AI Data Extraction to JSON

x402

AI Data Extraction to JSON is a paid API for AI agents from agentsvc.io, paid per call via x402, $0.007/call, status unknown (last checked 2026-10-11).

Extracts structured fields from a web page or raw text using AI and returns them as typed JSON

Price / call$0.007

Seller volume →—

Success—

StatusUnknown

How this service works

86 APIs for AI agents: web scraping and crawling to Markdown, AI summaries, data extraction and cited answers, screenshots and PDF, PDF, OCR and document conversion, news, paper and developer search and more, paid per call with USDC on Base, Solana, Polygon or Arbitrum via x402. No account, no API key, free sample on every service.

Output

Returns a JSON object with a 'data' key containing: the extracted field values keyed by the field names you defined, the AI model used, the page title, source URL, extraction timestamp, total fields requested, fields successfully filled, and whether input was truncated. Wrapped in a top-level 'success' boolean.

Example use cases

Extract company registration from impressum

“Go to https://www.example-company.de/impressum and extract the company name, VAT ID, registered address, and registration number as structured JSON.”

Parse product details from e-commerce listing

“Extract the product name, price, availability status, and ASIN from this Amazon product page: https://www.amazon.com/dp/B09XYZ — return them as typed fields.”

Structure raw invoice text into fields

“I have raw invoice text I'll paste — pull out the invoice number, issue date, total amount due, and supplier name and give them back to me as JSON fields.”

Technical details

Endpoint

POSThttps://agentsvc.io/api/v1/proxy/page-extract?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to extract specific, named fields from a web page or raw text and receive them as typed, structured JSON — especially when the source is unstructured (HTML, free text, PDFs rendered as text). Prefer this over generic scraping when you want AI to handle ambiguous layouts, infer field values, and return clean typed data without writing custom parsers. It is ideal for one-off or ad-hoc extractions where defining a schema upfront is easier than building a scraper.

Side effects

URL is unreachable or returns non-200 — extraction fails or returns empty fields
Page is JavaScript-heavy and content is not in static HTML — fields may be empty or incorrect
Raw text exceeds 60,000 character limit — input gets truncated, reducing accuracy
Field descriptions are ambiguous — model may fill wrong values or leave fields empty
Instructions exceed 500 character limit — request rejected or truncated
Payment in USDC not confirmed — request blocked before processing

Input

Schema · Request body

Field	Type	Description
url	string	Page to extract from (or send text)
text	string	Raw text instead of a URL, max 60,000 chars
fields	object	Field name -> description string, or {type, description}. Types: string, number, integer, boolean, string[], number[]
instructions	string	Optional extra rules, max 500 chars

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {
      "data": {
        "city": "Berlin",
        "vat_id": "DE 260543043",
        "company": "Zalando SE",
        "register_number": "HRB 158855 B"
      },
      "model": "claude-haiku-5-5",
      "title": "Zalando wohnen",
      "source": "https://www.zalando.de/zalando-impressum/",
      "extracted_at": "2026-10-10T09:58:24.270Z",
      "fields_total": 4,
      "fields_filled": 4,
      "input_truncated": false
    },
    "success": true
  }
}

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