Most data collection problems do not announce themselves. A field returns the wrong value. A column silently pulls from the wrong section of the page. A pipeline runs without errors but the output is unusable. By the time the issue surfaces, the damage is already in the dataset. This post walks through the specific points where unstructured data collection breaks, and explains what a structured approach actually does differently at each stage. Breakdown 1: Capturing data from

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

Most developers who start evaluating extraction tools focus on accuracy first. Cost comes second, usually after the first invoice arrives. This post is about that second conversation. Specifically, what it actually costs to run a data extraction pipeline at meaningful volume, and how the Minexa API compares to LLM-based extraction across different page volumes and HTML formats. The numbers here are not estimates. They come from real page size measurements across six content t

Before committing an external API to a production pipeline, developers ask specific questions. Not vague ones about "ease of use" or "scalability" but concrete ones about how the system actually behaves under real conditions. This article answers ten of those questions for the Minexa API, the programmatic interface to Minexa's deterministic DOM-based extraction engine. 1. Do I have to write CSS selectors or XPath to define what to extract? No. The Minexa API does not require

At low volumes, feeding HTML into an LLM for extraction looks like a reasonable shortcut. At 50,000 pages a month, it stops looking reasonable entirely. The problem is not that LLMs extract data poorly in every case. The problem is that their cost model scales with token volume, and web pages are large. A realistic full HTML page averages around 572,000 tokens. At that size, even the cheapest nano-class models charge roughly $0.03 per page. At 120,000 pages a month, that is $

The assumption has become almost automatic: if you need to extract structured data from web pages, you reach for an LLM. Feed it the HTML, write a prompt, get JSON back. It works in a demo. It works on ten pages. So teams build pipelines around it and move on. The problem shows up later, quietly, in production. When extraction fails without telling you The most dangerous failure mode in any data pipeline is not a crash. It is a wrong value that looks correct. LLM-based extrac