LinkedIn Company & People Data - SpiderIQ by Di-Atomic

Overview

SpiderPublicLinkedin allows you to extract LinkedIn company and people profile data using the Voyager API. This is useful for:

B2B Lead Generation: Build databases of companies and decision-makers
Company Research: Get company size, industry, specialties, and headquarters
People Research: Extract experience, education, skills, and contact info
Competitive Analysis: Monitor competitor companies and their teams
Sales Intelligence: Find key contacts at target accounts

LinkedIn Accounts RequiredUnlike SpiderPublicInstagram, SpiderPublicLinkedin requires authenticated LinkedIn accounts. You must add accounts via the admin API before submitting jobs.

Quick Start

1. Add a LinkedIn Account (Admin)

First, add at least one LinkedIn account to the pool:

curl -X POST "https://spideriq.ai/api/v1/admin/linkedin/accounts" \
  -H "X-Admin-Key: $ADMIN_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "account_email": "your-linkedin@example.com",
    "password": "your-password",
    "is_new_account": true
  }'

2. Submit a Job

# Get a company profile
curl -X POST "https://spideriq.ai/api/v1/jobs/spiderPublicLinkedin/submit" \
  -H "Authorization: Bearer $CLIENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "payload": {
      "mode": "get_company",
      "public_id": "microsoft"
    }
  }'

3. Check Job Status

curl "https://spideriq.ai/api/v1/jobs/{job_id}/status" \
  -H "Authorization: Bearer $CLIENT_TOKEN"

4. Get Results

curl "https://spideriq.ai/api/v1/jobs/{job_id}/results" \
  -H "Authorization: Bearer $CLIENT_TOKEN"

Operation Modes

SpiderPublicLinkedin supports four operation modes:

get_company

Fetch detailed company profile including size, industry, specialties, and headquarters.

get_profile

Fetch person profile with experience, education, skills, and current position.

search_companies

Search for companies by keywords and industry filters.

search_people

Search for people by keywords, location, company, or title.

Company Profiles

Get Company by Public ID

{
  "payload": {
    "mode": "get_company",
    "public_id": "microsoft"
  }
}

Get Company by URL

{
  "payload": {
    "mode": "get_company",
    "linkedin_url": "https://linkedin.com/company/microsoft"
  }
}

What Data Can You Extract?

Field	Description	Example
`name`	Company name	Microsoft
`description`	About text	”Every company has a mission…”
`website`	Company website	https://microsoft.com
`industry`	Primary industry	Software Development
`company_type`	Legal type	Public Company
`founded_year`	Year founded	1975
`specialties`	Company specialties	`["Cloud", "AI", "Software"]`
`headquarters`	HQ location	`{"city": "Redmond", "country": "US"}`
`follower_count`	LinkedIn followers	22,000,000
`employee_count`	Approximate employees	221,000
`employee_range`	Size bracket	”10001+“

Company Result Example

{
  "type": "company",
  "public_id": "microsoft",
  "name": "Microsoft",
  "description": "Every company has a mission...",
  "website": "https://microsoft.com",
  "industry": "Software Development",
  "company_type": "Public Company",
  "founded_year": 1975,
  "specialties": ["Business Software", "Developer Tools", "Cloud Computing"],
  "headquarters": {
    "city": "Redmond",
    "country": "United States"
  },
  "follower_count": 22000000,
  "employee_count": 221000,
  "employee_range": "10001+",
  "logo_url": "https://media.licdn.com/...",
  "logo_url_hosted": "https://media.spideriq.ai/client-xxx/linkedin_company_microsoft.jpg",
  "linkedin_url": "https://www.linkedin.com/company/microsoft"
}

People Profiles

Get Profile by Public ID

{
  "payload": {
    "mode": "get_profile",
    "public_id": "satyanadella"
  }
}

Get Profile by URL

{
  "payload": {
    "mode": "get_profile",
    "linkedin_url": "https://linkedin.com/in/satyanadella"
  }
}

What Data Can You Extract?

