LinkedIn Company Search API: Find & Enrich Company Data

11. Search LinkedIn: "software company United States"
22. Click each result
33. Check employee count (is it 50-500?)
44. Check location (is it really US-based?)
55. Check Jobs tab (are they hiring?)
66. Copy data to spreadsheet
77. Repeat 200 times
8
9Time per company: 5-7 minutes
10Total time: 1,000-1,400 minutes (16-23 hours)
11Cost at $50/hour: $800-1,150

1from linkdapi import LinkdAPI
2
3api = LinkdAPI("your_api_key")
4
5# Search companies matching your ICP
6companies = api.search_companies(
7    keyword="software saas",
8    geo_urn="103644278",  # United States
9    company_size=["51-200", "201-500"],
10    has_jobs=True
11)
12
13# Get full data for each
14for company in companies['data']:
15    details = api.get_company_info(company_id=company['id'])
16    jobs = api.get_company_jobs(company['id'])
17    
18    # You now have everything
19    print(f"{details['data']['name']}: {len(jobs['data'])} open roles")
20
21# Time: 30 seconds
22# Cost: $0.20 (200 companies × $0.001 per search)

1Sales team (10 reps):
2- Manual research: 10 hours/week/rep = 100 hours/week
3- API automation: 10 minutes/week total
4- Time saved: 99.5 hours/week
5- Value at $50/hour: $4,975/week = $258,700/year
6
7API cost: $150-300/month = $1,800-3,600/year
8
9ROI: 7,186-14,372%

1Official API:
2- Manage YOUR company page ✓
3- Research OTHER companies ✗
4
5Unofficial API:
6- Manage YOUR company page ✗
7- Research OTHER companies ✓

1{
2  "name": "Stripe",
3  "universalName": "stripe",
4  "description": "Online payment processing for internet businesses",
5  "website": "https://stripe.com",
6  "logo": "https://media.licdn.com/...",
7  "banner": "https://media.licdn.com/...",
8  "tagline": "Financial infrastructure for the internet",
9  "industry": "Financial Services",
10  "specialties": ["Payments", "API", "Developer Tools"]
11}

1{
2  "employeeCount": 7500,
3  "employeeCountRange": "5001-10000",
4  "headquarters": {
5    "city": "San Francisco",
6    "country": "United States",
7    "geographicArea": "California",
8    "postalCode": "94103",
9    "line1": "510 Townsend St"
10  },
11  "locations": [
12    {"city": "San Francisco", "country": "United States"},
13    {"city": "Dublin", "country": "Ireland"},
14    {"city": "Singapore", "country": "Singapore"}
15  ]
16}

1{
2  "followerCount": 850000,
3  "entityUrn": "urn:li:organization:2135371",
4  "url": "https://www.linkedin.com/company/stripe",
5  "founded": 2010
6}

1{
2  "affiliatedCompanies": [
3    {
4      "name": "Stripe Climate",
5      "logo": "https://...",
6      "url": "https://www.linkedin.com/company/stripe-climate"
7    }
8  ]
9}

1{
2  "jobs": [
3    {
4      "title": "Senior Backend Engineer",
5      "location": "San Francisco, CA",
6      "workplaceType": "On-site",
7      "listedAt": "2025-01-02",
8      "url": "https://www.linkedin.com/jobs/view/..."
9    },
10    {
11      "title": "Product Manager - Payments",
12      "location": "Remote",
13      "workplaceType": "Remote",
14      "listedAt": "2025-01-05"
15    }
16  ],
17  "totalJobs": 127
18}

1{
2  "posts": [
3    {
4      "text": "Excited to announce Stripe's new instant payouts feature...",
5      "postedAt": "2025-01-08",
6      "likes": 1240,
7      "comments": 58,
8      "shares": 89
9    }
10  ]
11}

