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Structured data extraction

Structured data extraction is the process of turning unstructured or semi-structured content, such as a web page, into clean records that follow a predefined shape with named, typed fields.

Structured data extraction is the work of converting messy source content (an HTML page, a PDF, an email) into tidy records you can store and query. Instead of free text, you end up with fields like title, price, and publishedDate, each with a known type and a known meaning. The output is predictable, which is what makes it useful in a database or a downstream pipeline.

Traditional approaches lean on hand-written rules: CSS selectors or XPath that point at exact positions in the markup. Those rules break the moment a site changes its layout. Extracto takes a schema-first route instead. You describe the fields you want as a JSON Schema, the page is rendered in a real headless browser, and an LLM maps the visible content onto your schema at temperature zero.

Every response is validated against that schema before it is returned, so the shape is guaranteed and missing fields come back as null rather than as a guessed value. This works on any public HTTPS URL, including JavaScript-rendered and anti-bot-protected pages, because Extracto renders in a real headless browser with a managed bypass layer, and it removes the ongoing maintenance that selector-based extraction demands.

Examples

Article metadata

Extracting headline, author, and publish date from a news article into three typed fields gives you a clean record you can sort, filter, and store without parsing raw HTML yourself.

Documentation pages

Pulling a function name, its parameters, and a one-line summary from an API docs page produces a structured row per endpoint, ready to index for search or feed into another tool.

See also

Structured data extraction: FAQ

How is structured data extraction different from plain scraping?
Plain scraping often just grabs raw HTML or text. Structured extraction goes further: it maps that content onto named, typed fields and validates the result, so what you get back is a clean record rather than a blob you still have to parse.
What happens when a field is not present on the page?
Extracto returns null for that field rather than inventing a plausible value. Because every response is validated against your schema, the shape stays consistent and you can trust that a null genuinely means the data was absent.

Want to put this into practice? Extracto extracts structured data from a URL using a JSON schema, with the result validated before it leaves the API. Try the live demo or read the docs.