Give it a kununu company URL - or just a company name - and get that employer's reviews back as rows: the star rating, the headline, what the reviewer liked and didn't, the date, and whether they would recommend the place. Sorted newest-first by default, or however you choose. No kununu account, no proxy pool, no parser to keep alive.
The input is the company, not the review - you name the employers you want feedback on and the job collects what their people wrote.
One row per review, each tagged with the query it came from - so a run across a dozen employers still reconciles back to your input list.
Paste the kununu URL if you have it, or just the employer's name and the job resolves it. The URL is the better input, and for one concrete reason - only URL queries come back with a permalink to each review.
kununu sits behind bot mitigation, which is why a hand-rolled script on an office IP gets a human-verification page instead of data. Rate limiting and IP rotation happen on our side; from yours it is a form or an API call.
Choose the sort order and cap the rows per query up front, so credits go on the slice you actually want rather than on an export you delete most of.
Each row carries the review as kununu displays it: the rating, the headline, the pros and cons, the free-text comment, the date, whether the reviewer would recommend the employer, and a link straight back to the review it came from.
Text comes back verbatim, with no preprocessing applied, so downstream sentiment work reads the reviewer's own wording rather than ours. The column list below is the header row of real runs rather than a specification - and the honest part of it is which columns are usually empty, because most reviewers leave a rating and a headline and nothing else.
Taken from the header row of real runs. Where a field is marked optional below, that describes what reviewers choose to fill in - not the scrape. How often it carries a value will vary with the employer and with how many reviews you pull.
Three things to know before you write code against this. position is the reviewer’s employment status, not a position in the list - the rank is position_index, and that field exists only in the JSON, so a CSV or XLSX export has eleven columns rather than twelve. review_url comes back empty for every row of a query submitted as a company name: pass the kununu URL when you need per-review permalinks. And pros, cons and text are three independent free-text answers that most reviewers skip, while title, rating, date and recommended are the ones that reliably carry a value.
Sort order and a per-query limit decide what a run costs and what lands in the file. Both are chosen before the job starts, alongside how you hand over the list of employers.
A few examples of how teams use employer review data to answer a question they actually have.
Pull your company's full history and read the one- and two-star text together rather than one at a time. Recurring complaints surface as repeated language - far more actionable for an HR team than an average that moved a tenth of a point.
Pros, cons and the recommendation column give you the version current staff wrote, not the one the careers page did. Useful whether you are placing candidates or deciding which brief to take.
Take twenty employers in one industry and export the lot. Ratings, recommendation rates and verbatim text in a single file let you compare workplaces on evidence rather than on reputation.
No subscription, no minimum, no recurring bill. Your first 500 rows are on us - after that, pay-as-you-go at the same flat rate as every other scraper here.
Every new account, one-time. No credit card required. Sorting, per-query limits, file upload and every export format included.
Roughly $2 per 1,000 reviews. The pre-flight estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.
Volume pricing, dedicated workers and an SLA for continuous monitoring or very large historical pulls. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.The other half of a company's reputation - what customers say rather than what employees say. Title, full text, star rating, date and the author's reviewing history.
The firmographics behind the rating - industry, size band, exact headcount, headquarters and website - so a score sits next to the company it describes.
Short answer: yes for the public review content - and this export carries no reviewer identity at all.
Reviews on kununu are published to be read. The rating, the headline, the pros and cons, the date and the recommendation are shown to anyone who opens the company page, signed in or not. Collecting publicly visible feedback for research is long-established practice, and nothing here touches a login or a paywall.
One point is worth stating plainly rather than burying, because it is unusual. There is no reviewer identity in this output - no name, no profile link, no avatar. The closest thing to an identifier is position, which records only whether the person was an employee, an intern or a contractor. That is a genuine difference from a marketplace review scrape, and it is a property of what kununu publishes rather than a filter we apply afterwards.
kununu's own terms restrict automated access, so this is a terms question as well as a legal one - if you have a contractual relationship with the site, check it. We run no third-party trackers on the data layer, and your exports auto-delete after 30 days.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
500 one-time free rows on every new account - no expiry. After that it is $0.002 per row, pay-as-you-go - no card on file until you say so.
Livescraper's Kununu Reviews Scraper turns a list of employers into review data. You submit kununu company URLs or plain company names - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - choose how the reviews should be sorted, cap the rows per query if you want to, and download what current and former staff wrote as a clean CSV, Excel or JSON file.
Each row carries the review as kununu displays it: the rating to one decimal, the headline, what the reviewer liked and disliked, the remaining free-text comment, the posting date, whether they would recommend the employer, and a permalink back to the review itself. Text is preserved verbatim, which matters if the next step is a sentiment model - the analysis then reads the reviewer's wording rather than ours.
HR and employer-brand teams pull their own company's history and read the low-star text as a corpus, where recurring complaints appear as repeated language instead of a rating that drifted. Recruiters check what current staff say about an employer before a candidate asks. Researchers export twenty companies in a sector at once and compare ratings, recommendation rates and verbatim comments in a single file.
One property of this dataset is unusual and worth knowing up front: kununu publishes employer reviews without an author identity, so the export contains no reviewer name, profile link or avatar. The only field describing the person is their relationship to the company - employee, manager, intern, apprentice, student or contractor. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.