1{
2  "similarCompanies": [
3    {"name": "Square", "id": "1049"},
4    {"name": "Adyen", "id": "244447"},
5    {"name": "PayPal", "id": "1065"}
6  ]
7}

1from linkdapi import LinkdAPI
2
3api = LinkdAPI("your_api_key")
4
5# Search by keyword and filters
6results = api.search_companies(
7    keyword="artificial intelligence",
8    geo_urn="103644278",  # United States
9    company_size=["51-200", "201-500"],
10    has_jobs=True,
11    industry="6"  # Computer Software
12)
13
14print(f"Found {len(results['data'])} companies")
15
16for company in results['data']:
17    print(f"- {company['name']} ({company['employeeCount']} employees)")

1{
2  "success": true,
3  "data": [
4    {
5      "id": "1441",
6      "name": "OpenAI",
7      "universalName": "openai",
8      "logo": "https://...",
9      "employeeCount": 500,
10      "location": "San Francisco, CA"
11    }
12  ]
13}

1# Find company by partial name
2results = api.company_name_lookup(query="micro")
3
4# Returns: Microsoft, Micro Focus, MicroStrategy, etc.
5for company in results['data']:
6    print(f"{company['name']} - {company['id']}")

1# By company ID
2company = api.get_company_info(company_id="1441")
3
4# Or by name
5company = api.get_company_info(name="OpenAI")
6
7data = company['data']
8print(f"Name: {data['name']}")
9print(f"Description: {data['description']}")
10print(f"Website: {data['website']}")
11print(f"Employees: {data['employeeCount']}")
12print(f"Founded: {data['founded']}")
13print(f"Headquarters: {data['headquarters']['city']}")
14print(f"Followers: {data['followerCount']}")

1employees = api.get_company_employees_data(company_id="1441")
2
3print(f"Total employees: {employees['data']['employeeCount']}")
4print(f"Growth: {employees['data']['employeeGrowth']}%")

1jobs = api.get_company_jobs(company_ids="1441", start=0)
2
3print(f"Open positions: {len(jobs['data'])}")
4
5for job in jobs['data']:
6    print(f"- {job['title']}")
7    print(f"  Location: {job['location']}")
8    print(f"  Posted: {job['listedAt']}")
9    print(f"  Type: {job['workplaceType']}")  # Remote/On-site/Hybrid

1# Check multiple competitors
2jobs = api.get_company_jobs(
3    company_ids=["1441", "2382910", "791962"]
4)
5
6# Compare hiring activity
7for company_jobs in jobs['data']:
8    print(f"{company_jobs['company']}: {len(company_jobs['jobs'])} openings")

1similar = api.get_similar_companies(company_id="1441")
2
3for company in similar['data']:
4    print(f"- {company['name']}")
5    print(f"  Employees: {company['employeeCount']}")
6    print(f"  Industry: {company['industry']}")

1affiliated = api.get_company_affiliated_pages(company_id="1035")
2
3for page in affiliated['data']:
4    print(f"- {page['name']}")
5    print(f"  URL: {page['url']}")

1posts = api.get_company_posts(company_id="1441", start=0)
2
3for post in posts['data']:
4    print(f"Posted: {post['postedAt']}")
5    print(f"Text: {post['text'][:100]}...")
6    print(f"Engagement: {post['likes']} likes, {post['comments']} comments")

1# You have: https://linkedin.com/company/openai
2# You need: Company ID for other API calls
3
4company_id = api.get_company_id(universal_name="openai")
5print(f"OpenAI ID: {company_id['data']['id']}")
6
7# Now use this ID for other endpoints
8info = api.get_company_info(company_id=company_id['data']['id'])

1from linkdapi import LinkdAPI
2
3api = LinkdAPI("your_api_key")
4
5# Define your ICP
6target_companies = api.search_companies(
7    keyword="technology software",
8    company_size=["101-250", "251-500"],
9    has_jobs=True,  # Must be hiring (buying signal!)
10    geo_urn="103644278"  # United States
11)
12
13print(f"Found {len(target_companies['data'])} target companies")

