Skip to main content

LinkedIn Company & People Data

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
warning

LinkedIn Accounts Required

Unlike 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?​

FieldDescriptionExample
nameCompany nameMicrosoft
descriptionAbout text"Every company has a mission..."
websiteCompany websitehttps://microsoft.com
industryPrimary industrySoftware Development
company_typeLegal typePublic Company
founded_yearYear founded1975
specialtiesCompany specialties["Cloud", "AI", "Software"]
headquartersHQ location{"city": "Redmond", "country": "US"}
follower_countLinkedIn followers22,000,000
employee_countApproximate employees221,000
employee_rangeSize 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?​

FieldDescriptionExample
first_nameFirst nameSatya
last_nameLast nameNadella
headlineProfessional headlineChairman and CEO at Microsoft
summaryAbout text"As Chairman and CEO..."
locationGeographic locationGreater Seattle Area
current_companyCurrent employerMicrosoft
current_positionCurrent job titleChairman and CEO
connectionsConnection count500+
experienceWork historyArray of positions
educationEducation historyArray of schools
skillsListed 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​

FilterDescriptionExample
keywordsSearch terms"CTO AI startup"
locationGeographic filter"San Francisco"
companyCompany filter"Microsoft"
titleTitle filter"CEO"
max_resultsResult 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 TypeProsCons
logo_urlOriginal qualityMay expire
logo_url_hostedPermanent, fast CDNStored 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:

DayDaily Limit
15 requests
210 requests
315 requests
420 requests
530 requests
640 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​

StatusDescriptionAction Needed
activeWorking normallyNone
rate_limitedHit daily limitWait for cooldown (30-120 min)
needs_verificationCAPTCHA requiredLogin manually to clear
bannedAccount blockedRemove or replace
inactiveDisabled by adminRe-enable if needed

Scaling Capacity​

Each LinkedIn account provides ~50 requests per day:

AccountsDaily Capacity
130-50 profiles
5150-250 profiles
10300-500 profiles
20600-1,000 profiles
tip

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​

LimitValue
Safe requests per account per day30-50
Built-in delay between requests5-10 seconds
Session break every20-30 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​

ErrorCauseSolution
No available accountsAll accounts rate limitedWait or add more accounts
Profile not foundInvalid public_idVerify the LinkedIn URL is correct
Rate limitedAccount hit daily limitAutomatic cooldown, job may retry
Needs verificationCAPTCHA triggeredLogin manually to clear
IP blockedDatacenter IP detectedUse 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