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Inference402 Extract – Named Entity & Field Extraction
x402
Inference402 Extract – Named Entity & Field Extraction is a paid API for AI agents from inference402.worlddata402.workers.dev, paid per call via x402, $0.008/call, status unknown (last checked 2026-10-19).
Extracts specified named fields (dates, names, amounts, organizations, custom fields, etc.) from free-form text using Llama 3.3 70B, paid per-call in USDC via x402
Price / call$0.008
Success—
StatusUnknown
How this service works
Pay-per-call AI inference for agents: chat (Llama 3.3 70B / 3.1 8B), structured JSON, summarize, translate (incl. Egyptian Arabic), extract, classify, sentiment, keywords, rewrite, embeddings, moderation, vision and speech-to-text. USDC on Base mainnet over x402. No API key, no account.
Output
A JSON object with a `data` key containing arrays of extracted values keyed by each requested field name (e.g. dates, names, amounts, organizations, invoice_number), plus a `model` field indicating the Llama model used for extraction.
Example use cases
Invoice data extraction for accounting
“Pull the invoice number, amounts, dates, and vendor name out of this scanned invoice text: 'Invoice #7821 issued 2025-11-15 by Apex Supplies Co. Total due: $3,400. Client: Jordan Reeves.'”
Contract party and date parsing
“From this contract snippet, extract all person names, organization names, and dates mentioned: 'This agreement is entered into on January 10, 2025 between GlobalEdge Partners LLC and Sarah Nkrumah, represented by Thornton & Associates.'”
Support ticket entity extraction
“I need you to extract any email addresses, product names, and dates from this customer support message: 'Hi, I bought the ProMax 3000 on March 5th and my account email is user@example.com. Still no refund!'”
Technical details
Endpoint
GEThttps://inference402.worlddata402.workers.dev/v1/extract?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to pull specific named fields from unstructured or semi-structured text without writing custom regex or NLP pipelines. It is ideal for invoice parsing, contract analysis, form data extraction, and any scenario where you need flexible, LLM-powered field extraction on a pay-per-call basis with no API key or account setup. Prefer this over general chat endpoints when the task is purely extraction into a structured JSON output.
Side effects
- Text exceeds 8000 character limit — request will be rejected or truncated
- Empty or missing `fields` array — no extraction targets specified
- Requested fields not present in text — returns empty arrays for those keys
- Model hallucination — LLM may infer plausible but incorrect values for ambiguous text
- Payment failure via x402 — USDC balance insufficient or Base mainnet transaction error
- Rate limiting or Cloudflare Worker timeout on very long processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | |
| fields | array | Field names to extract, e.g. names, dates, amounts, organizations, locations, emails, or custom like invoice_number |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {
"dates": [
"2026-03-02"
],
"names": [
"Omar Khaled"
],
"amounts": [
"EGP 12,500"
],
"organizations": [
"Nile Tech LLC"
],
"invoice_number": [
"4471"
]
},
"model": "@cf/meta/llama-3.3-70b-instruct-fp8-fast"
}
}Tags
x402
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