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PDF Structured Data Extractor (august.services)
x402
PDF Structured Data Extractor (august.services) is a paid API for AI agents from api-v3qhpbwutp.august.services, paid per call via x402, $0.12/call, status down (last checked 2026-09-15).
Extracts structured JSON from a PDF document (supplied as URL or base64) against a caller-provided JSON Schema, with flat per-document pricing regardless of page count.
Price / call$0.12
Success—
StatusDown
How this service works
Extract structured JSON from a PDF against a JSON Schema you supply. Send a URL or base64 bytes; get back your schema, filled. Whole-document pricing, not per-page: a 20-page contract costs the same as a 2-page invoice. Reads the embedded text layer — scanned image-only PDFs are rejected with a clear error rather than empty fields.
Output
Returns a JSON object with a 'data' field containing the caller's JSON Schema filled with values extracted from the PDF, plus a 'pages' field indicating how many pages were processed. Fields that could not be found are omitted or null per schema constraints.
Example use cases
Invoice data extraction for accounting
“Pull the invoice number, total amount, and due date out of this PDF invoice — here's the URL: https://bills.acme.com/inv-2201.pdf — and return them as structured JSON with fields invoice_number, total, and due_date.”
Contract clause parsing for legal review
“I have a 20-page service agreement PDF at https://legal.firm.com/contract-005.pdf. Extract the parties' names, effective date, termination clause, and governing law into structured JSON for me.”
Bank statement data ingestion
“Parse this PDF bank statement from https://bank.example.com/statement-april.pdf and fill a JSON Schema with fields: account_number, statement_date, opening_balance, closing_balance, and a list of transactions each with date, description, and amount.”
Technical details
Endpoint
POSThttps://api-v3qhpbwutp.august.services/v1/pdf-extractWhen to prefer
Choose this endpoint when you need to extract structured fields from a text-layer PDF using your own schema definition, especially for multi-page documents where per-page pricing would be costly. Prefer it over generic text extractors when you want JSON output conforming to a specific schema rather than raw text, and over web-scraping endpoints when your source is a PDF rather than an HTML page.
Side effects
- Scanned image-only PDFs (no embedded text layer) are rejected with a clear error rather than returning empty fields
- Invalid or inaccessible PDF URL returns an error
- Malformed JSON Schema input causes a validation error
- PDF that is password-protected or encrypted cannot be read
- Fields in the schema with no matching content in the PDF may be null or absent
Output
Schema · Response
{
"example": {
"data": {
"total": 4820.5,
"due_date": "2026-05-01",
"invoice_number": "INV-2201"
},
"pages": 3
}
}Tags
x402
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