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AI & Web data · Use case

From any page to the exact JSON your model expects

Define a schema, send a URL, get consistent structured data back. No selectors, no parsing, no token spent on boilerplate.

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Schema to JSONIllustrative values

Schema in. JSON out. Identical on every run.

Key takeaway

LLM-ready extraction means a page arrives as Markdown or schema-shaped JSON, not 200 KB of HTML. The Unlocker API does the fetch, the unblock and the conversion in one request; failed requests are free.

Why LLM-ready extraction is hard to do at scale

There is a gap between data being on the web and data being usable by a model. Raw HTML is mostly not content: navigation, scripts, tracking, banners. Feeding it to a model wastes tokens and confuses the answer.

The obvious fix, having a model read each page and extract what you need, has three problems. It is slow, seconds per page. It is expensive at volume. And it is non-deterministic: the same page can produce different JSON on two runs, which makes downstream code fragile.

Writing selectors per site is the old answer, and it breaks every time a site changes its layout.

How Datafuel handles LLM-ready extraction

Send a URL and a schema. The Unlocker API returns the fields filled, as JSON that matches the schema every time. Or ask for Markdown and get the page's content with the boilerplate already removed.

Under the hood, extraction logic is built once per site and run deterministically after that, so the same page gives the same output and the cost stays low. When you would rather use your own model for the parsing step, you can: bring your own API key and Datafuel routes the extraction through it.

Deterministic versus notIllustrative values

Products for LLM-ready extraction

Same price on every use case. Failed requests are free and purchased traffic never expires.

One call, clean outputUnlocker API $0.042per 1KAny URL to Markdown or JSON, past any protection, in one request.
  • Any page past any protection
  • CAPTCHAs handled inside the request
  • Markdown, JSON, HTML or screenshot
  • Failed requests are free
  • MCP ready
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How it works

  1. 01Send a URL with a JSON schema, or a plain-language description of the fields.
  2. 02Datafuel fetches the page past any protection and extracts to the schema.
  3. 03Structured JSON returns, identical in shape on every call.
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What to watch for

  • Define what to return when a field is missing: null, empty or omitted. Undefined behaviour here breaks downstream code.
  • Be specific about ambiguous fields. "Price" can mean list, sale, per-unit or with tax; say which.
  • Markdown is right for reading and RAG; JSON is right for pipelines. Pick per use, not per project.

LLM-ready extraction — frequently asked questions

Do I have to write a schema?
No. You can describe the fields in plain language and Datafuel derives the schema.
Will the output be the same every time?
For a given page and schema, yes. Extraction is deterministic once the logic for a site exists.
Can I use my own model for extraction?
Yes. Provide your API key and choose the model; the parsing step runs through your provider.
What if the site changes its layout?
Extraction logic is regenerated automatically and the failed requests in between are not charged.
What formats are available?
Markdown, HTML, JSON to your schema, plus screenshots and link lists.
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