Field	Description	Example
`first_name`	First name	Satya
`last_name`	Last name	Nadella
`headline`	Professional headline	Chairman and CEO at Microsoft
`summary`	About text	”As Chairman and CEO…”
`location`	Geographic location	Greater Seattle Area
`current_company`	Current employer	Microsoft
`current_position`	Current job title	Chairman and CEO
`connections`	Connection count	500+
`experience`	Work history	Array of positions
`education`	Education history	Array of schools
`skills`	Listed skills	[“Leadership”, “Strategy”]

Profile Result Example

{
  "type": "profile",
  "public_id": "satyanadella",
  "first_name": "Satya",
  "last_name": "Nadella",
  "full_name": "Satya Nadella",
  "headline": "Chairman and CEO at Microsoft",
  "location": "Greater Seattle Area",
  "current_company": "Microsoft",
  "current_position": "Chairman and CEO",
  "connections": 500,
  "experience": [
    {
      "title": "Chairman and CEO",
      "company": "Microsoft",
      "start_date": {"year": 2014, "month": 2},
      "end_date": null
    }
  ],
  "education": [
    {
      "school": "University of Chicago Booth School of Business",
      "degree": "MBA"
    }
  ],
  "skills": ["Leadership", "Strategy", "Technology"],
  "profile_pic_url_hosted": "https://media.spideriq.ai/client-xxx/linkedin_profile_satyanadella.jpg"
}

Searching Companies

Find companies matching your criteria:

{
  "payload": {
    "mode": "search_companies",
    "keywords": "AI startup San Francisco",
    "max_results": 20
  }
}

Search with Industry Filter

{
  "payload": {
    "mode": "search_companies",
    "keywords": "fintech",
    "industry": "Financial Services",
    "max_results": 10
  }
}

Search Results

{
  "type": "company_search",
  "keywords": "AI startup San Francisco",
  "total_results": 20,
  "results": [
    {
      "name": "OpenAI",
      "universal_name": "openai",
      "description": "OpenAI is an AI research and deployment company...",
      "industry": "Artificial Intelligence",
      "staff_count": 1500,
      "headquarters": {"city": "San Francisco"},
      "logo_url": "https://media.licdn.com/..."
    }
  ]
}

Searching People

Find people matching your criteria:

{
  "payload": {
    "mode": "search_people",
    "keywords": "CTO startup",
    "location": "San Francisco",
    "max_results": 20
  }
}

Search with Filters

{
  "payload": {
    "mode": "search_people",
    "keywords": "software engineer",
    "company": "Google",
    "title": "Senior",
    "location": "New York",
    "max_results": 10
  }
}

Available Filters

Filter	Description	Example
`keywords`	Search terms	”CTO AI startup”
`location`	Geographic filter	”San Francisco”
`company`	Company filter	”Microsoft”
`title`	Title filter	”CEO”
`max_results`	Result limit (1-100)	20

Logo & Photo Hosting

LinkedIn CDN URLs can expire. SpiderPublicLinkedin can upload images to SpiderMedia for permanent hosting:

{
  "payload": {
    "mode": "get_company",
    "public_id": "microsoft",
    "store_logo": true
  }
}

Response includes both URLs:

{
  "logo_url": "https://media.licdn.com/...",
  "logo_url_hosted": "https://media.spideriq.ai/client-xxx/linkedin_company_microsoft.jpg"
}

URL Type	Pros	Cons
`logo_url`	Original quality	May expire
`logo_url_hosted`	Permanent, fast CDN	Stored in your quota

Account Management

Adding Accounts

LinkedIn accounts are required for the worker to function. Add them via the admin API:

curl -X POST "https://spideriq.ai/api/v1/admin/linkedin/accounts" \
  -H "X-Admin-Key: $ADMIN_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "account_email": "linkedin@example.com",
    "password": "secure-password",
    "daily_request_limit": 50,
    "is_new_account": true
  }'

Account Warmup

New accounts should be warmed up gradually to avoid detection:

Day	Daily Limit
1	5 requests
2	10 requests
3	15 requests
4	20 requests
5	30 requests
6	40 requests
7+	50 requests

Set is_new_account: true to enable automatic warmup.

Monitoring Accounts

# List all accounts
curl "https://spideriq.ai/api/v1/admin/linkedin/accounts" \
  -H "X-Admin-Key: $ADMIN_KEY"

# Get account statistics
curl "https://spideriq.ai/api/v1/admin/linkedin/accounts/stats" \
  -H "X-Admin-Key: $ADMIN_KEY"

Account Statuses

Status	Description	Action Needed
`active`	Working normally	None
`rate_limited`	Hit daily limit	Wait for cooldown (30-120 min)
`needs_verification`	CAPTCHA required	Login manually to clear
`banned`	Account blocked	Remove or replace
`inactive`	Disabled by admin	Re-enable if needed

Scaling Capacity

Each LinkedIn account provides ~50 requests per day:

