Use Case

Lead Generation & Contact Extraction

Extract contact information from business directories, company pages, and industry listings to build targeted lead lists at scale.

Responsible Use

Always comply with applicable data protection regulations (GDPR, CCPA) when collecting personal information. Only collect publicly available business contact data and respect opt-out requests.

The Problem

Sales and marketing teams need qualified leads, but building contact lists manually is extremely time-consuming. Data is spread across directories, company websites, and professional networks:

Business directories have thousands of listings but no export functionality
Company websites have contact info in different formats and locations
Job boards reveal hiring companies and decision-maker roles
Industry listings and trade association pages have member directories

Solution Architecture

Combine crawling, extraction schemas, and batch processing to build lead lists efficiently:

1. Discover

POST /crawl or POST /search to find business listings, company pages, and directory entries.

2. Extract

POST /scrape with extraction_schema targeting contact fields: company name, email, phone, address, industry.

3. Scale

POST /batch to process hundreds of company pages in parallel and build your lead database.

Quick Example

Extract business contact information from a company page:

Python

import requests

response = requests.post(
    "https://api.alterlab.io/api/v1/scrape",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "url": "https://example-company.com/contact",
        "extraction_schema": {
            "type": "object",
            "properties": {
                "company_name": {"type": "string"},
                "email": {"type": "string"},
                "phone": {"type": "string"},
                "address": {"type": "string"},
                "industry": {"type": "string"},
                "employee_count": {"type": "string"}
            }
        }
    }
)

contact = response.json().get("filtered_content", {})
print(f"{contact.get('company_name')}: {contact.get('email')}")

Advanced Patterns

Directory Scraping

Crawl business directories to discover company pages, then batch extract contact information:

Python

import requests

contact_schema = {
    "type": "object",
    "properties": {
        "company_name": {"type": "string"},
        "email": {"type": "string"},
        "phone": {"type": "string"},
        "website": {"type": "string"},
        "address": {"type": "string"},
        "industry": {"type": "string"}
    }
}

# Step 1: Crawl a business directory to find company pages
crawl_resp = requests.post(
    "https://api.alterlab.io/api/v1/crawl",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "url": "https://directory.example.com/category/software",
        "max_pages": 50,
        "webhook_url": "https://your-app.com/webhooks/leads"
    }
)

# Step 2: When crawl results arrive, batch extract contacts
company_urls = [
    "https://company1.com/about",
    "https://company2.com/contact",
    # ... URLs from crawl results
]

batch_resp = requests.post(
    "https://api.alterlab.io/api/v1/batch",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "requests": [
            {"url": url, "extraction_schema": contact_schema}
            for url in company_urls
        ]
    }
)

job_id = batch_resp.json().get("job_id")
print(f"Lead extraction batch: {job_id}")

Company Enrichment

Enrich existing lead lists by scraping company websites for additional data points:

Python

import requests

def enrich_company(api_key, website_url):
    """Enrich a company record with data from their website."""
    enrichment_schema = {
        "type": "object",
        "properties": {
            "company_name": {"type": "string"},
            "description": {"type": "string"},
            "industry": {"type": "string"},
            "headquarters": {"type": "string"},
            "founded_year": {"type": "string"},
            "employee_count": {"type": "string"},
            "key_products": {"type": "array"}
        }
    }

    resp = requests.post(
        "https://api.alterlab.io/api/v1/scrape",
        headers={"X-API-Key": api_key},
        json={
            "url": website_url,
            "extraction_schema": enrichment_schema
        }
    )
    return resp.json().get("filtered_content", {})

# Enrich existing leads
leads = [
    {"company": "Acme Corp", "website": "https://acme.com/about"},
    {"company": "Widget Inc", "website": "https://widget.io/about"},
]

for lead in leads:
    enriched = enrich_company("YOUR_API_KEY", lead["website"])
    lead.update(enriched)
    print(f"{lead['company']}: {enriched.get('industry', 'N/A')}")

Caching for Enrichment

Company about pages rarely change. Leave caching enabled (the default) to avoid re-scraping pages you've already enriched. Cache hits are free and instant.

Last updated: June 2026

Lead Generation & Contact Extraction

The Problem

Solution Architecture

1. Discover

2. Extract

3. Scale

Quick Example

Advanced Patterns

Directory Scraping

Company Enrichment

Batch Scraping Guide

JSON Schema Filtering

Structured Extraction Tutorial

Pricing Reference

Lead Generation & Contact Extraction

The Problem

Solution Architecture

1. Discover

2. Extract

3. Scale

Quick Example

Advanced Patterns

Directory Scraping

Company Enrichment

Batch Scraping Guide

JSON Schema Filtering

Structured Extraction Tutorial

Pricing Reference

Lead Generation & Contact Extraction

The Problem

Solution Architecture

1. Discover

2. Extract

3. Scale

Quick Example

Advanced Patterns

Directory Scraping

Company Enrichment

Related Guides

Batch Scraping Guide

JSON Schema Filtering

Structured Extraction Tutorial

Pricing Reference

Lead Generation & Contact Extraction

The Problem

Solution Architecture

1. Discover

2. Extract

3. Scale

Quick Example

Advanced Patterns

Directory Scraping

Company Enrichment

Related Guides

Batch Scraping Guide

JSON Schema Filtering

Structured Extraction Tutorial

Pricing Reference