What kind of data can Minexa actually collect, and from where?

Most people who discover a web scraping tool ask the same first question: what can it actually pull, and from which sites? It is a fair question. The answer shapes whether a tool is useful for your specific situation or just a solution looking for a problem.

This post answers that question directly for Minexa.ai, a Chrome extension that extracts structured data from any public website and exports it to Excel, Google Sheets, or JSON. No code required, no list of supported sites, and no need to know how websites are built.

It starts with any public URL

Minexa does not work from a catalog of pre-approved websites. There is no list of supported sources to check before you start. If a page is publicly accessible in a browser, Minexa can work with it. That includes directories, marketplaces, job boards, listing platforms, review sites, news aggregators, and most other content-heavy pages you would encounter during research or analysis.

The extension reads the structure of whatever page you open and detects the repeating patterns on it automatically. It does not need to have seen that site before. Every new page type it encounters is treated as a fresh detection task, and the result is a custom scraper built specifically for that page structure.

What kinds of data does it actually collect?

The short answer is: any data point that appears in the page structure. That includes the obvious fields a human reader would notice, and some that they would not.

Here are the categories where Minexa is used most often:

Business and contact information

Company names, job titles, email addresses, phone numbers, physical addresses, and social profile links from online directories, professional networks, and event pages. This is one of the most common starting points for teams building prospect lists or enriching existing contact records.

Product and pricing data

Product names, SKUs, prices, discount labels, stock status, ratings, review counts, and category tags from ecommerce pages. Because Minexa ties each field to a specific position in the page structure rather than interpreting the content, it does not confuse a sale price with an original price or mix up two similar fields on the same page.

Property listings

Address, price, number of bedrooms and bathrooms, square footage, listing date, agent name, and property type from real estate platforms. Minexa handles both the summary data visible on the search results page and the fuller details available on each individual listing page, in a single run.

Job postings

Job title, company name, location, salary range, required skills, employment type, and posting date from job boards. The two-layer extraction model is especially useful here: Minexa can collect the summary fields from the listings page and then follow each link to extract the full job description and requirements from the detail page, all without any manual clicking.

Reviews and ratings

Reviewer name, star rating, review text, date, and verified purchase status from product pages, app stores, and dedicated review platforms. Collecting this at scale gives a much clearer picture of sentiment than reading individual reviews manually.

Travel and accommodation data

Hotel names, nightly rates, availability, location, amenities, and guest ratings from booking and travel platforms. Scheduled runs let you track how rates shift over time without checking manually.

Event and ticket information

Event names, dates, venues, ticket categories, and availability status from event listing sites. Monitoring this on a schedule means you can track when availability changes without visiting the page repeatedly.

News and editorial content

Article titles, publication dates, author names, categories, and summary text from news sites and content aggregators. This is useful for tracking coverage of a topic, a brand, or a market over time.

Install the Minexa Chrome extension

What about data that is not visible on screen?

This is one of the more useful things Minexa does that often goes unnoticed. When it scans a page, it reads the underlying structure, not just what is rendered visually. That means it can surface data points that a human reader would not see by looking at the page: image source URLs, data attributes embedded in the HTML, link targets, and other values stored in the page code but not displayed as text.

For example, on a product page, the visible content might show a product image. Minexa can extract the direct URL of that image file, which is stored in the page structure but not something a reader would copy from the screen. The same applies to identifiers, tracking codes, and other values that developers embed in pages for functional reasons but that do not appear in the visual layout.

What happens when a field is missing on a specific page?

Not every page in a dataset will have every field populated. A job posting might not include a salary range. A property listing might not have a listed agent. When Minexa encounters a page where a trained field is absent, it returns an empty value for that column rather than filling it with a guess or a value pulled from a nearby field.

This matters more than it might seem. A blank cell is informative: it tells you the data was not there. A wrong value filled in silently is much harder to catch and can corrupt downstream analysis. Minexa's approach is to reflect the page accurately, including its gaps.

How the two-layer model expands what you collect

Many websites present data in two stages: a list page showing summary information for many results, and individual detail pages with fuller information for each one. Minexa handles both in a single job.

After confirming what Minexa detected on the list page, you have the option to instruct it to follow each result's link and extract the detail page as well. The detection on detail pages works the same way as on the list: automatic, no manual field selection required. The output combines both layers into a single structured dataset.

This means a dataset that starts as 300 rows of summary data from a listings page can become 300 rows of complete records including every field from each individual detail page, collected in one run without any manual navigation.

Does it work on JavaScript-heavy sites?

Yes. Many modern websites load their content dynamically using JavaScript rather than serving it directly in the initial HTML. Minexa handles this automatically. You do not need to configure anything differently for a JavaScript-rendered page versus a static one. The extension processes the page as it appears in the browser, which means the data you see is the data it can collect.

The same applies to pages that show different content depending on your geographic location. Minexa handles geo-targeted content without any additional setup on your part.

One scraper, many pages

Once Minexa has been trained on a page type, that structure is saved. The next time you run the same scraper, whether on the same page or on any other page with the same layout, extraction starts almost immediately. There is no repeated setup. The time investment happens once, and the same scraper can be applied to as many structurally similar pages as you need.

This is what makes volume practical. Extracting data from 50 pages of job listings takes the same setup time as extracting from 5,000. The scraper does not need to relearn the structure for each new page it processes.

See what Minexa can do for your workflow

What the output looks like

Regardless of what type of data you collect or which site you collect it from, the output format is consistent: one row per result, one column per data point, structured exactly as Minexa found it on the page. You can export to Excel, Google Sheets, or JSON depending on where you plan to use the data.

If the page has nested data, such as a list of tags or multiple image URLs associated with a single result, that structure is preserved in the output rather than flattened in a way that loses information.

Where to start

The fastest way to understand what Minexa can collect is to open a page you already care about and let it run. Install the Minexa Chrome extension, browse to any page with a list of results, and confirm what it detects. Most users have their first dataset exported within a few minutes of that first run. The range of what is collectible becomes clear quickly once you see it working on a real page you have a reason to care about.