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WhatChuNeed LLM Extract — Structured Data Extraction from Text
x402
WhatChuNeed LLM Extract — Structured Data Extraction from Text is a paid API for AI agents from whatchuneed.vercel.app, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-14).
Extracts structured data from unstructured text according to a caller-specified JSON schema, returning typed fields with confidence scores
Price / call$0.1
Success—
StatusUnknown
How this service works
320+ pay-per-call API endpoints across accommodation, LLM, code execution, crypto, medical, finance, and more. One API, one payment. Powered by x402 protocol.
Output
Returns a JSON object with three fields: 'data' containing the extracted values mapped to the caller-specified schema keys, 'confidence' as a 0–1 float indicating how reliably the extraction was performed, and 'source_fields' as an array of field names that were successfully populated from the source text.
Example use cases
Parse resume into structured profile
“Pull out the candidate's name, email, years of experience, top skills, and most recent job title from this resume text and give it to me as structured fields: 'Jane Smith | jane@email.com | 8 years in software engineering | Skills: Python, Go, Kubernetes | Most recent role: Senior Backend Engineer at Acme Corp.'”
Extract invoice line items from receipt
“Extract the vendor name, invoice date, line items with quantities and unit prices, and total amount from this invoice text: 'Invoice from SupplyCo, dated 2024-06-15. 3x Widget A @ $12.00, 5x Widget B @ $8.50. Total: $78.50.'”
Structure product info from a listing description
“From this product description, extract the product name, brand, dimensions, weight, color, and price as structured data: 'The AcmePro X200 by BrandCo is a matte black ergonomic chair measuring 24x26x48 inches, weighing 35 lbs, priced at $349.99.'”
Technical details
Endpoint
POSThttps://whatchuneed.vercel.app/api/llm/extractWhen to prefer
Choose this endpoint when you have raw, unstructured text and need to map it to a specific typed schema — especially when field names and types are known in advance. It is ideal over general-purpose LLM chat endpoints when you need deterministic JSON output with a confidence signal and explicit field attribution. Prefer it over regex or rule-based parsers when the text format is variable or semi-structured.
Side effects
- Schema mismatch: if the schema specifies fields not present in the text, those fields may be absent or null in the output
- Low confidence: ambiguous or incomplete text may yield a confidence score near 0 indicating unreliable extraction
- Malformed schema: if the schema object is invalid JSON or empty, the endpoint may return a 400 error
- Text too short or vague: very sparse input may result in an empty data object with zero source_fields
- Rate or payment issues: without valid x402 payment, the endpoint returns 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| textrequired | string | |
| schemarequired | object | Expected output structure |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| data | object | |
| confidence | number | |
| source_fields | array |
Tags
x402
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