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

Tracking used car prices across a city like Mumbai page by page is the kind of task that sounds manageable until you actually try it. CarTrade lists hundreds of second-hand vehicles with prices, mileage, fuel type, and photo galleries per listing. Getting all of that into a spreadsheet manually takes hours. This guide shows how to do it in minutes using the Minexa.ai Chrome extension. Watch the full tutorial first The video below walks through the entire extraction from openi

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

AI web scraping has moved from a niche engineering topic to something data teams across industries are actively building into their workflows. The tooling landscape has expanded quickly, and the options now range from open-source Python libraries to fully managed APIs with LLM-powered extraction. That variety is useful, but it also means the differences between approaches matter more than ever, especially when accuracy and cost at scale are on the line. This article breaks do

When scraping moves beyond a handful of pages, the architecture required changes significantly. A script that works on one page rarely survives contact with thousands. Queues fill up, proxies get blocked, parsers break on layout variations, and storage becomes a bottleneck. Understanding what each layer of a large-scale setup actually does helps clarify where effort is worth spending and where it is not. The core layers every large-scale scraping system needs A production scr

Foundit is one of Southeast Asia's most active job platforms, and its Philippine portal at foundit.com.ph lists thousands of accounting, finance, and professional roles updated daily. If you are building a hiring intelligence tool, a salary benchmarking dataset, or a job market tracker, that volume of structured listing data is exactly what you need. The challenge is getting it out in a usable format without writing and maintaining a custom scraper from scratch. This guide sh

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

Tracking every press release, product announcement, and leadership update from a fast-moving digital bank like Starling Bank by hand is the kind of task that sounds manageable until you actually try it at scale. Starling Bank's news page at starlingbank.com/news/ publishes a steady stream of articles covering everything from AI feature launches and bond issuances to SME research and leadership appointments. If you need that data in a structured format for competitive monitori

Zerodha publishes sector-level stock listings that are genuinely useful for anyone tracking Indian equity markets by industry. The agriculture sector page, for example, lists every company in that category with its market capitalization and current trading price. The data is public, it updates regularly, and it covers dozens of companies per sector. The challenge is that it sits inside a web page with no export button and no API. Getting it into a spreadsheet the manual way m

Franchise and business opportunity data changes constantly. Listings appear, prices shift, categories expand. If your pipeline depends on that data being current and structured, manually checking Seek Business page by page is not a sustainable approach. This guide walks through how to extract franchise and business listing data from Seek Business (seekbusiness.com.au) using the Minexa API — a two-phase workflow where you train a scraper once using the Minexa Chrome extension,

Restaurant data is genuinely useful for a wide range of applications: hospitality intelligence platforms, travel recommendation engines, food media products, and market research tools all depend on structured, reliable listings. AGFG (agfg.com.au) is one of Australia's most established dining guides, covering awarded restaurants across every state with chef hat ratings, cuisine classifications, and regional breakdowns. The challenge is that none of this data is available via

Collecting bike specification and pricing data from BikeDekho manually means opening dozens of pages, copying values one by one, and still ending up with a spreadsheet that goes stale the moment prices update. This walkthrough shows how to extract that data automatically using the Minexa.ai Chrome extension, no code required. Watch the full video tutorial first, then follow the steps below. Step 1: Open Minexa.ai and navigate to BikeDekho Install the Minexa.ai Chrome extensio

Immigration research involves tracking hundreds of forum threads across country-specific portals, and doing that manually does not scale. This walkthrough shows how to extract structured data from Visa Journey (visajourney.com) using the Minexa API, covering every step from scraper creation to a production-ready Python script. Step 1: Open the target page Navigate to the Visa Journey country portal, for example the Brazil-specific thread listing at visajourney.com/portals/ind

ALDI Australia runs a rotating Special Buys catalogue that changes weekly, and the Pantry Staples theme alone can list dozens of branded grocery products in a single session. If you want that product data in a spreadsheet without copying it row by row, this guide shows you exactly how to do it using the Minexa.ai Chrome extension. Watch the full walkthrough first, then follow the steps below. What page are we scraping? The starting URL is the ALDI Special Buys page filtered t

Xero Central publishes a live schedule of accounting and tax training sessions at learning.central.xero.com/student/all_sessions. The page lists upcoming workshops, webinars, and certification events across regions including EMEA, ANZ, US, and CA. Each row carries a session title, scheduled time, instructor name, enrollment link, and session identifiers embedded in the page structure. This guide covers how to extract that data programmatically using the Minexa API. The workfl

Trading Economics publishes a live stream of commodity prices, currency rates, and market indicators across hundreds of instruments. The data is public, structured, and updates continuously. Getting it into a pipeline programmatically is a different story. This guide walks through how to extract structured alternative data from tradingeconomics.com/stream using the Minexa API. The workflow has two phases: train a scraper once using the Minexa Chrome extension, then call the A

Collecting job listings from Adzuna manually means opening pages, copying rows, and repeating that across dozens of search result pages. The Minexa API replaces that entire process with a single trained scraper and a POST request. This guide walks through how to extract structured job data from adzuna.com.au using the Minexa API, covering the full workflow from training the scraper to calling the API and working with the fields it returns. Before and after: navigating to Adzu

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

Getting data from a website into Excel sounds simple until you actually try it. The page loads fine in your browser, the data is right there, but copying it manually takes hours, and the built-in Excel tools stop working the moment a site uses JavaScript or blocks automated requests. Here is a clear breakdown of every practical method, what each one handles well, and where each one falls short. Method 1: Excel Power Query (built-in) Excel has a built-in feature under the Data

Indonesia's logistics sector spans hundreds of verified freight forwarders, cargo agents, and supply chain operators, all listed publicly on Indonetwork (indonetwork.co.id). Getting that data into a spreadsheet manually is slow and error-prone. This guide shows how to extract it in minutes using the Minexa.ai Chrome extension, no code required. Stage 1: Open the directory and launch the extension Navigate to the Indonetwork cargo and logistics company directory at indonetwork