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AI Structured Data Extractor (Cloudflare Workers AI / Granite)
x402
AI Structured Data Extractor (Cloudflare Workers AI / Granite) is a paid API for AI agents from ai-policy-kit.moneymaker-ai.workers.dev, paid per call via x402, $0.004/call, status unknown (last checked 2026-10-28).
Extracts structured JSON data from free-form text according to a caller-supplied JSON Schema, powered by IBM Granite on Cloudflare Workers AI.
Price / call$0.004
Success—
StatusUnknown
How this service works
Generate an AI_POLICY.md and a pull request template with an AI-disclosure section for your open-source repo. Free, runs in your browser.
Output
A JSON object containing: `data` (the extracted fields matching your schema), `valid` (boolean schema validation result), `note` (extraction caveat), `engine` (model used), `attempts` (number of inference attempts), `neurons_estimated` (compute estimate), and `validated_against` (summary of schema features checked).
Example use cases
Invoice data extraction pipeline
“Parse this invoice text and pull out the vendor name, invoice number, each line item with quantity and price, the total amount, and the currency — return it all as a structured JSON object: 'TechSupply Co | INV-00882 | 5x USB-C cable @ $12.00, 2x monitor stand @ $45.00 | Total: $150.00'.”
Job posting field parser
“Extract the job title, company name, location, required skills (as a list), and salary range from this job posting text and give me back a clean JSON object: 'Senior Backend Engineer at Stripe, San Francisco CA. Skills: Python, Postgres, Kafka. Salary: $160k–$200k.'”
Contract clause structuring
“Pull out the start date, end date, party names, and payment terms from this contract snippet and return them as structured JSON: 'This agreement between Acme Corp and Buildright LLC commences January 1 2025 and ends December 31 2025. Payment due net-30 upon invoice.'”
Technical details
Endpoint
GEThttps://ai-policy-kit.moneymaker-ai.workers.dev/api/ai/extract?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to extract structured, schema-validated JSON from free-form text in a single low-latency call, especially when you already have a JSON Schema defined. It is ideal for invoice parsing, document field extraction, and any NLP-to-JSON pipeline where you want schema enforcement and validation feedback in one response. Prefer it over general LLM calls when you need cost predictability ($0.004/call), schema validation, and a lightweight extraction-only API without managing prompts yourself.
Side effects
- Schema too large (>8,000 chars) or contains unsupported $ref — returns error or partial extraction
- Text exceeds 12,000 character limit — truncated or rejected
- Schema uses unsupported keywords (e.g. $ref) — validation skipped or degraded
- Model fails to produce valid JSON matching schema — valid:false returned with best-effort data
- Ambiguous source text leads to incorrect field extraction — check important values against source (as noted in response)
- Payment of 0.004 USDC not provided — HTTP 402 returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {
"date": "3 Oct 2026",
"items": [
{
"name": "cordless drill",
"quantity": 2,
"unit_price": 4500
},
{
"name": "safety goggles",
"quantity": 1,
"unit_price": 350
}
],
"total": 9350,
"seller": "Acme Tools Pvt Ltd",
"currency": "Rs",
"invoice_number": "INV-2041"
},
"note": "Machine extraction: check important values against the source.",
"valid": true,
"engine": "@cf/ibm-granite/granite-4.0-h-micro on Cloudflare Workers AI",
"attempts": 1,
"neurons_estimated": 1.56,
"validated_against": "your schema (type/properties/required/enum/items/min/max/pattern/anyOf/oneOf; $ref unsupported)"
}
}Tags
x402
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