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Data Enrichment

Build AI agents that automatically fill CRM data, enrich leads, and complete customer records at enterprise scale. Master the search-and-extract pattern for enrichment operations, from LinkedIn company data collection to lead scoring workflows.

Learn the Pattern

Understand the enrichment workflow

Get Started

Start with a LinkedIn example

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Why Standard Scraping Falls Short

Standard Scraping Stack

~50% success rate on protected sites like LinkedIn due to anti-bot measuresSlow SERP responses (2-5 seconds) limit throughputRate limits and IP bans break batch processing at scaleManual proxy management increases operational riskUnreliable beyond 1K concurrent enrichment jobs

Bright Data Stack

95%+ enrichment success, including on protected sites50K+ concurrent extractions with 99.99% uptimeAutomated proxy rotation, unblocking, and CAPTCHA solvingGDPR/CCPA-ready with enterprise-grade security controlsGlobal proxy network (400M+ monthly residential IPs) for broad market coverage

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Complexity Handling

Address common challenges in enrichment systems:
  • LinkedIn’s aggressive anti-bot measures - Automatically bypass with Web Unlocker
  • CAPTCHA challenges - Automatic CAPTCHA solving with no manual intervention
  • Rate limiting - Intelligent rate management and proxy rotation
  • Data quality issues - Built-in validation and error handling
Bright Data’s infrastructure solves these with automatic CAPTCHA solving, intelligent rate management, and production-ready reliability.

Automatic CAPTCHA Solving

Never get blocked by CAPTCHAs or bot detection

Rate Management

Intelligent rate limiting and proxy rotation

Data Validation

Built-in validation ensures data quality

Error Handling

Robust error handling for production reliability

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Scalability

Scale from enriching hundreds of leads to processing millions of records with the same infrastructure. Built for enrichment patterns like:
  • Parallel processing for throughput
  • Error handling for reliability
  • Data validation for quality

Parallel Processing

Process thousands of leads simultaneously with enterprise-scale infrastructure

Error Handling

Robust error handling ensures reliability at scale

Data Validation

Built-in validation ensures high-quality enriched data

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The Enrichment Pattern

The enrichment pattern typically follows these steps:
  1. Input - Receive a list of leads or records that need enrichment
  2. Search - Search for each lead using SERP API or web scraping
  3. Extract - Extract relevant data from search results
  4. Validate - Validate the extracted data for quality
  5. Enrich - Add the enriched data to your CRM or database
  6. Monitor - Monitor success rates and data quality
1

Prepare Input Data

Prepare your list of leads or records that need enrichment. Include identifiers like company names, domains, or email addresses.
[
  {
    "company_name": "Example Corp",
    "domain": "example.com",
    "email": "contact@example.com"
  }
]
2

Search for Data

Use SERP API or web scraping to search for each lead and find relevant information.
const response = await fetch('https://api.brightdata.com/datasets/v3/trigger?dataset_id=YOUR_DATASET_ID', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify([{
    url: 'https://www.google.com/search',
    keyword: 'Example Corp company information',
    country: 'US'
  }])
});
3

Extract and Validate

Extract relevant data from search results and validate for quality.
Use data validation endpoints to ensure the extracted data meets your quality standards.
4

Enrich Records

Add the enriched data to your CRM or database.
Successfully enriched leads are saved with complete contact information.

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Contact Enrichment - LinkedIn Example

Enrich leads with LinkedIn company data:

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Step 1: Search LinkedIn

Search for company information on LinkedIn: 
const response = await fetch('https://api.brightdata.com/datasets/v3/trigger?dataset_id=YOUR_LINKEDIN_DATASET_ID', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify([{
    url: 'https://www.linkedin.com/company/example-corp',
    company_name: 'Example Corp'
  }])
});

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Step 2: Extract Company Data

Extract company information: 
{
  "company_name": "Example Corp",
  "industry": "Technology",
  "employee_count": "1000-5000",
  "location": "San Francisco, CA",
  "website": "https://example.com",
  "description": "Leading technology company..."
}

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Step 3: Enrich Your CRM

Add the enriched data to your CRM: 
// Add enriched data to your CRM
await crm.addContact({
  company_name: enrichedData.company_name,
  industry: enrichedData.industry,
  employee_count: enrichedData.employee_count,
  location: enrichedData.location,
  website: enrichedData.website
});

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Bulk Processing

Process large volumes of leads efficiently:

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Parallel Processing

Process multiple leads simultaneously: 
const leads = [/* array of leads */];
const enrichmentPromises = leads.map(lead => 
  enrichLead(lead)
);

const enrichedLeads = await Promise.all(enrichmentPromises);

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Batch Processing

Process leads in batches to manage rate limits: 
async function processBatch(leads, batchSize = 10) {
  for (let i = 0; i < leads.length; i += batchSize) {
    const batch = leads.slice(i, i + batchSize);
    await Promise.all(batch.map(lead => enrichLead(lead)));
    // Wait between batches to respect rate limits
    await new Promise(resolve => setTimeout(resolve, 1000));
  }
}

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Common Data Sources

LinkedIn

Company and professional data from LinkedIn

Google Search

Search results for company information and news

Company Websites

Extract company information directly from websites

Social Media

Social media profiles and engagement data

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Error Handling

Implement robust error handling for production reliability: 
async function enrichLeadWithRetry(lead, maxRetries = 3) {
  for (let attempt = 1; attempt <= maxRetries; attempt++) {
    try {
      const result = await enrichLead(lead);
      return result;
    } catch (error) {
      if (attempt === maxRetries) {
        throw error;
      }
      // Exponential backoff
      await new Promise(resolve => setTimeout(resolve, Math.pow(2, attempt) * 1000));
    }
  }
}

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Templates

Use pre-built templates for common enrichment workflows:

LinkedIn Company Enrichment

Template for enriching leads with LinkedIn company data

Email Validation

Template for validating and enriching email addresses

Contact Information

Template for enriching contact information

Company Intelligence

Template for collecting company intelligence data

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Additional Use Cases

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Product Catalog Enrichment

Enrich product databases with up-to-date market data:
  1. Ingest incomplete product records (SKU, name, category).
  2. Scrape pricing, competitor data, and availability from live sources using Web Unlocker.
  3. Merge and deduplicate entries across multiple vendors.
  4. Generate enriched product descriptions, specifications, and metadata.
  5. Sync enriched catalogs to inventory and recommendation systems.

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Research Data Normalization

Standardize and complete research datasets at scale:
  1. Ingest raw data from multiple CSV files or APIs.
  2. Extract and validate company information, rankings, or trends using SERP API.
  3. Normalize inconsistent formats and fill missing values.
  4. Enrich with external APIs and verified data sources.
  5. Output clean, deduplicated datasets for analytics pipelines and ML models.

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Next Steps

SERP API Quickstart

Start collecting search results for enrichment

LinkedIn Scrapers

Use pre-built LinkedIn scrapers for company data

Deep Lookup

Use Deep Lookup for comprehensive data enrichment

Browse Examples

Explore pre-built scrapers for common data sources
Need help? Check out our Data Validation Guide or contact support.