacts.csv' WITH CSV HEADER;

📈 Integration Ideas

Form Integration

Automatically enrich new leads from forms:

CRM Integration

Real-time enrichment as contacts enter CRM:

Email Tools

Enhance cold outreach campaigns:

Marketing Automation

Power ABM and segmentation:

Slack Integration

Team notifications and collaboration:

🔒 Security & Compliance Best Practices

Data Protection

  1. Encrypt Sensitive Data: Use environment variables for all credentials
  2. Access Control: Limit webhook access with authentication
  3. Data Retention: Set automatic deletion policies in Supabase
  4. Audit Logging: Track all enrichment activities

Privacy Compliance

  1. GDPR Compliance:
    • Get consent before enriching personal data
    • Allow contacts to request data deletion
    • Document legal basis for processing
  2. CCPA Compliance: Honor do-not-sell requests
  3. Data Minimization: Only enrich necessary fields
  4. Right to Access: Allow contacts to view enriched data

AI Ethics

  1. Bias Awareness: Review AI inferences for bias
  2. Accuracy Validation: Verify critical information
  3. Transparency: Disclose use of AI enrichment
  4. Human Oversight: Review before critical decisions

💡 Best Practices

Input Data Quality

Prompt Engineering

Post-Enrichment Workflow

Performance Optimization

🏷️ Tags

sales-automation, lead-enrichment, ai-automation, crm-integration, data-enrichment, contact-intelligence, buyer-personas, lead-scoring, webhook, supabase, openai, anthropic, b2b-sales

📝 License

This workflow template is provided as-is for use with n8n.

🤝 Support

For questions or issues:

🌟 Example Output

Input:

{
  "email": "mike.johnson@cloudtech.com",
  "name": "Mike Johnson",
  "company": "CloudTech Solutions",
  "job_title": "Director of IT"
}

AI-Generated Enrichment:

{
  "full_title": "Director of Information Technology",
  "seniority": "Director",
  "department": "Technology/IT",
  "experience_years": "10-15",
  "company_insights": {
    "industry": "Cloud Computing",
    "size": "Mid-Market (100-500)",
    "revenue_estimate": "$10M-$50M"
  },
  "buyer_persona": {
    "responsibilities": ["Infrastructure management", "Vendor selection", "Security oversight"],
    "pain_points": ["Legacy system migration", "Cost optimization", "Security compliance"],
    "priorities": ["Scalability", "Cost reduction", "Team efficiency"]
  },
  "engagement_strategy": {
    "best_channels": ["Email", "LinkedIn"],
    "timing": "Tuesday-Thursday, 9-11 AM",
    "talking_points": ["ROI and cost savings", "Security features", "Ease of implementation"],
    "personalization": "Reference cloud migration challenges"
  },
  "lead_score": 75
}

🔄 Version History

🔗 Nodes Used

HTTP Request, Webhook, Supabase

📥 Import

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

📖 Importing guide · 🔑 Credential setup