A linkedin company profile scraper is the fastest way to collect public business information without spending hours clicking through pages.

Instead of opening every linkedin company profile manually, copying details, and building spreadsheets by hand, you simply paste the URLs and let the automation do the heavy lifting. Within minutes, you receive structured, clean, analysis-ready data from each linkedin company page.

Think of it as converting any public linkedin company listing into an instant database.

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Why use a LinkedIn Company Profile Scraper?

If you work in sales, recruitment, investing, partnerships, or market intelligence, information from a linkedin company page is incredibly valuable.

The problem is time.

Manual collection is slow, repetitive, and error-prone.

A linkedin company scraper helps you:

• build prospect lists
• analyze markets
• track competitors
• enrich CRM records
• discover hiring trends
• monitor company growth

You get standardized output from every linkedin company profile, ready for Excel, Google Sheets, BI tools, or APIs.

No more copy-paste. No more messy formatting.

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How the LinkedIn Company scraper works

It’s simple.

You submit a list of linkedin company URLs. The actor visits each linkedin company page and extracts the publicly available data.

Behind the scenes:

• URLs are read from the urls input
• each linkedin company profile is processed
• structured results are stored in the dataset

Start → run → download.

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What are the constraints?

The scraper only collects data visible on a public linkedin company profile.

✔ No login ✔ No private information ✔ No hidden data

For example, LinkedIn usually shows only a preview of employees. The scraper can access exactly what is displayed publicly for that linkedin company.

If a visitor can see it, the tool can extract it.

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Supported input URL format

Currently supported → slug-based linkedin company URLs

✅ https://www.linkedin.com/company/apifytech ❌ https://linkedin.com/company/10038644

If you only have IDs, convert them first using the Linkedin Company Profile Id To Slug Finder.

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📦 What company insights can you pull from LinkedIn?

Field	What it means	How people use it
company_name	Official company name on LinkedIn	CRM enrichment, matching, deduplication
universal_name_id	Unique slug from the LinkedIn URL	Stable ID, joins, profile building
company_id	Numeric LinkedIn organization ID	Internal mapping, automation
linkedin_internal_id	Extra LinkedIn reference (may be empty)	Advanced integrations
logo_image_url	Company logo	Dashboards, lead previews, UI
background_cover_image_url	Banner image	Brand monitoring, marketing analysis
tagline	Short mission / positioning line	Personalization, AI classification
about	Full description of the company	LLM enrichment, summarization, intent detection
industry	Primary industry category	Market segmentation, filtering
industries	Additional industries	Vertical routing
type	Ownership type (private/public/etc.)	Investor & enterprise research
founded	Year the company started	Stage & maturity modeling
specialties	Declared expertise keywords	Capability search, competitor analysis
headquarters	Main office location	Territory assignment
location	General visible region	Geo enrichment
locations[]	Detailed office information	Expansion & footprint research
website	Official domain	Tech lookup, contact discovery
company_size	Employee range	Lead qualification
company_size_on_linkedin	Count of profiles linked	Growth tracking
follower_count	Displayed follower number	Social proof
follower_count_num	Numeric followers	Analytics & trends
employees[]	List of visible employees	Decision maker discovery, recruiting
updates[]	Recent posts & activity	Intent data, trigger outreach
similar_companies[]	LinkedIn recommended peers	Competitor & TAM expansion
affiliated_companies[]	Related organizations	Corporate structure mapping
inputURL	Source page	Data lineage
status	Extraction result	Pipeline reliability

