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TradepilotUSA Structured Extraction
x402
TradepilotUSA Structured Extraction is a paid API for AI agents from www.tradepilotusa.com, paid per call via x402, $0.25/call, status unknown (last checked 2026-10-02).
Extracts structured, organized information from unstructured source text based on a provided brief and target audience.
Price / call$0.25
Success—
StatusUnknown
How this service works
Extract requested fields from supplied text into a clearly structured artifact; use null for missing data. Uses buyer-supplied information only; no external research or outbound actions. Returns a draft for human review.
Output
Returns a JSON object containing a structured summary of the source text, a markdown-formatted artifact with organized sections, a list of assumptions made during extraction, identified evidence gaps, provenance metadata (source, timestamp, whether external research was used), a request ID, and a flag indicating whether human review is recommended.
Example use cases
Vendor capability assessment from raw notes
“I've got a wall of text from a potential vendor describing their logistics service — can you pull out the structured details like service area, contact info, scheduling process, and any gaps or assumptions, formatted for our operations team to review?”
Customer onboarding document parsing
“Here's a messy intake form submission from a new customer — can you extract the structured business details like company name, service needs, availability, and open questions so I can turn it into a clean onboarding summary for our account manager?”
Sales call notes to structured CRM entry
“I have raw notes from a sales call about a residential installation company — extract the key structured facts like what they offer, their service area, who handles scheduling, and any information gaps I still need to follow up on, written for my sales team.”
Technical details
Endpoint
POSThttps://www.tradepilotusa.com/api/agent-commerce/v1/services/structured_extraction/execute?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to take long-form, unstructured business text and extract it into organized, structured sections with explicit assumptions and evidence gaps identified. It is particularly suited for business operations contexts — vendor documents, sales notes, onboarding submissions, or operational briefs — where the output needs to be audience-targeted and audit-ready with provenance tracking. Prefer this over generic NLP APIs when you need a structured artifact with explicit gap analysis rather than just a summary or classification.
Side effects
- source_text too short (below 20 characters) returns validation error
- brief too short (below 10 characters) returns validation error
- source_text exceeds 32,000 character limit returns 400 error
- brief exceeds 12,000 character limit returns 400 error
- ambiguous or low-context source_text may produce low-confidence output with many evidence_gaps
- payment failure via x402 returns 402 status with no extraction performed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| brief | string | |
| audience | string | |
| source_text | string |
Output
Schema · Response
{
"type": "json",
"example": {
"task": "structured_extraction",
"summary": "Example Door Company provides residential entry door installation in Dallas with consultations available Monday through Friday. The operations manager handles scheduling, and the installation crew con",
"artifact": "Illustrative excerpt from a synthetic provider check:\n## Business Draft: Residential Entry Door Installation Service\n\n### Company Overview\n- **Company Name:** Example Door Company\n- **Service Provided:** Residential entry door installation\n- **Service Area:** Dallas\n\n### Consultation Details\n- **Availability:** Monday through Friday\n- **Scheduling Owner:** Operations Manager\n\n### Installation Process\n- **Measurement Confirmation:** Installation crew confirms measurements before providing a final quote\n\n### Current Operational Notes\n- **Upcoming Mee",
"provenance": {
"source": "buyer_supplied",
"generated_at": "2026-09-22T14:42:29.861Z",
"external_research_performed": false
},
"request_id": "example_structured_extraction",
"assumptions": [
"The operations manager has full authority over consultation scheduling.",
"Measurement confirmation by the installation crew is a standard step before quoting."
],
"evidence_gaps": [
"Specific current delay rates for appointments.",
"Final project budget details."
],
"requires_human_review": true
}
}Tags
x402
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