Glassdoor reviews are one of the most honest datasets on the internet. Current and former employees saying exactly what they think about culture, leadership, compensation, and work-life balance - unfiltered, in their own words. Whether you're a recruiter trying to understand why candidates are ghosting you, a job seeker vetting a potential employer, or a product marketer building a competitive intelligence file on a rival company, Glassdoor review data is invaluable.
The problem? There's no export button. Pulling review data out of Glassdoor - ratings, pros, cons, reviewer role, date - means either reading through pages of reviews manually or wrestling with a scraping setup that breaks every time Glassdoor updates its markup.
That's where PixieBrix's Page Editor comes in. It's a browser-native, point-and-click interface that lets you build custom web scrapers - including for Glassdoor - by describing what you want in plain English. No terminal. No selectors. No debugging. Just: "grab the rating, pros, cons, and reviewer title from this review" - and the AI builds the extractor for you.
In this post, we'll walk you through everything: what PixieBrix is, why Glassdoor review data matters, and a full step-by-step guide to building your own Glassdoor review scraper from scratch using the AI Page Editor.
Before we get into the how, let's talk about the why - because the use cases here span recruiting, HR, sales, and competitive strategy.
Employer branding and HR. If your company has a hundred Glassdoor reviews, reading them one by one is painful. Scraping them into a spreadsheet lets you spot patterns - recurring complaints about management, consistent praise for benefits, themes that show up in one-star reviews but not five-star ones. That kind of analysis informs real decisions about where to invest in culture and how to address retention problems.
Recruiting and candidate experience. Candidates read Glassdoor before they accept offers - or before they even apply. Recruiters who understand what their own Glassdoor page says (and what competitor pages say) can better anticipate objections, craft more honest job descriptions, and set expectations that reduce early attrition.
Competitive intelligence. Your competitors' Glassdoor pages are a window into their internal culture, leadership quality, compensation philosophy, and operational stress points. A wave of negative reviews about "poor communication from leadership" or "constant reorgs" tells you something about a competitor's stability that no press release will. Scraping and tracking that data over time gives you a living pulse on their organization.
Sales prospecting. Companies with a sudden spike in negative reviews - particularly around layoffs, leadership changes, or culture breakdown - are often in buying mode for HR tech, engagement platforms, or consulting services. Glassdoor review sentiment is an underused signal for sales teams selling into HR and people operations.
PixieBrix is a low-code browser extension platform that lets you customize, automate, and extend any website - including ones you didn't build and don't control. Think of it as a toolkit for bending the web to your workflow.
At the core of PixieBrix is the Page Editor: a point-and-click interface that lives in your browser's developer panel. With it, you can create custom browser "mods" - mini extensions that do things like extract data from a page, inject new UI elements, trigger automations, or push data to external tools.
The building blocks of every mod are called bricks - pre-made components for things like extracting HTML, transforming data, calling APIs, and writing output to a Google Sheet or clipboard. You snap them together like Lego, configure them visually, and the result runs inside your browser tab.
The AI layer is what makes all of this feel like magic. Instead of manually identifying CSS selectors or writing JavaScript to grab page elements, you describe the data you want in natural language and the AI generates the appropriate extraction logic for you. It understands the structure of the page you're on, maps your description to the right DOM elements, and wires it all up automatically.
The instant feedback loop is a standout feature: changes preview live, with no recompiling or reloading required. Iterate in seconds, not minutes.
"Vibe coding" is a term that's been picking up steam in developer circles - it describes the practice of building software by describing your intent in natural language and letting AI handle the implementation details. Instead of writing code line by line, you articulate what you want and iterate on the output.
For most scraping tasks, the hard part has always been the implementation layer: figuring out the exact HTML attributes or XPath expressions that target the right data on a specific page, handling edge cases when the page structure changes, and debugging when something breaks. Vibe coding short-circuits all of that.
With PixieBrix's AI Page Editor, you don't need to know how Glassdoor structures its review markup or which nested div holds the "Pros" text versus the "Cons" text. You just say: "extract the star rating, pros, cons, reviewer job title, and review date from this Glassdoor review" - and the AI does the selector hunting for you. If it gets something wrong, you describe the correction. You're steering, not building.
For non-technical users, this is a complete unlock. For developers, it's a massive speed multiplier. Either way, what used to take an afternoon now takes about ten minutes.
Install the PixieBrix browser extension. PixieBrix runs directly inside your browser and can interact with the SaaS tools your team already uses.
Once installed, navigate to Glassdoor. Then open the PixieBrix Page Editor - you can access it through the PixieBrix toolbar icon or via Chrome DevTools. You'll see the editor open alongside your active tab.
Here's where the vibe coding magic happens. Instead of configuring bricks manually or writing a single line of code, you just type what you want the scraper to do into the Page Editor's AI prompt.
One thing worth knowing about Glassdoor before you write your prompt: unlike LinkedIn profiles or Indeed job postings, individual Glassdoor reviews don't have their own dedicated URLs. All reviews for a company live on a single paginated company page. That means rather than scraping one review at a time, the smarter approach is to extract multiple reviews in one pass - pulling the most recent batch from whatever page you're currently on. Here's the exact prompt used to build the scraper in this post:
"When I right-click on Glassdoor from the context menu, extract the 5 most recent employee reviews on the page and copy to my clipboard. Each review should be formatted as its own row in a table with separate columns in both plain text and HTML so I can paste it nicely in a Notion table or Google Sheet. Do not include a header.