1enriched_companies = []
2
3for company in target_companies['data']:
4    # Get full company details
5    details = api.get_company_info(company_id=company['id'])
6    
7    # Get hiring data
8    jobs = api.get_company_jobs(company['id'])
9    
10    # Combine data
11    enriched = {
12        'name': details['data']['name'],
13        'website': details['data']['website'],
14        'employees': details['data']['employeeCount'],
15        'location': details['data']['headquarters']['city'],
16        'open_positions': len(jobs['data']),
17        'hiring_for': [job['title'] for job in jobs['data'][:5]],
18        'founded': details['data']['founded'],
19        'industry': details['data']['industry']
20    }
21    
22    enriched_companies.append(enriched)
23
24# Save to CSV
25import csv
26
27with open('target_companies.csv', 'w', newline='') as f:
28    writer = csv.DictWriter(f, fieldnames=enriched_companies[0].keys())
29    writer.writeheader()
30    writer.writerows(enriched_companies)
31
32print(f"Exported {len(enriched_companies)} companies to target_companies.csv")

1# Score companies by buying signals
2for company in enriched_companies:
3    score = 0
4    
5    # More hiring = higher score
6    if company['open_positions'] > 20:
7        score += 3
8    elif company['open_positions'] > 10:
9        score += 2
10    elif company['open_positions'] > 5:
11        score += 1
12    
13    # Growing companies
14    if company['employees'] > 300:
15        score += 2
16    
17    # Recently founded (high growth phase)
18    if company['founded'] > 2015:
19        score += 1
20    
21    # Hiring HR roles (direct signal!)
22    hr_keywords = ['human resources', 'people ops', 'recruiter', 'talent']
23    if any(keyword in job.lower() for job in company['hiring_for'] for keyword in hr_keywords):
24        score += 5  # MAJOR signal!
25    
26    company['priority_score'] = score
27
28# Sort by score (highest first)
29enriched_companies.sort(key=lambda x: x['priority_score'], reverse=True)
30
31# Your top 20 targets
32top_targets = enriched_companies[:20]
33
34for i, company in enumerate(top_targets, 1):
35    print(f"{i}. {company['name']} (Score: {company['priority_score']})")
36    print(f"   {company['open_positions']} open roles, hiring {company['hiring_for'][0]}")

1def build_complete_profile(company_name):
2    """
3    Create comprehensive company intelligence report
4    """
5    # 1. Find the company
6    lookup = api.company_name_lookup(query=company_name)
7    
8    if not lookup['data']:
9        return None
10    
11    company_id = lookup['data'][0]['id']
12    
13    # 2. Get full details
14    info = api.get_company_info(company_id=company_id)
15    
16    # 3. Get hiring data
17    jobs = api.get_company_jobs(company_id)
18    
19    # 4. Get similar companies
20    similar = api.get_similar_companies(company_id)
21    
22    # 5. Get recent activity
23    posts = api.get_company_posts(company_id, start=0)
24    
25    # 6. Get affiliated companies
26    affiliated = api.get_company_affiliated_pages(company_id)
27    
28    # 7. Compile report
29    profile = {
30        'basic_info': {
31            'name': info['data']['name'],
32            'website': info['data']['website'],
33            'description': info['data']['description'],
34            'founded': info['data']['founded'],
35            'industry': info['data']['industry']
36        },
37        'size_and_location': {
38            'employees': info['data']['employeeCount'],
39            'headquarters': info['data']['headquarters']['city'],
40            'all_locations': [loc['city'] for loc in info['data'].get('locations', [])]
41        },
42        'social_proof': {
43            'followers': info['data']['followerCount'],
44            'linkedin_url': info['data']['url']
45        },
46        'hiring_signals': {
47            'open_positions': len(jobs['data']),
48            'top_roles': [job['title'] for job in jobs['data'][:10]],
49            'hiring_locations': list(set(job['location'] for job in jobs['data'])),
50            'remote_jobs': sum(1 for job in jobs['data'] if 'remote' in job.get('workplaceType', '').lower())
51        },
52        'competitive_landscape': {
53            'similar_companies': [comp['name'] for comp in similar['data'][:10]]
54        },
55        'recent_activity': {
56            'latest_post': posts['data'][0]['text'][:200] if posts['data'] else None,
57            'post_engagement': posts['data'][0]['likes'] if posts['data'] else 0
58        },
59        'company_structure': {
60            'subsidiaries': [aff['name'] for aff in affiliated['data']]
61        }
62    }
63    
64    return profile
65
66# Build profiles for your list
67company_names = ["Stripe", "Square", "Adyen", "PayPal"]
68
69profiles = {}
70for name in company_names:
71    print(f"Building profile for {name}...")
72    profile = build_complete_profile(name)
73    if profile:
74        profiles[name] = profile
75
76# Save profiles
77import json
78
79with open('company_profiles.json', 'w') as f:
80    json.dump(profiles, f, indent=2)
81
82print(f"Built {len(profiles)} complete company profiles")

