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Synergy Structured Data Extraction
x402
Synergy Structured Data Extraction is a paid API for AI agents from api.exo-trust.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).
Extracts structured data (entities, fields, and records) from unstructured text and returns the result as JSON.
Price / call$0.05
Success—
StatusUnknown
How this service works
Extract structured data (entities, fields, records) from unstructured text into JSON.
Output
A JSON object containing the extracted fields, entities, or records mapped from the input text. Field values are populated according to the provided field list or schema, with missing fields typically returned as null or omitted.
Example use cases
Invoice data extraction for accounting
“Extract the vendor name, invoice number, line items, due date, and total from this invoice text: 'From: BrightTech LLC | Invoice #2034 | Date: April 1 2025 | Due: April 30 2025 | Item: Cloud Hosting x3 @ $120 = $360 | Total: $360.' Return it as structured JSON.”
Resume parsing for hiring pipeline
“I have this raw resume text — can you extract the candidate's name, email, years of experience, current job title, and list of skills from it? Here's the text: 'Jane Doe | jane@example.com | Senior Engineer at DataCorp, 8 years exp. | Skills: Python, SQL, Kubernetes.'”
Contact extraction from email thread
“Go through this email body and pull out every person's name, company, phone number, and email address you can find: 'Reach out to Tom Harris at Nexus Corp, tom.harris@nexus.com, +1-555-204-3812, or Sara Lin at Velo Inc, sara@velo.io.'”
Technical details
Endpoint
POSThttps://api.exo-trust.com/execute/extractWhen to prefer
Use this endpoint when you need to convert free-form or unstructured text into machine-readable JSON fields — especially for documents like invoices, resumes, emails, contracts, or form responses. Prefer this over general LLM calls when you need deterministic, schema-aligned field extraction with a consistent output format and pay-per-call pricing. Best suited for single-document extraction tasks where specific field names or a JSON schema are known in advance.
Side effects
- Text contains no recognizable data matching the requested fields — returns empty or null values
- Ambiguous or conflicting values in text may result in incorrect field assignments
- Very long input text may be truncated or processed incompletely
- Malformed or missing schema/fields input may result in a generic extraction or an error
- Non-English text may reduce extraction accuracy if language support is limited
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | |
| fields | array | |
| schema | object |
Output
Schema · Response
{
"type": "json",
"example": {
"result": {
"missing": [
"example"
],
"extracted": {},
"confidence": 1
},
"status": "completed",
"receipt": {
"amount": 0.05,
"method": "x402",
"settled": true,
"currency": "USDC"
},
"task_id": "00000000-0000-0000-0000-000000000000"
}
}Tags
x402
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