Feed a RAG knowledge base
Turn public web pages into clean, uniform records your retrieval pipeline can chunk and embed, without stripping HTML or maintaining scrapers.
- Industries:
- AI, Developer tools, SaaS
The problem
Retrieval-augmented generation is only as good as the data behind it. Most teams start by dumping raw HTML into their pipeline, then spend weeks stripping navigation, ads, cookie notices, and footer boilerplate before they can chunk and embed anything useful. That boilerplate does not just waste storage, it pollutes retrieval, because your embeddings end up encoding menu links instead of the answer the user needs.
Hand-written scrapers make this worse over time. Every source site has its own layout, and each one breaks on its own schedule, so the ingestion step turns into a maintenance treadmill instead of a one-time setup. When a parser silently breaks, stale or empty documents slip into the index, and the model starts answering from gaps you cannot see.
What a retrieval pipeline really wants is a stream of clean, uniform records: title, body, source, and a date, in the same shape from every source. Get the input right and the rest of the pipeline stops fighting the data.
The workflow
- 1
Define the record you want
Write a JSON schema for the fields each source should contribute, such as title, author, published date, and main body text, so every record lands in the same shape regardless of which site it came from.
- 2
Point Extracto at each source URL
Send the URL and schema to the API. Extracto runs every request through a real headless browser with a managed anti-bot bypass layer that handles protected sites like Cloudflare, DataDome, and PerimeterX, extracts the fields, and validates them, so boilerplate like menus, ads, and footers never enters your dataset. It works on any public HTTPS URL, including JavaScript-rendered and anti-bot-protected pages, with proxies and anti-bot handling managed for you.
- 3
Handle nulls before indexing
Missing fields return as null instead of a guess, so you can skip empty documents and keep stale or broken pages out of the index. This prevents the silent quality decay that breaks retrieval over time.
- 4
Chunk and embed in your own pipeline
Take the clean body text and run your existing chunking and embedding steps. Because the input is already free of HTML noise, your chunker splits on real content instead of slicing through navigation and markup.
- 5
Refresh on a schedule
Re-run the same URLs and schema periodically to keep the index current. The schema stays stable even when a source site changes its layout, so refreshes do not require touching parser code.
A schema to start from
Hand Extracto a schema like this and a URL. You get back validated JSON with exactly these fields.
{
"title": "string",
"author": "string",
"publishedDate": "string",
"bodyText": "string",
"summary": "string",
"sourceUrl": "string"
}Is it worth it?
Clean, uniform records cut the preprocessing that usually dominates RAG setup, and better input means retrieval encodes content instead of boilerplate. The trade-off is a per-page cost, so this pays off most when source layouts vary or change often. Extracto works on any public HTTPS URL, including JavaScript-rendered and anti-bot-protected pages, because every request runs through a real headless browser with a managed anti-bot bypass layer. The one boundary is login-gated content like LinkedIn, X, or Instagram, which needs session cookies available on Enterprise plans. The free plan covers 100 pages with no credit card, and paid plans start at $14/month.