1def generate_company_report(profile):
2    """
3    Create human-readable report from profile data
4    """
5    report = f"""
6COMPANY INTELLIGENCE REPORT
7============================
8
9Company: {profile['basic_info']['name']}
10Website: {profile['basic_info']['website']}
11Industry: {profile['basic_info']['industry']}
12
13OVERVIEW
14--------
15{profile['basic_info']['description']}
16
17Founded: {profile['basic_info']['founded']}
18Employees: {profile['size_and_location']['employees']}
19Headquarters: {profile['size_and_location']['headquarters']}
20LinkedIn Followers: {profile['social_proof']['followers']:,}
21
22HIRING ACTIVITY (BUYING SIGNAL)
23-------------------------------
24Open Positions: {profile['hiring_signals']['open_positions']}
25Remote Roles: {profile['hiring_signals']['remote_jobs']}
26
27Top Hiring Areas:
28"""
29    
30    for role in profile['hiring_signals']['top_roles'][:5]:
31        report += f"  • {role}\n"
32    
33    report += f"""
34COMPETITIVE LANDSCAPE
35--------------------
36Similar Companies:
37"""
38    
39    for competitor in profile['competitive_landscape']['similar_companies'][:5]:
40        report += f"  • {competitor}\n"
41    
42    report += f"""
43RECENT ACTIVITY
44---------------
45Latest Post: {profile['recent_activity']['latest_post']}
46Engagement: {profile['recent_activity']['post_engagement']} likes
47
48COMPANY STRUCTURE
49-----------------
50"""
51    
52    if profile['company_structure']['subsidiaries']:
53        report += "Subsidiaries:\n"
54        for sub in profile['company_structure']['subsidiaries'][:5]:
55            report += f"  • {sub}\n"
56    else:
57        report += "No affiliated companies listed\n"
58    
59    return report
60
61# Generate reports for all profiles
62for company_name, profile in profiles.items():
63    report = generate_company_report(profile)
64    
65    # Save to file
66    filename = f"{company_name.replace(' ', '_')}_report.txt"
67    with open(filename, 'w') as f:
68        f.write(report)
69    
70    print(f"Generated {filename}")

