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August Services — Structured JSON Extraction from Unstructured Text
x402
August Services — Structured JSON Extraction from Unstructured Text is a paid API for AI agents from api-v3qhpbwutp.august.services, paid per call via x402, $0.05/call, status down (last checked 2026-09-15).
Extracts structured JSON fields from free-form text according to a caller-supplied JSON Schema, returning nulls for fields not found in the text.
Price / call$0.05
Success—
StatusDown
How this service works
Extract structured JSON from unstructured text against a JSON Schema you supply. Returns data conforming to your schema, or nulls where the text does not contain a field. Runs on dedicated local hardware — no per-token cloud billing. Typical alternative: $15-30 per 1,000 pages.
Output
A JSON object whose keys match the schema you supplied. Fields successfully extracted from the text contain their parsed values (strings, numbers, etc.); fields the model could not find in the text are returned as null. The example response shape is {"total": 1240, "vendor": "Acme Ltd", "invoice_number": "4471"}.
Example use cases
Invoice data extraction pipeline
“Pull the vendor name, invoice number, and total from this raw invoice text and give me structured JSON: 'Bill from Globex Corp, Ref: INV-9923, Amount owed: $3,450.00'. Schema fields: vendor, invoice_number, total.”
Contract clause parsing
“Extract the start date, end date, and party names from this contract snippet and return structured JSON — here's the text: 'This agreement is entered into on January 1 2025 between Initech LLC and Umbrella Corp, and expires December 31 2025.'”
Customer intake form structuring
“I've got raw text from a customer intake form — can you pull out the customer name, email, and issue description as structured JSON? Text: 'Name: Sarah Bloom. Email: sarah@bloom.co. Issue: My order #5523 arrived damaged last Tuesday.'”
Technical details
Endpoint
POSThttps://api-v3qhpbwutp.august.services/v1/extractWhen to prefer
Choose this endpoint when you have free-form text (emails, pasted documents, notes, reports) and need to extract specific named fields into a structured JSON object. It is especially valuable over manual parsing or regex when the text is inconsistently formatted. Prefer this over the sibling PDF extraction endpoint when the source is already plain text rather than a PDF document. At $0.05 per call with no per-token cloud billing, it is cost-effective for high-volume extraction pipelines compared to GPT-4 or Claude-based alternatives ($15–30 per 1,000 pages).
Side effects
- Text contains no content matching a schema field — all values returned as null
- Malformed or invalid JSON Schema supplied — likely returns an error or unpredictable output
- Ambiguous text with multiple possible values for a field — model may pick one without warning
- Very long input text may exceed processing limits
- Schema fields with vague names may result in incorrect extraction or null values
Output
Schema · Response
{
"example": {
"total": 1240,
"vendor": "Acme Ltd",
"invoice_number": "4471"
}
}Tags
x402
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