Most developers who integrate the Minexa API use it the same way: train a scraper, pass some URLs, get structured JSON back. That covers the basics. But there is a wider set of capabilities built into the API that rarely gets used, either because it is not obvious from the docs or because the default setup already works well enough that no one goes looking further. This article covers ten of those capabilities, with enough detail to know when each one is worth reaching for. 1

Every SEC filing J.Jill submits is publicly visible on their investor relations page. The problem is that reading it is easy, but extracting it at scale is not. Form types, filing dates, PDF links, XBRL zips, conversion format arrays, and detail page URLs all sit inside a structured HTML table that changes with every new submission. Manually collecting that data is slow. Parsing it with custom selectors breaks whenever the page updates. This guide shows how to use the Minexa

Most data extraction pipelines have the same weak point: the gap between fetching a page and getting usable data out of it. Crawling is solved. Rendering is mostly solved. The part that still costs engineering time is turning raw HTML into a consistent, structured output that downstream systems can actually use. The Minexa API is built specifically for that last step, and it handles more of the pipeline than most developers expect going in. The scraper is the foundation Befor

WorkBC publishes one of the most complete public job boards in British Columbia, covering roles across every sector, employment type, and region. For developers building labour market tools, recruitment pipelines, or regional salary datasets, that listing page is a structured data source waiting to be tapped. This guide shows how to extract that data at scale using the Minexa API. Train a scraper once via the Chrome extension, then call the API programmatically against any vo

Finanzfluss is one of Germany's most widely used personal finance platforms. Its ETF search tool at finanzfluss.de/informer/etf/suche/ lists hundreds of funds with structured attributes including total expense ratios, fund volumes, share class sizes, launch dates, distribution policies, and replication methods. For developers building investment research pipelines, fund comparison tools, or cost-monitoring workflows, this page is a reliable and regularly updated data source.

The electronic Medicines Compendium (medicines.org.uk) is one of the most complete publicly accessible references for UK-authorised medicines. Every listed product links to its Summary of Product Characteristics (SmPC), its Patient Information Leaflet (PIL), and where applicable, risk minimisation materials. For pharmaceutical researchers, biotech analysts, and regulatory data teams, this is a dense, structured source worth extracting at scale. This walkthrough covers how to

The USDA Rural Development lender directory at rd.usda.gov/resources/lenders lists hundreds of approved mortgage lenders across the United States, each with a company name and a direct website link. For developers building fintech tools, mortgage research pipelines, or lender qualification workflows, that data is genuinely useful. The problem is that it sits in an HTML table with no public API and no bulk export option. This guide walks through how to extract that lender data

LeCEGEP (lecegep.ca) is the central directory for continuing education and professional development courses offered across Quebec's CEGEP network. Hundreds of institutions list their programs there, covering everything from 3D design and database management to workplace safety and HR certification. The data is public and well-structured, but there is no export button and no official API. If you need this data in a usable format, whether to map the continuing education landsca

Seek is one of the largest job boards in Australia, and its listings page for Perth alone can surface hundreds of roles across industries. If you need that data in a structured format for analysis, hiring intelligence, or market research, copying it manually is not a realistic option. This guide covers how to extract jobs data from Seek using the Minexa API developer workflow: train a scraper once using the Minexa Chrome extension, get a stable scraper_id, then call the API t

OLX is one of Eastern Europe's largest classifieds platforms. Its real estate section for Ukraine lists thousands of houses, apartments, and land plots updated daily. Getting that data into a structured format without writing a custom scraper from scratch is where the Minexa API workflow saves significant time. This guide walks through extracting house sale listings from OLX's Lviv region page using Minexa. Step 1: Train a scraper with the Chrome extension Before calling the

Most data extraction pipelines start the same way: someone needs structured data from a website, and the first instinct is to write a scraper. Then comes the selector logic, the edge cases, the JavaScript rendering layer, the proxy setup, and eventually a fragile script that breaks when the site updates. The Minexa API was built to replace that entire process with a single trained scraper and a POST request. This guide walks through exactly how that works, from training your