TradepilotUSA Field Mapping Executor

x402

TradepilotUSA Field Mapping Executor 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).

Maps source text fields to a structured target schema using AI, extracting and transforming business data fields with validation and gap analysis.

Price / call$0.25

Seller volume →—

Success—

StatusUnknown

How this service works

Map supplied source fields to a target schema, specifying transformations, required validation, and unmapped fields. Uses buyer-supplied information only; no external research or outbound actions. Returns a draft for human review.

Output

Returns a JSON object containing the mapped field summary, a detailed artifact excerpt with extracted and transformed fields, a list of assumptions made during mapping, identified evidence gaps (missing critical data), a provenance record, a request ID, and a flag indicating whether human review is required.

Example use cases

CRM onboarding field normalization

“I have raw notes from a sales call with a new lead — their company name, services offered, location, and contact availability are all mixed into one paragraph. Can you map those fields to our CRM schema and tell me what critical information is still missing?”

Operations data extraction from meeting brief

“Here's a summary brief from our operations team meeting. Please extract all the relevant fields — crew roles, scheduling details, upcoming tasks — map them to our standard operations schema, and flag any gaps like missing budget or delay rate data.”

Invoice and order data field mapping

“I need to map fields from this supplier document — company details, service line items, delivery schedule, and payment terms — into our invoicing system's target schema. Can you do that and list any assumptions or evidence gaps you find?”

Technical details

Endpoint

POSThttps://www.tradepilotusa.com/api/agent-commerce/v1/services/field_mapping/execute?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to extract and organize structured fields from unstructured or semi-structured business text — such as sales notes, operational briefs, supplier documents, or meeting summaries — and map them to a defined target schema with gap identification. It is particularly useful when the source data is messy or incomplete and you need both the mapping result and an audit trail of assumptions and missing data, rather than a simple regex or rule-based extraction.

Side effects

Source text too short (below 20 characters) returns validation error
Brief field missing or below minimum length (10 characters) returns 400 error
Source text exceeds 32,000 character limit causing rejection
Ambiguous or highly unstructured source text may produce incomplete or low-confidence mappings
Missing critical fields in source result in evidence_gaps list requiring human follow-up
requires_human_review flag set to true when data is insufficient for full automated mapping

Input

Schema · Request body

Field	Type	Description
brief	string
audience	string
source_text	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "task": "field_mapping",
    "summary": "Mapped source fields from the fictional example to a target schema with specified transformations and validation rules. Identified missing critical data such as delay rates and project budget for foll",
    "artifact": "Illustrative excerpt from a synthetic provider check:\n## Business Draft: Mapping Source Fields to Target Schema\n\n### Source Fields Extracted\n- Company Name: Example Door Company\n- Service Offered: Residential entry door installation\n- Location: Dallas\n- Consultation Availability: Monday through Friday\n- Consultation Scheduling Owner: Operations Manager\n- Installation Crew Role: Confirms measurements before final quote\n- Upcoming Meeting Purpose: Review appointment delays\n- Missing Data: Current delay rates, final project budget\n- Action Item: Opera",
    "provenance": {
      "source": "buyer_supplied",
      "generated_at": "2026-09-22T14:42:44.436Z",
      "external_research_performed": false
    },
    "request_id": "example_field_mapping",
    "assumptions": [
      "The source text fields represent the complete set of relevant data for this mapping.",
      "Consultation availability is limited to weekdays as stated."
    ],
    "evidence_gaps": [
      "Quantitative data on current appointment delay rates.",
      "Final project budget figures."
    ],
    "requires_human_review": true
  }
}

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