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Claim Check Extract
x402
Claim Check Extract is a paid API for AI agents from claim-check.agent-utils.workers.dev, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-15).
Extracts structured JSON from unstructured text (invoices, receipts, job posts, contacts, events) using a built-in preset or custom schema, paying per call via x402.
Price / call$0.05
Success—
StatusUnknown
How this service works
Grounded verification and extraction for AI agents: fact-check a claim against a source with verbatim quotes, or turn invoices, receipts, job posts and any text into typed JSON. Pay per call over x402, no API key. Daily model quota published at /health.
Output
A JSON object shaped by the chosen preset or custom schema containing all extracted fields (e.g. for invoices: seller, buyer, line items, subtotal, tax, total, due date, currency, invoice number), plus metadata including the model used, processing time in milliseconds, and character count of the input text.
Example use cases
Automated invoice data entry
“Extract all the structured fields from this invoice text using the invoice preset — I need seller name, tax ID, buyer, line items with quantities and prices, subtotal, tax, total, due date, and payment terms: 'Globex Corp | INV-0099 | To: Initech | 5x Gadget @ $40 = $200, 2x Doohickey @ $15 = $30 | Subtotal $230 + Tax $23 = $253 | Due 2026-10-15, net 30'.”
Job posting structured parsing
“Turn this job posting into structured JSON using the job preset: 'Senior Backend Engineer at NovaTech — Remote, $140k–$170k, 5+ years Go/Kubernetes required, apply by Nov 1 2026 at jobs@novatech.io'.”
Receipt expense categorization
“Parse this receipt into structured data with the receipt preset so I can log it as an expense: 'Starbucks #4412 | 2026-09-15 | 1x Latte $6.50, 1x Blueberry Muffin $4.25 | Total $10.75 | Card ending 4242'.”
Technical details
Endpoint
POSThttps://claim-check.agent-utils.workers.dev/v1/extractWhen to prefer
Choose this endpoint when you need to turn free-form document text (invoices, receipts, job postings, contacts, events) into typed, structured JSON without setting up an API key — especially in agentic pipelines that already handle x402 micropayments. It is ideal for one-off or low-volume extraction tasks where pay-per-call is preferable to a subscription. Prefer it over general-purpose LLM calls when you want a deterministic output schema and verbatim grounding from the source text.
Side effects
- Text exceeds 200,000 character limit — request rejected
- Custom schema exceeds 8 KB limit — schema rejected
- Model quota exhausted for the day — check /health for current quota
- Payment not processed via x402 — 402 response returned
- Malformed or ambiguous text yields incomplete extraction with null fields
- Unsupported preset value causes validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {
"tax": 29.7,
"iban": null,
"buyer": {
"name": "Beta Corp",
"taxId": null,
"address": null
},
"total": 178.2,
"seller": {
"name": "Acme Tools Ltd",
"email": null,
"taxId": "GB123456789",
"address": null
},
"dueDate": "2026-09-30",
"currency": "EUR",
"subtotal": 148.5,
"issueDate": "2026-09-01",
"lineItems": [
{
"amount": 120,
"quantity": 1,
"unitPrice": 120,
"description": "Widget Pro"
},
{
"amount": 28.5,
"quantity": 3,
"unitPrice": 9.5,
"description": "Cable"
}
],
"paymentTerms": "bank transfer, 30 days",
"invoiceNumber": "2026-0142"
},
"model": "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
"preset": "invoice",
"modelMs": 2100,
"textChars": 230
}
}Tags
x402
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