AI data extraction

AI data extraction tools read a web page and return the information on it as named fields. Instead of writing a CSS selector for each price or author name, you describe what you want, or pick a page type such as product, article or job posting, and a model finds those values on the page.

The main gain is coverage. A team that collects from hundreds of different sites cannot write and maintain a parser for each one, and a model that understands what a price or a publish date looks like copes with many layouts at once. It also tends to survive small redesigns that would break a selector.

The tradeoff is certainty. A selector either matches or fails, which is easy to detect. A model can return a plausible value that is wrong, such as an old price, a related product's name or a date from a comment instead of the article. Some tools add checks for this, and some return a confidence signal. Either way, plan validation into your pipeline: type checks, ranges, required fields and spot checks against the live page.

When you test a tool, use a sample of your own pages, including awkward ones, and score each field by hand. Ask how the tool handles pages with many items, pagination, tables and content in other languages. Check whether you can define your own schema, and whether output stays stable when you rerun the same page.