1import schedule
2import time
3from datetime import datetime
4
5competitors = [
6    {"name": "Competitor A", "id": "12345"},
7    {"name": "Competitor B", "id": "67890"},
8    {"name": "Competitor C", "id": "11223"}
9]
10
11def monitor_competitor(company_id, company_name):
12    """
13    Check for changes in competitor activity
14    """
15    # Get current data
16    info = api.get_company_info(company_id=company_id)
17    jobs = api.get_company_jobs(company_id)
18    posts = api.get_company_posts(company_id, start=0)
19    
20    # Load previous data
21    import json
22    import os
23    
24    filename = f"competitor_{company_id}_history.json"
25    
26    if os.path.exists(filename):
27        with open(filename, 'r') as f:
28            previous = json.load(f)
29    else:
30        previous = {}
31    
32    # Detect changes
33    changes = []
34    
35    # Employee count changed
36    current_employees = info['data']['employeeCount']
37    if previous.get('employees') and current_employees != previous['employees']:
38        change = current_employees - previous['employees']
39        changes.append(f"Employee count: {previous['employees']} → {current_employees} ({change:+d})")
40    
41    # New job postings
42    current_jobs = len(jobs['data'])
43    if previous.get('jobs') and current_jobs > previous['jobs']:
44        new_jobs = current_jobs - previous['jobs']
45        changes.append(f"New job postings: {new_jobs} added")
46        
47        # Show what roles they're hiring for
48        recent_jobs = jobs['data'][:new_jobs]
49        for job in recent_jobs:
50            changes.append(f"  • {job['title']} ({job['location']})")
51    
52    # New posts
53    if posts['data']:
54        latest_post_time = posts['data'][0]['postedAt']
55        if previous.get('latest_post') != latest_post_time:
56            changes.append(f"New LinkedIn post: {posts['data'][0]['text'][:100]}...")
57    
58    # Follower count changed
59    current_followers = info['data']['followerCount']
60    if previous.get('followers') and current_followers != previous['followers']:
61        change = current_followers - previous['followers']
62        changes.append(f"Followers: {previous['followers']:,} → {current_followers:,} ({change:+,})")
63    
64    # Save current state
65    current_state = {
66        'employees': current_employees,
67        'jobs': current_jobs,
68        'followers': current_followers,
69        'latest_post': posts['data'][0]['postedAt'] if posts['data'] else None,
70        'last_checked': datetime.now().isoformat()
71    }
72    
73    with open(filename, 'w') as f:
74        json.dump(current_state, f, indent=2)
75    
76    # Alert if changes detected
77    if changes:
78        alert = f"""
79🚨 COMPETITOR ALERT: {company_name}
80{'='*50}
81Detected changes:
82"""
83        for change in changes:
84            alert += f"  • {change}\n"
85        
86        alert += f"\nChecked: {datetime.now().strftime('%Y-%m-%d %H:%M')}"
87        
88        print(alert)
89        
90        # Send to Slack/email (implement your notification system)
91        send_notification(alert)
92    else:
93        print(f"✅ {company_name}: No changes detected")
94
95def daily_competitor_scan():
96    """
97    Run daily competitor monitoring
98    """
99    print(f"\n=== Daily Competitor Scan: {datetime.now().strftime('%Y-%m-%d %H:%M')} ===\n")
100    
101    for competitor in competitors:
102        monitor_competitor(competitor['id'], competitor['name'])
103        time.sleep(2)  # Be nice to the API
104
105# Schedule daily checks
106schedule.every().day.at("09:00").do(daily_competitor_scan)
107
108# Or run immediately for testing
109daily_competitor_scan()
110
111# Keep running
112while True:
113    schedule.run_pending()
114    time.sleep(3600)  # Check every hour

1🚨 COMPETITOR ALERT: Competitor A
2==================================================
3Detected changes:
4  • Employee count: 450 → 478 (+28)
5  • New job postings: 12 added
6    • Senior Backend Engineer (Remote)
7    • Product Manager - Enterprise (SF)
8    • Sales Director (NYC)
9  • New LinkedIn post: Excited to announce our Series B funding...
10  • Followers: 45,230 → 47,891 (+2,661)
11
12Checked: 2025-01-08 09:00

