TeamBlind Scraper

💼 TeamBlind Scraper

Scrape employee reviews from TeamBlind for any company: the full pros and cons, the overall rating and five sub-ratings, job title, tenure, reviewer location and the company's own profile. Paste review URLs or just type company names. No login, no browser, and 100 reviews take about 20 seconds.

✨ Why use TeamBlind Scraper?

• 📝 Full pros and cons on every page — TeamBlind hides the review text after the first page for visitors without an account. The scraper reads each hidden review from its own public page, so every row has the real pros, cons and reason for leaving.
• ⭐ Six ratings per review — overall plus career, work-life balance, compensation, culture and management, each 1 to 5.
• 🏢 Company data on every row — company rating, review count, industry, website, HQ, size, founded year and logo, so a CSV needs no join. Optional company summary rows add average sub-ratings and where reviewers are based.
• 🎯 Real filters — sort order, reviewer country or city, job function, current or former employees, rating range and a "newer than" date.
• 🔁 Monitoring mode — save only reviews that earlier runs have not saved yet; schedule it daily or weekly.
• ⚡ Fast and light — plain HTTP on datacenter proxies, pages fetched in parallel; a 100-review job finishes in about 20 seconds and 1,000 reviews in about a minute.

🎯 Use cases

• 🧑‍💼 Recruiting and employer branding — see what engineers and managers say about pay, management and work-life balance at the companies you hire against.
• 📊 Competitive intelligence — compare sub-ratings across competitors, or track how sentiment at one company moves after layoffs, reorgs or a return-to-office policy.
• 💹 Investment research — use workforce sentiment as an alternative-data signal next to financials.
• 🔬 Academic and HR research — build datasets of anonymous workplace reviews by job function, country and tenure.
• 📰 Journalism — find and quote first-hand reviews (with links) about a company's culture.
• 🛠️ Internal dashboards — pull your own company's reviews into BI tools on a schedule.

🚀 How to use

1. Paste one or more TeamBlind company review URLs (for example https://www.teamblind.com/company/Twilio/reviews) or type company names such as Stripe or Capital One.
2. Set Max reviews per company (0 for all of them).
3. Optionally pick a sort order and filters (country or city, job function, current or former employees, ratings, date).
4. Click Start and wait for the run to finish.
5. Download the results as JSON, CSV, Excel or HTML, or read them through the API.

📥 Input

{
  "startUrls": ["https://www.teamblind.com/company/Twilio/reviews"],
  "companyNames": ["Stripe", "Capital One"],
  "maxReviews": 100,
  "sortBy": "recent",
  "location": "United States",
  "employeeStatus": "former"
}

📤 Output

One row per review. With Add one company summary row per company on, the dataset also holds one dataType: "company" row per company.

Example review row (from a test run, nickname and member id changed):

{
  "dataType": "review",
  "id": 416959,
  "urlAlias": "FryL2VRyqR",
  "rating": 5,
  "ratingsBreakdown": { "career": 4, "balance": 4, "compensation": 3, "culture": 5, "management": 3 },
  "summary": "Great people and WLB",
  "pros": "Good teammates and people in the company, they are very supportive. ",
  "cons": "Need to have more product as SMS will die soon. This is 199x technology.",
  "reasonResign": null,
  "jobTitle": "Technical Account Manager",
  "memberJobgroupId": 8402,
  "jobFamilyId": 6,
  "reviewDate": "2025-10-01T16:07:15.000Z",
  "updatedAt": "2025-10-21T06:37:43.000Z",
  "approvedAt": "2025-10-01T16:07:15.000Z",
  "helpfulCnt": 0,
  "companyResponse": null,
  "companyResponseAt": null,
  "memberLocation": "Singapore",
  "memberLocationCountryIsoCode": "SG",
  "memberStart": 2022,
  "memberEnd": null,
  "isCurrentEmployee": true,
  "nickname": "j*****",
  "memberId": 1000001,
  "isPinned": "N",
  "isFeatured": null,
  "isBlindPick": false,
  "status": "A",
  "companyName": "Twilio",
  "companyUrlAlias": "Twilio",
  "companyPageId": 89,
  "companyChannelId": 100109,
  "companyRating": 3.5,
  "companyReviewCount": 1270,
  "companyIndustry": "Internet",
  "companyWebsite": "www.twilio.com",
  "companyHqLocation": "San Francisco, CA",
  "companyEmployeeSize": "1,001 to 5,000 employees",
  "companyFounded": "2008",
  "companyLogoUrl": "https://static.teamblind.com/img/channel/logo/us/3ee5273a649429.png",
  "sourceUrl": "https://www.teamblind.com/company/Twilio/review/FryL2VRyqR",
  "fullTextHidden": false,
  "scrapedAt": "2026-10-22T04:28:15.687Z"
}