Sample Output

[{
  "company_name": "Apify",
  "universal_name_id": "apify",
  "logo_image_url": "https://media.licdn.com/dms/image/v2/D4D0BAQF7OnC-r1njIA/company-logo_200_200/B4DZwd3LFrHEAI-/0/1770027528966/apify_logo?e=2147483647&v=beta&t=3P-kpqqDp-xRunnoe7qQwvZ3DazE1KnOdCiVlTREoJQ",
  "background_cover_image_url": "https://media.licdn.com/dms/image/v2/D4D3DAQHvzCPj3SIuwg/image-scale_191_1128/B4DZwd25f3J0Ac-/0/1770027457587/apify_cover?e=2147483647&v=beta&t=LINhTZoj6pz0QZOMBRAR4zsAmibBHWaR_XR-qn7SmLU",
  "linkedin_internal_id": "",
  "industry": "Technology, Information and Internet",
  "location": "",
  "follower_count": "21,408",
  "tagline": "",
  "company_size_on_linkedin": 191,
  "about": "Apify is the world's largest marketplace of tools for web scraping, data extraction, and automation tools. \n\nUsers extract data from social media, e-commerce sites, search engines, and maps for competitive analysis, lead generation, and product research. Apify integrates with n8n, Make, Zapier, and LangChain, and its MCP support lets AI agents dynamically discover and use tools from the marketplace without custom code. Apify handles infrastructure, proxies, and scaling, so users focus on the data, not the pipeline.\n\nTrusted by enterprises like Siemens, Intercom, Groupon, and Microsoft, as well as 10,000+ customers around the world 🌎",
  "website": "https://apify.com/",
  "industries": "",
  "company_size": "51-200 employees",
  "headquarters": "Prague",
  "type": "Privately Held",
  "founded": "2016",
  "specialties": "Web scraping, Browser automation, AI agents, API integration, Data pipelines, No-code tools, Actor marketplace, and Developer platform",
  "company_id": 10608457,
  "locations": [
    {
      "is_hq": true,
      "office_address_line_1": "Vodickova 704/36",
      "office_address_line_2": "Prague, 11100, CZ",
      "office_location_link": "https://www.bing.com/maps?where=Vodickova+704%2F36+Prague+11100+CZ&trk=org-locations_url"
    },
    {
      "is_hq": false,
      "office_address_line_1": "San Francisco, CA, US",
      "office_address_line_2": "",
      "office_location_link": "https://www.bing.com/maps?where=San+Francisco+CA+US&trk=org-locations_url"
    }
  ],
  "employees": [
    {
      "employee_photo": "https://media.licdn.com/dms/image/v2/C4E03AQHmtgVOIg7GeQ/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1638120354738?e=2147483647&v=beta&t=-JjUdsDqMPboRwz07ODwvK0971jMiFrZlt38Tl8ipz0",
      "employee_name": "Honza Javorek",
      "employee_position": "Tvořím junior.guru, které pomáhá juniorním programátorům",
      "employee_profile_url": "https://cz.linkedin.com/in/honzajavorek?trk=org-employees"
    },
    {
      "employee_photo": "https://media.licdn.com/dms/image/v2/D4E03AQGVYzvGHp9zhw/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1688982177437?e=2147483647&v=beta&t=RZOrroNiBfyCRXLYrJGiJfMIhpA9OO-MjINurO_LwIU",
      "employee_name": "Jan Čurn",
      "employee_position": "",
      "employee_profile_url": "https://www.linkedin.com/in/jancurn?trk=org-employees"
    },
    {
      "employee_photo": "https://media.licdn.com/dms/image/v2/C4E03AQFbR0qzQd-6eA/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1516309144835?e=2147483647&v=beta&t=7Y2jjcvg3MWTreeC2ojO7B5pmmvToO2r3S5zCGylqnA",
      "employee_name": "Jakub Balada",
      "employee_position": "Co-founder at Apify",
      "employee_profile_url": "https://cz.linkedin.com/in/jbalada?trk=org-employees"
    },
    {
      "employee_photo": "https://media.licdn.com/dms/image/v2/C4E03AQFZbIYP7gY9Sg/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1630875589640?e=2147483647&v=beta&t=Pa6gHOuM4nqo9mL8tujZBw8Sms0--R1q4VZw15OVW5c",
      "employee_name": "Simona Baxa",
      "employee_position": "Head of People at Apify",
      "employee_profile_url": "https://cz.linkedin.com/in/simonabaxa?trk=org-employees"
    }
  ],
  "updates": [
    {
      "text": "Monitoring video performance across YouTube, TikTok, Facebook, and Instagram used to eat up hours of manual work.\n\nMindvalley built a system that does it automatically, and predicts which ads will perform before spending a dollar.\nThe setup: Apify scrapers pull video data (hooks, thumbnails, engagement, posting times), then AI analyzes patterns.\n\n💡 Key insight: the first 5 seconds make or break everything.\n\nCheck out the full case study in the comments 👇",
