Build training datasets for AI
Turn public, structured web pages into uniform labeled records for fine-tuning and evaluation, without writing or babysitting a single scraper.
- Industries:
- AI, Research, Developer tools
The problem
Training and evaluating models starts with data collection, and that is where most projects stall. The public web has the examples you want, but every page wraps them in navigation, ads, cookie banners, and inconsistent markup. Before you can label anything, someone has to strip all of that out, and the cleanup work quietly becomes the bulk of the effort.
Hand-written scrapers make the problem worse instead of better. You write one parser per source, each tied to that site's exact layout, and each one breaks the moment the site ships a redesign. A dataset that needs a hundred sources turns into a hundred fragile scripts that someone has to keep alive, and a single broken parser leaves gaps in your data that are hard to notice until training is already underway.
What you actually want is uniform records: the same fields, in the same shape, from every source, ready to label or feed straight into a pipeline. That is a contract problem, not a parsing problem, and that is exactly what Extracto is built for.
The workflow
- 1
Define the record schema
Write a JSON schema describing the exact fields each example should contain, such as title, body text, category, and source. The schema becomes the contract every page must satisfy before it joins your dataset.
- 2
Collect your source URLs
Build your list of public HTTPS pages: documentation, Wikipedia articles, news pieces, GitHub repositories, company sites, or JavaScript-rendered and anti-bot-protected pages. Extracto works on any public URL, so you can pull from the sources your dataset actually needs rather than only the easy ones.
- 3
Send each URL with the schema
Call the API with a URL and your schema. 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, passes the page text to an LLM at temperature zero, and validates the result against your schema before returning it. Proxies and anti-bot handling are managed for you, with no setup.
- 4
Keep null fields, drop or flag them
Missing fields come back as null rather than a guess, so you can confidently filter incomplete records or route them for review. Your training set stays clean because nothing fabricated ever enters it.
- 5
Batch, store, and version the output
Loop over your URL list, write the validated JSON records to storage, and version the dataset. Because every record shares one shape, downstream labeling, splitting, and loading need no per-source special casing.
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",
"bodyText": "string",
"category": "string",
"language": "string",
"publishedDate": "string",
"sourceUrl": "string"
}Is it worth it?
Uniform records remove the preprocessing that usually dominates dataset work, and null-instead-of-guess keeps fabricated text out of training. The trade-off is a per-page cost. 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.