- Company Name
- Star Rating
- Reviewer Job Title
- Review Date
- Pros
- Cons
- Page URL"
That's it. You're describing the trigger (right-click context menu), the data you want (seven fields across the five most recent reviews), the output format (each review as its own table row in both plain text and HTML), and the destination (clipboard). No selectors, no configuration, no brick-by-brick assembly. In one right-click you're capturing five rows of structured review data instead of one.
After you send the prompt, PixieBrix's AI reads it, analyzes the Glassdoor page structure, and generates the entire mod for you - trigger, extraction logic, formatting, and clipboard output, all wired together automatically. No configuration required on your end.
Once the AI is done, the Page Editor reveals exactly what it constructed. You'll see a three-brick pipeline:
@review.Hit the green "Test" button in the top-right corner of the Page Editor to do a live test against the review page currently open in your tab. If everything looks right, you'll see a popup appear directly on the Glassdoor page.
From here, navigate to any Glassdoor review page and right-click anywhere on the page. Select "Copy Glassdoor Review to Clipboard" from the context menu and PixieBrix will extract the review data in real time. A small popup appears directly on the page with a single "Copy text" button - click it, and the formatted table lands on your clipboard.
Because PixieBrix copies the data in both plain text and HTML table format simultaneously, pasting works cleanly in either tool - no reformatting required.
In Google Sheets: Click into the first empty cell in your target row, then hit Cmd+V (Mac) or Ctrl+V (Windows). The data will paste across seven columns automatically - Company Name, Star Rating, Reviewer Job Title, Review Date, Pros, Cons, and Page URL each land in their own cell. If you're building a competitive intelligence tracker or an employer branding audit, just keep a running sheet open in a pinned tab and paste after every review you read.
In Notion: Click into a table row, then paste. Notion picks up the HTML table format and distributes the fields across columns cleanly. If you're pasting into a Notion database, make sure your column names match the fields you configured in the prompt - and the data will slot right in.
That's the full workflow: right-click a Glassdoor review → click "Copy text" → paste into your sheet or database. Seven fields, one click, zero manual typing.
Google Sheets and Notion are just the starting point. PixieBrix integrates directly with a wide range of databases and tools - so instead of copying to clipboard and pasting manually, you can configure your mod to push extracted data straight to wherever your workflow lives. Popular destinations include Airtable, Salesforce, HubSpot, Slack, Microsoft Excel, Coda, Monday.com, Jira, and any tool that accepts a webhook or REST API call. If your stack has an API endpoint, PixieBrix can send data to it. That means the same Glassdoor scraper mod you built in this post can feed an HR dashboard, trigger a Slack alert for your people ops team, append rows to an Airtable base, or kick off a Zapier or Make workflow - all without leaving your browser or writing a single line of code.
You don't need to configure anything from scratch. Open PixieBrix's Page Editor on any Glassdoor review page, paste the prompt below, and the mod will be built for you in seconds:
"When I right-click on Glassdoor from the context menu, extract the 5 most recent employee reviews on the page and copy to my clipboard. Each review should be formatted as its own row in a table with separate columns in both plain text and HTML so I can paste it nicely in a Notion table or Google Sheet. Do not include a header.
- Company Name
- Star Rating
- Reviewer Job Title
- Review Date
- Pros
- Cons
- Page URL"
That's the whole thing. One prompt, one built mod, one click to copy any Glassdoor review into your workflow.
Once you're comfortable with the review scraper, the Page Editor opens up a lot more. Here are four natural extensions of the same approach:
Overall Company Rating Scraper. Rather than scraping individual reviews, build a mod that extracts the top-level company scorecard from a Glassdoor company overview page - overall rating, culture score, work-life balance score, CEO approval rating, and "recommend to a friend" percentage. Run it across a list of competitor companies to build a benchmark comparison sheet.
Interview Review Scraper. Glassdoor's interview section is a goldmine for both candidates and recruiters. Scrape interview difficulty ratings, process descriptions, and specific questions that candidates report being asked - useful for candidates preparing, and for talent teams benchmarking their own interview experience against competitors.
Salary Report Scraper. Glassdoor's salary pages aggregate self-reported compensation data by role, location, and experience level. Build a mod that extracts base salary ranges, total comp figures, and data point counts from any Glassdoor salary page and logs it to a spreadsheet - useful for comp benchmarking without a dedicated salary intelligence subscription.
Competitor Review Tracker. Set up a mod that runs on page load for a competitor's Glassdoor review page and extracts the most recent reviews automatically. Pipe the results to a Google Sheet or Slack channel so your HR or competitive intelligence team gets a running feed of what employees are saying about your competitors - in real time, without anyone having to remember to check.
Each of these follows the same build pattern as the review scraper - just a different page, different fields, and a different trigger. Once you've built one, the rest take a fraction of the time.
Glassdoor review data has always been valuable. What's changed is who can access it at scale. Until recently, pulling structured review data out of Glassdoor meant knowing Python, reverse-engineering the page markup, and rebuilding your scraper every few months when Glassdoor updated its layout.
PixieBrix's AI Page Editor changes that equation entirely. You describe the data you want, the AI builds the extractor, you point it at a review page - and the data lands exactly where you need it. No code, no setup, no developer required.
If you want to try it yourself, install PixieBrix from the Chrome Web Store, open the Page Editor on any Glassdoor company page, and paste the prompt from this post. The whole setup - from install to first scraped review in a Google Sheet - takes about fifteen minutes.
And if Glassdoor is just the beginning, the same approach works on virtually any website: LinkedIn profiles, Indeed job postings, Google Maps listings, Amazon product pages, and more. Keep an eye on upcoming posts in this series.
Part of the Vibe Code Your Scraper series - building AI-powered web scrapers for popular platforms using PixieBrix's Page Editor.