      "articlePostedDate": "9h",
      "totalLikes": "5"
    },
    {
      "text": "🚨 Web scraping in 2026 is getting more expensive - and AI is still surprisingly underused.\n\nIn December 2025, Apify and The Web Scraping Club surveyed hundreds of web scraping professionals to understand what’s changed, what’s working, and what’s coming next.\n\nA few highlights from the State of Web Scraping Report 2026 👇\n\n💸 Costs are rising fast\n• 66% increased proxy usage\n• 58% are spending more on proxies\n• 62%+ report higher infrastructure costs due to tougher anti-bot defenses\n\n🤖 AI: skepticism first, adoption next\n• 54% don’t use AI in their scraping workflows (yet)\n• But 66% plan to try AI-assisted scraping tools\n• Among current AI users, 100% plan to increase usage\n\nThe signal is clear. Once developers adopt AI for scraping, they don’t go back.\n\n\nDownload the free report in the comments 👇",
      "articlePostedDate": "18h",
      "totalLikes": "11"
    },
    {
      "text": "THE FUTURE BELONGS TO BUILDERS\n\nThe timing is actually insane. Apify announced the winners yesterday (Feb 4th), and today is my birthday. I will gladly take the regional prize for the Apify $1M Challenge as a birthday gift.\n\nHuge shout-out to the entire Apify crew for building a world-class competition.\n\nThey have one of the strongest developer ecosystems in the game right now. \n\nKeep a close eye on them in 2026.",
      "articlePostedDate": "1d",
      "totalLikes": "30"
    },
    {
      "text": "🏆 Beyond the grand prizes of the Apify $1M Challenge, we’re also recognizing standout developers from across the Apify community!\n\nThese winners stood out through long-term impact, rapid momentum, and regional representation, helping the challenge grow into a global effort.\n\nTap through to meet them 💚 →\n\nBuilt once, growing over time. Their work shows how consistent building on Apify can turn into lasting impact and recurring rewards.\n\n#Apify1MChallenge",
      "articlePostedDate": "1d",
      "totalLikes": "12"
    },
    {
      "text": "Scraping 10,000 Amazon products with an AI agent used to mean 500 batches of 20 and constant timeout anxiety.\n\nApify MCP server now supports async execution. Start an Actor, get a run ID, let the agent handle other work, and fetch results when ready.\n\nDocs in comments 👇",
      "articlePostedDate": "1d",
      "totalLikes": "9"
    },
    {
      "text": "Reviews are structured customer research hiding in plain sight - what people notice, what they hate, the exact language they use.\n\nJoe Nilsen built a Google Sheet that pulls Amazon reviews by ASIN, star rating, and keyword filters. No code, runs straight from the sheet menu, powered by Apify.\n\nGrab it below 👇Amazon sellers have a goldmine in their accounts and never touch it.\n\nReviews are not \"feedback.\"\n\nThey are a searchable, structured map of:\n\n- what customers actually notice\n- what they hate\n- what language they use\n- what competitors are getting away with\n\nWe built a Google Sheet that scrapes reviews without having the user touch any code.\n\n 1. Add ASINs.\n 2. Select star ratings.\n 3. Add keyword filters.\n 4. Run it from a menu inside the sheet.\n\nPowered by Apify, so it scales.\n\nI'm giving the sheet away to anyone who wants it.\n\nReviews are the cheapest customer research you will ever get.\n\nYou don’t need better ideas.\n\nYou need better extraction.\n\nDive in.",
      "articlePostedDate": "1d\n  \n                  \n                    \n\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n\n      2d",
      "totalLikes": "4"
    },
    {
      "text": "The journey led somewhere big 🚀\n\nAfter months of building at the edge of the AI frontier, these creators went the furthest.\n\nIntroducing the Grand winners of the Apify $1M Challenge → the builders whose work stood out, scaled up, and helped push automation forward.\n\n💙 Click through to meet the winners and their top submitted Actors.\n\nP.S. Their Actors are 100% worth trying, find them in the comments.\n\n#Apify1MChallenge",
      "articlePostedDate": "2d",
      "totalLikes": "18"
    },
    {
