DomCop is a domain research platform that surfaces domain authority metrics, open pagerank scores, and web asset statistics across millions of indexed domains. The stats page at domcop.com/stats aggregates this data into a structured listing format, making it a useful source for SEO analysts, domain investors, and data teams building competitive intelligence pipelines. This guide walks through extracting that data programmatically using the Minexa API, a deterministic AI web

Government form catalogues hold more structured data than they appear to at first glance. The Service Canada eForms catalogue lists hundreds of official government forms across departments including Employment and Social Development Canada, Human Resources and Social Development Canada, and others. Each entry carries a form code, a descriptive title, an issuing department, and a link to the form detail page. Collecting that data manually, row by row, across multiple pages is

ScrapingBee is a well-known scraping API. It handles JavaScript rendering, proxy rotation, and basic extraction rules, and it works for many standard use cases. But teams that push it harder tend to run into the same friction: credit costs that are hard to predict, output that still needs parsing before it is usable, and limited options when a site is heavily protected or structured data is the actual goal. This comparison covers the most relevant alternatives available today

Firecrawl has built a strong reputation as a go-to tool for converting web pages into LLM-ready Markdown. It handles JavaScript rendering, offers a clean API, and integrates well with frameworks like LangChain and LlamaIndex. For many developers building AI pipelines, it was a natural first choice. But as projects scale, a few structural issues tend to surface: credit costs that grow faster than expected, an AGPL 3.0 license that complicates commercial use without enterprise

If you have ever tried to collect data from a website at any meaningful scale, you have probably run into some form of resistance. Requests start failing. Pages return empty content. CAPTCHAs appear. IP addresses get blocked. These are not accidents — they are deliberate technical measures put in place to slow down or stop automated access. Understanding how these defenses work is useful whether you are building a data pipeline, doing market research, or just trying to pull a

Legal and compliance data scattered across dozens of pages is genuinely difficult to work with. LegalDesk publishes a wide catalogue of contract templates, agreement guides, and compliance documents, and getting that content into a structured format manually takes far longer than it should. This walkthrough shows how to extract that data using the Minexa.ai Chrome extension in a few minutes, no code required. Watch the full tutorial first Outcome: a confirmed starting point o

When someone searches for a web scraping agency, they are usually not in love with the idea of hiring one. They are looking for data they cannot easily get any other way, and they want someone to handle the hard parts. That is a reasonable starting point, but it is worth understanding what you are actually buying before signing anything. What a web scraping agency actually does Managed scraping services handle the full pipeline on your behalf: building the scrapers, managing

Car listing pages are packed with structured data. Every search result contains make, model, trim, price, mileage, location, dealer name, fuel type, transmission, and more. But that data sits locked inside HTML, visible on screen and completely inaccessible at scale without a scraping workflow. For automotive marketplaces, that gap is a real operational problem. Manually collecting listing data across dozens of sources is slow, inconsistent, and impossible to maintain. This p

IMDb holds some of the most detailed publicly available entertainment data on the internet. Episode lists, air dates, ratings, cast details, and season breakdowns are all sitting right there on the page. The problem is that none of it is structured in a way you can actually work with. If you have ever tried to copy episode data from IMDb into a spreadsheet manually, you already know how quickly that falls apart. This post walks through how to extract that data cleanly using t

Flight price data sitting behind a search interface is genuinely difficult to collect at scale. Kayak surfaces destination tiles with prices, stop counts, and editorial tags, but none of that is available via an official API. This walkthrough shows how to extract it using the Minexa API, a data extraction platform that combines a browser-based training step with a programmatic extraction endpoint. The workflow has two phases: train a scraper once using the Minexa Chrome exten

The London Metal Exchange publishes a steady stream of market reports, press releases, member notices, and metal price pages. If you are tracking copper warrant stocks, monitoring off-warrant reporting changes, or building a dataset of LME announcements over time, the search interface at lme.com/search?searchTerm=stocks surfaces exactly the kind of structured content you need. The problem is that it is all locked inside a web page, not a spreadsheet. This guide walks through

Manually copying flight data from Cheapflights row by row is the kind of task that looks simple until you are three pages in and the prices have already changed. This guide shows how to connect the Minexa API to Cheapflights and pull structured flight data programmatically, starting from a single trained scraper. What the data looks like before and after Without automation, flight data from Cheapflights lives in a rendered page that updates dynamically. You can see the prices

You want to collect job listings. You open a job platform, see hundreds of relevant postings, and immediately hit a wall the moment you try to pull that data into a spreadsheet. This is one of the most common starting points for anyone learning data collection. And it tends to go wrong in the same predictable ways. The platform problem is not what most people think The instinct is to treat all job data as equally accessible. It is not. There is a meaningful difference between

Collecting product data manually is one of those tasks that feels manageable until it isn't. A few pages, a spreadsheet, some copy-pasting. Then the product catalog grows, the sites multiply, and suddenly what took an afternoon now takes a week. This post answers the questions that come up most often when people start thinking seriously about automating ecommerce data collection. Why does manual product data collection break down? The core problem is volume. Browsing through

Collecting job listing data from Doda, one of Japan's largest job platforms, by hand is slow and breaks down quickly once you need more than a handful of records. Each company card on Doda surfaces multiple job references, salary structures, and linked detail pages, and copying that manually across hundreds of pages is not a realistic workflow. This guide walks through exactly how to extract that data using the Minexa.ai Chrome extension, no code required, starting from the c

Salary data is publicly available on dozens of platforms. The problem is not access. The problem is that reading it page by page, role by role, does not scale. Compensation researchers who need structured, comparable data across hundreds of roles end up spending most of their time copying and formatting rather than actually analysing. This guide covers how to extract salary data from role detail pages using the Minexa.ai Chrome extension, what fields you can expect to capture

Drug label data sitting behind a browser is useless to a pipeline. This guide shows how to pull structured pharmaceutical listings from PDR at scale using the Minexa API, covering every field worth knowing and the exact request body to get started. What PDR exposes at the browse-by-drug-name endpoint PDR (pdr.net) is a clinical reference platform used across healthcare and biotech. Its drug browse page at pdr.net/browse-by-drug-name lists thousands of drug entries alphabetica

Federal job listings contain more structured data than most people realize. USAJobs surfaces job titles, hiring organizations, salary ranges, employment types, application deadlines, and eligibility paths all on a single search results page. The challenge is getting that data out in a usable format without copying rows manually. This guide walks through how to extract that data using the Minexa.ai Chrome extension, a no-code tool that turns any structured web page into a down

Collecting property listing data from Dom.ria.com manually means opening dozens of pages, copying prices, addresses, and specs one by one, and ending up with a spreadsheet that is already outdated by the time you finish. This post shows how to replace that process with a single API call using Minexa, a web data extraction platform that turns any structured listing page into clean JSON without writing selectors or parsing HTML by hand. Before: what you are dealing with on Dom.

Travel Wisconsin's food and drink directory lists hundreds of restaurants, bars, breweries, cafes, and distilleries across the state. If you want that data in a spreadsheet rather than clicking through pages one by one, the Minexa.ai Chrome extension handles the full extraction without any code. Here is the complete walkthrough, step by step. Watch the full tutorial first The video below covers the entire workflow from installing the extension to downloading your export. Watc