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Vextorium Extract Structured Data
x402
Vextorium Extract Structured Data is a paid API for AI agents from api.vextorium.com, paid per call via x402, $0.008/call, status unknown (last checked 2026-10-12).
Uses a local AI model to extract specific named fields from unstructured text such as invoices, emails, or messages, returning structured key-value pairs.
Price / call$0.008
Success—
StatusUnknown
How this service works
517 pay-per-call data APIs for AI agents: Solana and EVM on-chain data, tokenized stocks, DeFi, Hyperliquid, economy and official statistics, FX, validation, web and developer tools. Pay in USDC via x402.
Output
Returns a JSON object with a 'data' field containing the extracted key-value pairs for each requested field (null if not found), the local AI model used (e.g. Qwen3-8B), and a 'found_fields' count indicating how many fields were successfully extracted.
Example use cases
Invoice data extraction for accounting
“I have this raw invoice text: 'Invoice #INV-0042, Globex Corp, issued 2024-03-15, total $4,750.00 USD, due 2024-04-15.' Can you extract the invoice number, company, date, amount, currency, and due date from it?”
Email purchase order parsing
“Pull the supplier name, order number, total amount, and currency out of this email body: 'Dear team, please process PO-8891 from TechParts Ltd. for $12,300 USD received on January 5th, 2025.'”
Contract key term extraction
“From this contract excerpt, extract the fields: party name, effective date, and contract value — here's the text: 'This agreement is entered into by Skyline Holdings LLC effective February 1, 2025, for a total value of €85,000.'”
Technical details
Endpoint
POSThttps://api.vextorium.com/v1/extract-structured-data?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to pull specific named fields from unstructured text documents like invoices, emails, contracts, or messages and want structured JSON output. It is particularly well-suited for document automation pipelines where the field names are known in advance. Prefer it over generic LLM prompting when you want a consistent, schema-driven extraction result with a field-found count, and when paying per-call via x402 USDC micropayments is acceptable.
Side effects
- Requested fields not present in source text return null values
- Ambiguous or poorly formatted text may result in incorrect field extraction
- Very short or context-free text may yield low found_fields count
- Fields with multiple candidates in text may return only one match
- Non-standard date or number formats may be returned as-is without normalization
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {
"date": "12/09/2026",
"amount": "1.250,50",
"company": "Acme SL",
"currency": "EUR",
"invoice_number": "2026-117"
},
"model": "Qwen3-8B (local)",
"found_fields": 5
}
}Tags
x402
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