      "text": "Starting in 1 hour! ⏰  \n\nWe’re announcing the winners of the Apify $1M Challenge live 🏆 Join us to see the top Actors and a special update for all participating devs.\n\nRSVP → https://luma.com/6c1493t0The Apify $1M Challenge is coming to an end - and on February 4, we're announcing the winners live 🏆\n\nTune in to see which developers and Actors came out on top, and stick around for a special announcement about updated rewards for all participating developers.\n\nWhether you competed, missed out, or just want to discover some of the best new Actors on the platform, this one's worth your time.\n\n🗓️ February 4\n⏰ 5 pm CET / 8 am PST / 9:30 pm IST\n\nRSVP below in the comments ☑️",
      "articlePostedDate": "2d\n  \n                  \n                    \n\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n\n      1w",
      "totalLikes": "1"
    },
    {
      "text": "As we conclude the Apify $1M Challenge, I would like to invite everyone to an online celebration with the community and us. ❤️ \n\nWe will announce the Grand Prize winners, our regional winners, and a special surprise for everyone who participated in this challenge.\n\nRegister now( https://luma.com/6c1493t0 ), and see you on Feb 4th :) \n\nWith love, Apify team. 🎉",
      "articlePostedDate": "1w",
      "totalLikes": "29"
    },
    {
      "text": "When people hear the story of Apify, they often focus on its origins: two developers in Prague, frustrated by the limitations of the modern web, building a tool to solve their own problem. \n\nIt’s a great startup story, and it’s only the beginning of what matters.\n\nApify is no longer just a product born out of developer frustration. It has become the world’s largest marketplace for AI-ready data collection tools, and a critical infrastructure layer for companies that want to move faster, work smarter, and build better products.\n\n\n𝗥𝗘𝗔𝗗 𝗧𝗛𝗘 𝗗𝗘𝗧𝗔𝗜𝗟𝗦 👉 https://lnkd.in/dDNtAC3z\n\n\nJakub Balada Jan Čurn Petra Chocholova Magda Rýdová Ondra Urban Dusan Antos Nicolas Bowles Daniel Čížek Marek Trunkát Richard \"Cameron\" Hawes Uri Gankin 📈 Theo Vasilis Tomáš Jindra Christos Seraphim Stamopoulos Kirill Turin Dávid Lukáč Dmitry Chepurnikh Natasha Lekh Ales Wilk Jan Zenisek Myrsini Koukiasa Zuzka Pelechova Simona Liptáková Michelle Macias Mendez Václav Králíček \n\n\n#Startups #Founders #AI #Marketplace #StartupStory #Data #Tools",
      "articlePostedDate": "2d",
      "totalLikes": "68"
    }
  ],
  "similar_companies": [
    {
      "link": "https://cz.linkedin.com/company/itsmakehq?trk=similar-pages",
      "name": "Make",
      "summary": "Software Development",
      "location": "Prague, Praha 8"
    },
    {
      "link": "https://de.linkedin.com/company/n8n?trk=similar-pages",
      "name": "n8n",
      "summary": "Software Development",
      "location": "Berlin, BE"
    },
    {
      "link": "https://www.linkedin.com/company/grow-with-clay?trk=similar-pages",
      "name": "Clay",
      "summary": "Software Development",
      "location": "New York, NY"
    },
    {
      "link": "https://www.linkedin.com/company/instantlyapp?trk=similar-pages",
      "name": "Instantly.ai",
      "summary": "Software Development",
      "location": "Sheridan, WY"
    },
    {
      "link": "https://fr.linkedin.com/company/phantombuster-official?trk=similar-pages",
      "name": "PhantomBuster",
      "summary": "Technology, Information and Internet",
      "location": "Paris, Île-de-France"
    },
    {
      "link": "https://es.linkedin.com/company/zenrows?trk=similar-pages",
      "name": "ZenRows",
      "summary": "Technology, Information and Internet",
      "location": ""
    },
    {
      "link": "https://www.linkedin.com/company/serperapi?trk=similar-pages",
      "name": "Serper",
      "summary": "Information Services",
      "location": ""
    },
    {
      "link": "https://uk.linkedin.com/company/trigify?trk=similar-pages",
      "name": "Trigify.io",
      "summary": "Software Development",
      "location": ""
    },
    {
      "link": "https://mk.linkedin.com/company/heyreachio?trk=similar-pages",
      "name": "HeyReach.io",
      "summary": "Software Development",
      "location": "Skopje, North Macedonia"
    },
    {
      "link": "https://au.linkedin.com/company/smartlead-ai?trk=similar-pages",
      "name": "Smartlead",
      "summary": "Software Development",
      "location": "Sydney, Nsw"
    }
  ],
  "affiliated_companies": [],
  "inputURL": "https://www.linkedin.com/company/apify",
  "follower_count_num": 21408,
  "status": "success"
}]

