This LinkedIn company extractor program enables you to gather comprehensive information on multiple companies in bulk. It provides details such as company name, address, phone numbers, website, employee count, and more.
Interested in using this scraper? Get it here: LinkedIn Company Scraper. Scrape LinkedIn companies and extract complete information in bulk.
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Data Export and Integration: Once the scraping process is complete, you can easily export the extracted data in various formats such as JSON, CSV, or Excel. You can select the fields you want, allowing for seamless integration with other tools and platforms for further analysis and utilization.
Automatic Retry and Error Handling: In case of temporary issues like network failures or timeouts, the actor has built-in automatic retry functionality. It intelligently handles errors to ensure a smooth and uninterrupted scraping experience.
Ability to Resume Last Failed Runs: In case of unexpected errors, you can simply go to the actor's last run page and click on the 'Resurrect' button to resume the last scraping progress.
You can use Make to integrate the LinkedIn company scraper with any other SaaS platform by designing your own automation flows.
Here is a sample output of this actor:
1{ 2 "affiliated_pages_data": [], 3 "background_cover_image_url": null, 4 "company_id": "162479", 5 "company_name": "Apple", 6 "company_type": "Public Company", 7 "description": "We’re a diverse collective of thinkers and doers, continually reimagining what’s possible to help us all do what we love in new ways. And the same innovation that goes into our products also applies to our practices — strengthening our commitment to leave the world better than we found it. This is where your work can make a difference in people’s lives. Including your own.\n\nApple is an equal opportunity employer that is committed to inclusion and diversity. Visit apple.com/careers to learn more.", 8 "employees": "171,148", 9 "employees_data": [ 10 { 11 "link": "https://www.linkedin.com/in/pdking?trk=org-employees", 12 "name": "Paul King", 13 "title": "Sr. Data Science Manager at Apple; fmr Computational Neuroscientist, Software Technologist" 14 }, 15 { 16 "link": "https://www.linkedin.com/in/instanttim?trk=org-employees", 17 "name": "Timothy Martin", 18 "title": null 19 }, 20 { 21 "link": "https://www.linkedin.com/in/evitiello?trk=org-employees", 22 "name": "Eric Vitiello", 23 "title": "Software Engineering Manager at Apple" 24 }, 25 { 26 "link": "https://www.linkedin.com/in/kevinlynch2?trk=org-employees", 27 "name": "Kevin Lynch", 28 "title": "VP Technology, Apple" 29 } 30 ], 31 "followers": "17,563,839", 32 "founded_year": "1976", 33 "headcount": "10,001+ employees", 34 "hq_address": { 35 "is_hq": true, 36 "line_1": "1 Apple Park Way", 37 "line_2": "Cupertino, California 95014, US" 38 }, 39 "industry": "Computers and Electronics Manufacturing", 40 "location": "Cupertino, California", 41 "profile_pic_url": null, 42 "similar_pages_data": [ 43 { 44 "industry": "Software Development", 45 "link": "https://www.linkedin.com/company/google?trk=similar-pages", 46 "location": "Mountain View, CA", 47 "name": "Google" 48 }, 49 { 50 "industry": "Software Development", 51 "link": "https://www.linkedin.com/company/amazon?trk=similar-pages", 52 "location": "Seattle, WA", 53 "name": "Amazon" 54 }, 55 { 56 "industry": "Software Development", 57 "link": "https://www.linkedin.com/company/microsoft?trk=similar-pages", 58 "location": "Redmond, Washington", 59 "name": "Microsoft" 60 }, 61 { 62 "industry": "Entertainment Providers", 63 "link": "https://www.linkedin.com/company/netflix?trk=similar-pages", 64 "location": "Los Gatos, CA", 65 "name": "Netflix" 66 }, 67 { 68 "industry": "Software Development", 69 "link": "https://www.linkedin.com/company/meta?trk=similar-pages", 70 "location": "Menlo Park, CA", 71 "name": "Meta" 72 }, 73 { 74 "industry": "Motor Vehicle Manufacturing", 75 "link": "https://www.linkedin.com/company/tesla-motors?trk=similar-pages", 76 "location": "Austin, Texas", 77 "name": "Tesla" 78 }, 79 { 80 "industry": "Musicians", 81 "link": "https://se.linkedin.com/company/spotify?trk=similar-pages", 82 "location": "Stockholm, Stockholm County", 83 "name": "Spotify" 84 }, 85 { 86 "industry": "IT Services and IT Consulting", 87 "link": "https://www.linkedin.com/company/ibm?trk=similar-pages", 88 "location": "Armonk, New York, NY", 89 "name": "IBM" 90 }, 91 { 92 "industry": "Business Consulting and Services", 93 "link": "https://www.linkedin.com/company/deloitte?trk=similar-pages", 94 "location": null, 95 "name": "Deloitte" 96 }, 97 { 98 "industry": "Software Development", 99 "link": "https://www.linkedin.com/company/linkedin?trk=similar-pages", 100 "location": "Sunnyvale, CA", 101 "name": "LinkedIn" 102 } 103 ], 104 "specialities": [ 105 "Innovative Product Development", 106 "World-Class Operations", 107 "Retail", 108 "Telephone Support" 109 ], 110 "tagline": null, 111 "universal_name_id": "apple", 112 "update_data": [], 113 "website": "http://www.apple.com/careers" 114}
Here is the JSON fields documentation without including the sample values:
Yes, if you're scraping publicly available data for personal or internal use. Always review Websute's Terms of Service before large-scale use or redistribution.
No. This is a no-code tool — just enter a job title, location, and run the scraper directly from your dashboard or Apify actor page.
It extracts job titles, companies, salaries (if available), descriptions, locations, and post dates. You can export all of it to Excel or JSON.
Yes, you can scrape multiple pages and refine by job title, location, keyword, or more depending on the input settings you use.
You can use the Try Now button on this page to go to the scraper. You’ll be guided to input a search term and get structured results. No setup needed!