1def find_hiring_companies(min_jobs=10):
2    """
3    Find companies with significant hiring activity
4    """
5    # Search for companies with jobs
6    companies = api.search_companies(
7        company_size=["51-200", "201-500", "501-1000"],
8        has_jobs=True
9    )
10    
11    hiring_intelligence = []
12    
13    for company in companies['data']:
14        # Get detailed job data
15        jobs = api.get_company_jobs(company['id'])
16        
17        if len(jobs['data']) >= min_jobs:
18            # Analyze the jobs
19            job_analysis = analyze_hiring_patterns(jobs['data'])
20            
21            hiring_intelligence.append({
22                'company': company['name'],
23                'company_id': company['id'],
24                'total_jobs': len(jobs['data']),
25                'hiring_analysis': job_analysis
26            })
27    
28    # Sort by hiring activity
29    hiring_intelligence.sort(key=lambda x: x['total_jobs'], reverse=True)
30    
31    return hiring_intelligence
32
33def analyze_hiring_patterns(jobs):
34    """
35    Extract insights from job postings
36    """
37    analysis = {
38        'departments': {},
39        'locations': {},
40        'remote_ratio': 0,
41        'seniority': {'senior': 0, 'mid': 0, 'entry': 0}
42    }
43    
44    for job in jobs:
45        # Department analysis
46        title = job['title'].lower()
47        if 'engineer' in title or 'developer' in title:
48            analysis['departments']['Engineering'] = analysis['departments'].get('Engineering', 0) + 1
49        elif 'sales' in title or 'account' in title:
50            analysis['departments']['Sales'] = analysis['departments'].get('Sales', 0) + 1
51        elif 'product' in title or 'manager' in title:
52            analysis['departments']['Product'] = analysis['departments'].get('Product', 0) + 1
53        elif 'marketing' in title:
54            analysis['departments']['Marketing'] = analysis['departments'].get('Marketing', 0) + 1
55        
56        # Location analysis
57        location = job.get('location', 'Unknown')
58        analysis['locations'][location] = analysis['locations'].get(location, 0) + 1
59        
60        # Remote work
61        if 'remote' in job.get('workplaceType', '').lower():
62            analysis['remote_ratio'] += 1
63        
64        # Seniority
65        if 'senior' in title or 'lead' in title or 'principal' in title:
66            analysis['seniority']['senior'] += 1
67        elif 'junior' in title or 'entry' in title or 'associate' in title:
68            analysis['seniority']['entry'] += 1
69        else:
70            analysis['seniority']['mid'] += 1
71    
72    # Calculate remote percentage
73    analysis['remote_ratio'] = (analysis['remote_ratio'] / len(jobs)) * 100
74    
75    return analysis
76
77# Find companies with big hiring pushes
78hiring_companies = find_hiring_companies(min_jobs=15)
79
80# Generate report
81print("\n🔥 TOP HIRING COMPANIES\n")
82
83for i, company in enumerate(hiring_companies[:20], 1):
84    analysis = company['hiring_analysis']
85    
86    print(f"{i}. {company['company']}")
87    print(f"   Total Open Roles: {company['total_jobs']}")
88    print(f"   Remote: {analysis['remote_ratio']:.0f}%")
89    print(f"   Top Department: {max(analysis['departments'], key=analysis['departments'].get)}")
90    print(f"   Hiring Locations: {', '.join(list(analysis['locations'].keys())[:3])}")
91    print()
92
93# Export for sales team
94with open('hiring_hotlist.csv', 'w', newline='') as f:
95    writer = csv.writer(f)
96    writer.writerow(['Company', 'Open Roles', 'Remote %', 'Top Department', 'Locations'])
97    
98    for company in hiring_companies:
99        analysis = company['hiring_analysis']
100        writer.writerow([
101            company['company'],
102            company['total_jobs'],
103            f"{analysis['remote_ratio']:.0f}%",
104            max(analysis['departments'], key=analysis['departments'].get) if analysis['departments'] else 'N/A',
105            ', '.join(list(analysis['locations'].keys())[:3])
106        ])
107
108print("✅ Exported hiring_hotlist.csv for sales team")