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:
Smart Data Completion**: Fill in missing contact fields using AI inference
Buyer Persona Generation**: Create detailed profiles from basic information
Universal AI Support**: Works with OpenAI, Anthropic Claude, or custom providers
CRM Enhancement**: Automatically enrich contacts as they enter your system
Lead Qualification**: Assess lead quality and fit based on enriched data
Personalization Engine**: Generate insights for tailored outreach
Data Quality**: Maintain clean, complete contact records
Use Cases:
Sales prospecting and lead enrichment
Marketing persona development
CRM data cleansing and completion
Account-based marketing (ABM) research
Lead scoring and qualification
Personalized email campaign preparation
Contact segmentation and targeting
⚙️ Setup Instructions
Prerequisites
n8n instance (cloud or self-hosted)
AI Provider account (OpenAI, Anthropic, or custom)
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:
Cost-effective**: gpt-3.5-turbo (fast, affordable, good for basic enrichment)
High-quality**: gpt-4 or claude-3-sonnet-20240229 (better inference, deeper insights)
Premium**: claude-3-opus-20240229 (best for complex persona generation)
How to set environment variables:
n8n Cloud**: Go to Settings → Environment Variables
Self-hosted**: Add to your .env file or docker-compose configuration
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:
Open your Supabase project dashboard
Go to the SQL Editor
Paste the SQL above and click "Run"
Step 3: Configure Supabase Credentials in n8n
Go to Settings → Credentials
Click Add Credential → Supabase API
Enter your Supabase URL and API key (found in Project Settings → API)
Name it Supabase API
Click Save
Step 4: Activate the Webhook
Import this workflow into n8n
Click the Activate toggle in the top-right corner
Click on the "Webhook Trigger" node
Copy the Production URL (this is your webhook endpoint)
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": "
[email protected]",
"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)"
}
Recommended Input
{
"email": "string",
"name": "string",
"company": "string",
"job_title": "string",
"linkedin_url": "string",
"phone": "string",
"location": "string",
"website": "string"
}
Complete Input Example
{
"email": "
[email protected]",
"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:
At minimum, provide either email or name
More input fields = better AI enrichment quality
Include linkedin_url for best results
company helps with firmographic enrichment
Any additional context improves accuracy
🔄 Workflow Flow
Webhook Trigger: Receives basic contact information from your application, form, or CRM
Process Data: Adds unique ID and timestamp to the incoming data
Prepare AI Request: Configures AI provider settings from environment variables
Call AI API: Sends contact data to AI with enrichment prompt
Save to Supabase: Archives original data and enrichment results
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:
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
COMPANY INSIGHTS: If company name provided
Industry and sub-industry
Company size and revenue (estimated)
Key products/services
Recent news or developments
BUYER PERSONA: Create a detailed profile
Primary responsibilities
Likely pain points and challenges
Key priorities and goals
Decision-making authority
Budget influence level
ENGAGEMENT STRATEGY: Provide outreach recommendations
Best communication channels
Optimal outreach timing
Key talking points
Personalization suggestions
Content interests
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: $json } };
Add External Data Sources
Enhance enrichment with third-party APIs:
After "Process Data" node, add:
Clearbit/Hunter.io Node: Get verified company data
LinkedIn API: Pull professional information
Company Database: Query internal customer data
Web Scraping: Extract data from company websites
Then merge all data before AI enrichment for best results
Connect to Your CRM
Auto-update contacts after enrichment:
Salesforce Integration:
// Add after "Call AI API" node
// Update Salesforce contact with enriched data
const enrichedData = JSON.parse($json.ai_response);
return {
json: {
contactId: $json.data.salesforce_id,
updates: {
Description: enrichedData.buyer_persona,
Custom_Score__c: enrichedData.lead_score,
Pain_Points__c: enrichedData.pain_points
}
}
};
HubSpot Integration:
Add HubSpot node to update contact properties
Map enriched fields to custom HubSpot properties
Pipedrive Integration:
Use Pipedrive node to update person records
Add custom fields for AI insights
Implement Lead Scoring
Add scoring logic after enrichment:
// Calculate lead score based on enrichment
const enrichment = JSON.parse($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 = $json.email.toLowerCase();
