Extract structured data from any unstructured text into your own JSON Schema — s
Extract structured data from any unstructured text into your own JSON Schema — structured-data / information extraction, text-to-JSON, LLM data enrichment. You supply the schema; the LLM returns a matching object. Works for contacts, invoices, events, product specs, medical records, legal clauses, resumes — any shape. Returns the extracted object plus token usage; unfound fields are omitted or null.
Answeringour last check, 2026-09-24
1 of 1checks answered this week
922 msmedian answer time
$0.01listed price per call
$0.01price it asked us
Paid test badge: not yet. The checks above are free: we call the tool without paying and read the payment request it sends back. The Verified badge needs paid calls whose answers match the promised output, and nobody can buy a badge.
Endpoint
POST https://netintel.dev/schema-parse/extract
| Category | Code and developer |
|---|---|
| Provider host | netintel.dev |
| Networks | eip155:8453, solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp |
| Payment schemes | exact |
| Self-reported calls, 30 days | 4 from 2 payers (the provider's figure, not ours) |
Our checks, last 30 days
| Day | Result | HTTP | Asked | Time |
|---|---|---|---|---|
| 2026-09-24 | valid payment request | 402 | $0.01 | 922 ms |
Example input (from the provider)
{
"body": {
"raw_text": "Hi I'm Sarah Chen, VP of Engineering at Acme Corp. Reach me at sarah.chen@acme.com or 555-867-5309.",
"target_schema": {
"properties": {
"company": {
"type": "string"
},
"email": {
"type": "string"
},
"name": {
"type": "string"
},
"phone": {
"type": "string"
},
"role": {
"type": "string"
}
},
"required": [
"name",
"email"
],
"type": "object"
}
},
"bodyType": "json",
"method": "POST",
"type": "http"
}
Promised output schema (from the provider)
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {
"input": {
"additionalProperties": false,
"properties": {
"body": {
"properties": {
"raw_text": {
"description": "Unstructured text to extract data from \u2014 emails, resumes, articles, log entries, contracts, support tickets, etc.",
"type": "string"
},
"target_schema": {
"description": "A standard JSON Schema object with type, properties, and optionally required. Defines the shape of the extracted output.",
"type": "object"
}
},
"required": [
"raw_text",
"target_schema"
],
"type": "object"
},
"bodyType": {
"enum": [
"json",
"form-data",
"text"
],
"type": "string"
},
"method": {
"enum": [
"POST"
],
"type": "string"
},
"type": {
"const": "http",
"type": "string"
}
},
"required": [
"type",
"method",
"bodyType",
"body"
],
"type": "object"
},
"output": {
"properties": {
"example": {
"properties": {
"extracted": {
"description": "Structured data conforming to the caller's target_schema. Missing fields are omitted or null.",
"type": "object"
},
"tokens_used": {
"description": "LLM token consumption for this extraction",
"properties": {
"input": {
"description": "Prompt tokens consumed",
"type": "number"
},
"output": {
"description": "Completion tokens consumed",
"type": "number"
}
},
"type": "object"
}
},
"type": "object"
},
"type": {
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}
},
"required": [
"input"
],
"type": "object"
}