Accounts	Daily Capacity
1	30-50 profiles
5	150-250 profiles
10	300-500 profiles
20	600-1,000 profiles

Account Recommendations

Use accounts at least 1 year old
Complete profiles with photos and connections
Spread accounts across different email providers
Add 2FA for security (session cookies work with 2FA)

Rate Limits and Best Practices

LinkedIn Rate Limits

Limit	Value
Safe requests per account per day	30-50
Built-in delay between requests	5-10 seconds
Session break every	20-30 requests

Best Practices

Use Mobile Proxies

LinkedIn blocks datacenter IPs quickly. SpiderProxy mobile proxies are automatically assigned for production jobs, providing carrier-grade IP addresses.

Respect Rate Limits

Don’t submit more than 50 jobs per account per day. The worker includes built-in delays, but excessive volume triggers detection.

Warmup New Accounts

New accounts should start with is_new_account: true to enable the 7-day warmup schedule. Jumping straight to 50 requests/day can trigger blocks.

Use Multiple Accounts

Spread load across multiple accounts. The worker automatically rotates using LRU (least recently used) selection.

Monitor Account Health

Check account stats regularly. Address needs_verification status promptly by logging in manually.

Error Handling

Common Errors

Error	Cause	Solution
No available accounts	All accounts rate limited	Wait or add more accounts
Profile not found	Invalid public_id	Verify the LinkedIn URL is correct
Rate limited	Account hit daily limit	Automatic cooldown, job may retry
Needs verification	CAPTCHA triggered	Login manually to clear
IP blocked	Datacenter IP detected	Use mobile proxy (automatic)

Retry Strategy

For failed jobs, implement exponential backoff:

import time

def get_linkedin_profile(public_id, max_retries=3):
    for attempt in range(max_retries):
        job = submit_job(public_id)
        result = wait_for_results(job["job_id"])

        if result.get("success"):
            return result["data"]

        if "rate limit" in result.get("error", "").lower():
            wait_time = (2 ** attempt) * 60  # 1, 2, 4 minutes
            time.sleep(wait_time)
        elif "no available accounts" in result.get("error", "").lower():
            time.sleep(300)  # Wait 5 minutes for cooldown
        else:
            raise Exception(result.get("error"))

    raise Exception("Max retries exceeded")

Combining with Other Workers

LinkedIn → SpiderSite Pipeline

Extract LinkedIn data, then scrape the company website:

# Step 1: Get LinkedIn company profile
linkedin_job = submit_linkedin_job("get_company", "microsoft")
linkedin_data = wait_for_results(linkedin_job["job_id"])

# Step 2: Scrape the company website
if linkedin_data["data"].get("website"):
    website_job = submit_spidersite_job(linkedin_data["data"]["website"])
    website_data = wait_for_results(website_job["job_id"])

# Step 3: Combine data
enriched = {
    "company": linkedin_data["data"]["name"],
    "linkedin_followers": linkedin_data["data"]["follower_count"],
    "employee_count": linkedin_data["data"]["employee_count"],
    "website_emails": website_data["data"].get("emails", []),
    "website_phones": website_data["data"].get("phones", [])
}

SpiderMaps → LinkedIn Pipeline

Discover businesses on Google Maps, then enrich with LinkedIn:

# Step 1: Get businesses from Google Maps
maps_job = submit_spidermaps_job("AI companies San Francisco")
businesses = wait_for_results(maps_job["job_id"])

# Step 2: Enrich each business with LinkedIn
for business in businesses["data"]["results"]:
    company_name = business["name"]

    # Search for the company on LinkedIn
    linkedin_job = submit_linkedin_job(
        "search_companies",
        keywords=company_name,
        max_results=1
    )
    linkedin_data = wait_for_results(linkedin_job["job_id"])

    if linkedin_data["data"]["results"]:
        business["linkedin"] = linkedin_data["data"]["results"][0]

