DataFuel.dev

One query covers a whole website or knowledge base. DataFuel's crawler follows links on its own. You don't hand-build a list of URLs for a docs site or wiki. This is the core of any RAG data pipeline that needs fresh pages on a schedule. The output arrives as clean markdown, already stripped of the navigation and ad clutter that pollutes raw HTML. For RAG work, that's the difference between usable chunks and a cleaning project.

The default format is markdown tuned for retrieval pipelines and vector databases. This website to markdown conversion preserves headings, lists, and links. That gives your chunking strategy real structure to work with. You can also export JSON for AI-filtered values or plain TXT when you just need the text. Picking the right format up front saves a conversion step later, and markdown is the one most RAG stacks want.

You can point DataFuel at pages that sit behind a login. Send credentials with the request and the API handles authentication to reach private documentation, internal wikis, and gated knowledge bases. Credentials are encrypted at rest and in transit, according to DataFuel, and that matters if you're scraping internal sources rather than public pages. Not all scrapers do this, which is a real gap when your best data sits behind a login.

Define a custom JSON schema and the API uses GPT-4o to pull structured values from each page. DataFuel claims 100% structured extraction accuracy for fields like emails and other well-defined data. The tradeoff is cost. AI extraction runs 15 credits per URL instead of one. Use it when the payoff in clean structured data outweighs the 15x credit burn. Not ideal for a million-page crawl, perfect for a few hundred high-value pages.

The built-in crawler walks large sites and retries failed requests without you writing error handling. For big domains, that reliability is the difference between a complete dataset and one full of holes. Concurrency scales with your plan, from a single request on Freelancer up to 50 at once on Ultimate, so a slow crawl on the entry tier becomes much faster the higher you go.

DataFuel connects to Zapier and Make, so you can trigger a scrape from a no-code workflow instead of a script. Native n8n support is listed as coming soon. If your team leans on automation tools rather than a codebase, this keeps data prep inside the tools you already use. No glue code required.