Extract Structured JSON from Any Website with AI (Zero Schema)

Every scraping project eventually hits the same wall: the data you want is on the page, but turning messy HTML into clean, typed records means hand-writing selectors that break the moment the site ships a redesign. This guide shows how to extract structured data from a website with AI - no selectors, no maintenance.

The short answer

Send the URL to ArrowCrawl's /extract endpoint. With zero config it reads the rendered page, auto-infers a schema from the content, and returns typed JSON generated by Claude. If you need a guaranteed shape, pass your own JSON Schema and every field comes back with the right type. One call, no parsing code:

curl -X POST https://api.arrowcrawl.com/api/v1/extract \
  -H "Authorization: Bearer ac_live_..." \
  -d '{
    "url": "https://example.com/products/atlas-desk",
    "schema": {
      "type": "object",
      "properties": {
        "name":  { "type": "string" },
        "price": { "type": "number" },
        "in_stock": { "type": "boolean" },
        "specs": { "type": "array", "items": { "type": "string" } }
      },
      "required": ["name", "price"]
    }
  }'

Zero-schema mode: let the model infer the shape

Drop the schemafield entirely and ArrowCrawl runs in zero-config mode. It renders the page, hands the meaningful content to Claude, and asks it to identify the entities and fields that matter - product name, price, author, publish date, review score, whatever the page is actually about. You get back structured JSON without ever describing it. This is the fastest way to explore an unfamiliar site and see what's extractable before you commit to a shape.

Bring-your-own JSON Schema for guaranteed output

Once you know what you want, pass a standard JSON Schema. ArrowCrawl uses it as the contract: Claude maps the page into exactly those keys, coerces types (a "$1,299" string becomes the number 1299), respects required fields, and fills nested objects and arrays. Because the output is validated against your schema, the response is safe to insert straight into a typed database or a Zod-parsed pipeline - no defensive parsing on your side.

Why this beats hand-written selectors

Traditional scrapers couple your code to the DOM: one layout change and your .product-price selector returns null. AI extraction works from the meaning of the content, not its markup, so it survives redesigns and works across sites that structure the same data completely differently. You stop maintaining brittle parsers and start describing outcomes. See the full capability list on our features page.

Do it at scale

Extraction composes with the rest of the platform. Pair /extractwith a crawl to walk a whole catalog and pull the same schema from every page, returning a clean array of records. For high-volume jobs, submit an async batch with a webhook callback so results land as they finish. Every request routes through ArrowCrawl's reputation-scored proxy pool and JS renderer, so blocked IPs and client-side content are handled for you. Credit costs are laid out on the pricing page.

Getting started

Grab a key, and your first extraction is a single POST away - the free tier includes 1,000 credits a month, enough to prototype a real pipeline. Create an account and try zero-schema mode on a URL you care about; add your own JSON Schema once you know the exact shape you need.