Example: Building a Lead Database

Complete example building a B2B lead database:

import requests
import time

API_BASE = "https://spideriq.ai/api/v1"
TOKEN = "your_token"

def submit_job(payload):
    response = requests.post(
        f"{API_BASE}/jobs/spiderPublicLinkedin/submit",
        headers={"Authorization": f"Bearer {TOKEN}"},
        json={"payload": payload}
    )
    return response.json()

def wait_for_results(job_id, max_wait=60):
    for _ in range(max_wait // 2):
        status = requests.get(
            f"{API_BASE}/jobs/{job_id}/status",
            headers={"Authorization": f"Bearer {TOKEN}"}
        ).json()

        if status["status"] == "completed":
            return requests.get(
                f"{API_BASE}/jobs/{job_id}/results",
                headers={"Authorization": f"Bearer {TOKEN}"}
            ).json()
        elif status["status"] == "failed":
            return {"success": False, "error": status.get("error")}

        time.sleep(2)

    return {"success": False, "error": "Timeout"}

def build_lead_database(keywords, max_companies=10, people_per_company=5):
    leads = []

    # Step 1: Search for companies
    company_job = submit_job({
        "mode": "search_companies",
        "keywords": keywords,
        "max_results": max_companies
    })

    company_results = wait_for_results(company_job["job_id"])

    if not company_results.get("success"):
        print(f"Company search failed: {company_results.get('error')}")
        return leads

    companies = company_results["data"]["results"]
    print(f"Found {len(companies)} companies")

    # Step 2: For each company, find key people
    for company in companies:
        company_name = company.get("name")
        print(f"\nProcessing: {company_name}")

        # Search for decision makers at this company
        people_job = submit_job({
            "mode": "search_people",
            "keywords": "CEO CTO VP Director",
            "company": company_name,
            "max_results": people_per_company
        })

        people_results = wait_for_results(people_job["job_id"])

        if people_results.get("success"):
            for person in people_results["data"]["results"]:
                leads.append({
                    "company": company_name,
                    "company_industry": company.get("industry"),
                    "company_size": company.get("staff_count"),
                    "person_name": f"{person.get('first_name', '')} {person.get('last_name', '')}".strip(),
                    "person_title": person.get("headline"),
                    "person_linkedin": f"https://linkedin.com/in/{person.get('public_id')}",
                    "company_linkedin": f"https://linkedin.com/company/{company.get('universal_name')}"
                })

        # Respect rate limits
        time.sleep(5)

    return leads

# Build database
leads = build_lead_database("AI startup San Francisco", max_companies=5)
print(f"\nBuilt database with {len(leads)} leads")

for lead in leads[:5]:
    print(f"- {lead['person_name']} at {lead['company']}")

Next Steps

API Reference

Complete API documentation

SpiderSite

Scrape company websites

SpiderMaps

Discover businesses

Mobile Proxies

SpiderProxy configuration

Guides

​Overview

​Quick Start

​1. Add a LinkedIn Account (Admin)

​2. Submit a Job

​3. Check Job Status

​4. Get Results

​Operation Modes

get_company

get_profile

search_companies

search_people

​Company Profiles

​Get Company by Public ID

​Get Company by URL

​What Data Can You Extract?

​Company Result Example

​People Profiles

​Get Profile by Public ID

​Get Profile by URL

​What Data Can You Extract?

​Profile Result Example

​Searching Companies

​Search with Industry Filter

​Search Results

​Searching People

​Search with Filters

​Available Filters

​Logo & Photo Hosting

​Account Management

​Adding Accounts

​Account Warmup

​Monitoring Accounts

​Account Statuses

​Scaling Capacity

​Rate Limits and Best Practices

​LinkedIn Rate Limits

​Best Practices

​Error Handling

​Common Errors

​Retry Strategy

​Combining with Other Workers

​LinkedIn → SpiderSite Pipeline

​SpiderMaps → LinkedIn Pipeline

​Example: Building a Lead Database

​Next Steps

API Reference

SpiderSite

SpiderMaps

Mobile Proxies

Overview

Quick Start

1. Add a LinkedIn Account (Admin)

2. Submit a Job

3. Check Job Status

4. Get Results

Operation Modes

Company Profiles

Get Company by Public ID

Get Company by URL

What Data Can You Extract?

Company Result Example

People Profiles

Get Profile by Public ID

Get Profile by URL

What Data Can You Extract?

Profile Result Example

Searching Companies

Search with Industry Filter

Search Results

Searching People

Search with Filters

Available Filters

Logo & Photo Hosting

Account Management

Adding Accounts

Account Warmup

Monitoring Accounts

Account Statuses

Scaling Capacity

Rate Limits and Best Practices

LinkedIn Rate Limits

Best Practices

Error Handling

Common Errors

Retry Strategy

Combining with Other Workers

LinkedIn → SpiderSite Pipeline

SpiderMaps → LinkedIn Pipeline

Example: Building a Lead Database

Next Steps