Example company summary row (trimmed):

{
  "dataType": "company",
  "urlAlias": "Capital-One",
  "companyName": "Capital One",
  "webSiteUrl": "www.capitalone.com",
  "founded": "1994",
  "employeeSize": "10,000+ employees",
  "hqLocation": "Mc Lean, VA",
  "industryName": "Financial Services",
  "rating": 3.5,
  "ratingsBreakdown": { "career": 3.2, "balance": 4.2, "compensation": 3.3, "culture": 3.4, "management": 3 },
  "reviewCount": 3616,
  "cityDistribution": [{ "name": "McLean, VA", "count": 920 }, { "name": "New York, NY", "count": 514 }],
  "countryDistribution": [{ "isoCode": "US", "name": "United States", "count": 3494 }],
  "sourceUrl": "https://www.teamblind.com/company/Capital-One/reviews"
}

💰 Pricing

You pay $0.70 per 1,000 results (each review row, and each company summary row when you turn them on), plus platform usage (compute and proxy traffic). Platform usage is not included in the result price.

Worked example: 1,000 reviews ≈ $0.70 in results + about $0.06 platform usage ≈ $0.76 (measured on a 1,000-review test run). Most of the usage is datacenter proxy traffic for opening the reviews whose text TeamBlind hides; with Get the full pros and cons off, the proxy traffic drops to well under a cent per 1,000 reviews.

❓ FAQ

Is it legal to scrape TeamBlind? The scraper only reads public pages that anyone can open without an account. Reviews are anonymous and TeamBlind masks nicknames. You are responsible for using the data in line with TeamBlind's terms and the data protection laws that apply to you.

Do I need a TeamBlind account, cookies or my own proxy? No. No login or cookies are used. Datacenter proxies are the default and work well; if they are refused, the run switches to US residential proxies on its own.

How many reviews can I get? As many as the company has, up to TeamBlind's listing limit: the site shows at most 10,000 reviews per sort order. For bigger companies the run reads the list from both ends (newest and oldest first), which reaches about 20,000 reviews; add a city or job function filter to reach more.

Why is a field empty? TeamBlind leaves some fields empty for many reviews: reasonResign is only given by some former employees, companyResponse only when the company replied, and memberEnd only for former employees. fullTextHidden: true means a review's own page could not be read in that run, so its pros and cons are empty.

Can I scrape only new reviews on a schedule? Yes. Turn on Only reviews not saved by earlier runs, sort by most recent and schedule the actor. Each run saves only reviews it has not saved before.

Can I use it through the API? Yes. Start runs and download the dataset with the Scrapely API, or connect the actor to your own tools with webhooks and scheduled runs.

⚠️ Limits

• TeamBlind lists at most 10,000 reviews per sort order; companies with more than about 20,000 reviews need filters (city, job function) to be read completely.
• TeamBlind's own country filter is a loose text match, so country filters are checked on each review instead: the run reads every listing page of that company to find them.
• Only reviews. Salaries, benefits, posts and discussion threads are not scraped.
• Company names are matched to TeamBlind's URL names; when a name has no exact match the closest one is used (for example JPMorgan Chase -> JPMorgan). Use the company's TeamBlind URL to be exact.