// Check against suppression list
const suppressedDomains = ['competitor.com', 'spam.com'];
const domain = email.split('@')[1];
if (suppressedDomains.includes(domain)) {
throw new Error('Contact on suppression list');
}
// Verify email format
const emailRegex = /^+@+\.+$/;
if (!emailRegex.test(email)) {
throw new Error('Invalid email format');
}
return { json: $json };
Batch Enrichment
Process multiple contacts:
Add Spreadsheet File trigger instead of webhook
Add Split In Batches node (process 10-20 at a time)
Run enrichment for each contact
Combine results and export to CSV
🛠️ Troubleshooting
Common Issues
Issue: "Enrichment is too generic"
Solution**: Provide more input data (company, job title, LinkedIn)
Use GPT-4 or Claude models for better inference
Enhance the system prompt with specific instructions
Issue: "AI_API_KEY is undefined"
Solution**: Ensure environment variables are set correctly
Verify variable names match exactly (case-sensitive)
Issue: "Enrichment contradicts actual data"
Solution**: AI makes inferences - always validate critical information
Add validation step to check enriched data against known facts
Use external APIs for verification
Issue: "Too slow for real-time use"
Solution**: Implement queue system for async processing
Use faster models (gpt-3.5-turbo) for speed
Process in batches during off-peak hours
Issue: "Supabase credentials not found"
Solution**: Check credential name matches exactly: "Supabase API"
Verify Supabase URL and API key are correct
Debugging Tips
Test with known contacts first to validate accuracy
Compare AI enrichment against actual data
Check execution logs for API errors
Start with minimal prompt, then enhance gradually
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:
Typeform**: Trigger on form submission
Google Forms**: Use Google Sheets trigger
Calendly**: Enrich after meeting booking
Webflow Forms**: Webhook trigger from form
CRM Integration
Real-time enrichment as contacts enter CRM:
Salesforce**: Trigger on new lead/contact creation
HubSpot**: Enrich on form submission or import
Pipedrive**: Auto-enrich new persons
Close**: Webhook on lead creation
Email Tools
Enhance cold outreach campaigns:
Instantly.ai**: Enrich before campaign launch
Lemlist**: Generate personalization variables
Apollo.io**: Supplement with AI insights
Mailshake**: Enrich prospect lists
Marketing Automation
Power ABM and segmentation:
Marketo**: Enrich leads for scoring
Pardot**: Enhance prospect profiles
ActiveCampaign**: Personalization data
Klaviyo**: E-commerce customer insights
Slack Integration
Team notifications and collaboration:
Send enrichment summaries to sales channel
Notify reps of high-value leads
Share persona insights with marketing
Alert on key account contacts
🔒 Security & Compliance Best Practices
Data Protection
Encrypt Sensitive Data: Use environment variables for all credentials
Access Control: Limit webhook access with authentication
Data Retention: Set automatic deletion policies in Supabase
Audit Logging: Track all enrichment activities
Privacy Compliance
GDPR Compliance:
Get consent before enriching personal data
Allow contacts to request data deletion
Document legal basis for processing
CCPA Compliance: Honor do-not-sell requests
Data Minimization: Only enrich necessary fields
Right to Access: Allow contacts to view enriched data
AI Ethics
Bias Awareness: Review AI inferences for bias
Accuracy Validation: Verify critical information
Transparency: Disclose use of AI enrichment
Human Oversight: Review before critical decisions
💡 Best Practices
Input Data Quality
Always include email or full name** as anchor point
Add LinkedIn URLs** for 50% better accuracy
Provide company name** for firmographic insights
Include any known details** - more data = better results
Prompt Engineering
Be specific** about your ideal customer profile (ICP)
Request structured output** (JSON format)
Define scoring criteria** that match your business
Ask for actionable insights** not just descriptions
Post-Enrichment Workflow
Always validate** critical information before use
Review AI inferences** for accuracy and bias
Update CRM promptly** to maintain data freshness
Track enrichment ROI** (conversion rates, time saved)
Performance Optimization
Batch process** during off-peak hours
Use appropriate models** (gpt-3.5 for speed, gpt-4 for quality)
Cache common enrichments** to reduce API costs
Set rate limits** to avoid API throttling
🏷️ 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:
n8n Community Forum: https://community.n8n.io
n8n Documentation: https://docs.n8n.io
🌟 Example Output
Input:
{
"email": "
[email protected]",
"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
v1.0.0** (2025-01-14): Initial release with universal AI provider support