---

When should you use a LinkedIn Company scraper?

Whenever your workflow requires visiting multiple linkedin company profiles.

Common scenarios:

✔ outbound sales ✔ recruitment research ✔ VC scouting ✔ agency prospecting ✔ competitor monitoring ✔ market mapping

If humans are clicking profile after profile, automation will multiply productivity.

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Why teams switch to this scraper

Most tools promise data.

This one focuses on reliable linkedin company extraction at scale while keeping costs predictable. You can run bulk jobs, plug the output into your systems, and trust the format every time.

For growing teams, that reliability matters more than flashy features.

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🆚 LinkedIn Company Scraper – Feature Comparison

Feature / Capability	Our LinkedIn Company Scraper	Typical Other Scrapers
Data Accuracy	⭐ High – optimized & continuously maintained	Unstable / inconsistent
Success Rate	🚀 Very High – reliable extraction every run	Varies / often fails
Cost Efficiency	💰 Lowest Cost – optimized resource usage	Moderate to High
Speed / Performance	⚡ High Speed – fast extraction & processing	Slow to Moderate
Public Data Coverage	📊 Large – deep LinkedIn company extraction	Partial / limited
Similar Companies List	🔍 Yes	Rare / minimal
Affiliate / Related Companies List	🤝 Yes	Rare or unavailable

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💬 FAQ

1) How do I find LinkedIn company URLs as input?

You can automate that part too using LinkedIn Company URL - Mass Finder

Stop wasting time manually searching for LinkedIn company pages. Provide your company list and get their matching LinkedIn URLs in minutes. It’s the fastest way to prepare bulk input for your scraping workflow.

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2) How do I extract a LinkedIn company profile?

The safest and most common approach is to collect information that is publicly visible on linkedin company pages. You can use specialized automation tools or Apify actors built for this purpose.

Typically, you provide the company URL (or first discover it using a finder actor), configure the fields you want (about, industry, size, website, followers, etc.), and export the results to CSV or JSON for further use in sales, research, or enrichment workflows.

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3) Can I extract data from Sales Navigator or Recruiter platforms?

Proceed carefully. Accessing those environments may involve contractual or legal risks. Many teams stay focused on publicly visible linkedin company data.

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4) In which formats can I export the results?

Most workflows support CSV for spreadsheets and JSON for integrations, analytics, or engineering pipelines.

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5) Can this be combined with other Apify actors?

Yes. It can run independently or as part of a larger system. Matching schemas between actors makes automation seamless.

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6) Can scraped data help me design a better company page?

Definitely. Reviewing multiple linkedin company profiles can inspire positioning, messaging, and keyword strategies. Just make sure the final content is original.

Here’s an additional FAQ written in the same tone and structure:

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7) Why we don’t support setting cookies?

Adding cookies allows LinkedIn to associate activity with your personal account. If scraping is done in the wrong way, that account can be restricted or permanently blocked. We promote collecting only publicly available data, ensuring your personal identity is never involved in the process.

---

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