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Support Intent Exact Normalised Duplicate Audit
x402
Support Intent Exact Normalised Duplicate Audit is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-11).
Audits up to 200 labelled support-intent text records for exact normalised duplicates across billing, technical, account, and other categories.
Price / call$0.01
Success—
StatusUnknown
How this service works
Support intent - Exact normalised duplicate audit: Exact normalised duplicate audit for Support intent. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded exact normalised duplicate audit deliverable.
Output
A bounded duplicate audit deliverable identifying exact normalised duplicate records within the supplied dataset of up to 200 labelled support-intent text items, reporting which record IDs are duplicates and their associated intent categories.
Example use cases
Pre-training data deduplication check
“Before I train my support intent classifier, can you audit these 150 labelled records across billing, technical, account, and other categories and flag any exact normalised duplicates?”
Support dataset quality review
“I've collected 80 customer support tickets with labels — billing, technical, account, or other — can you run a duplicate audit to tell me which records are exact normalised matches I should remove?”
Labelling pipeline validation
“My labelling team just finished tagging 120 support messages. Can you check for exact normalised duplicates across all four categories — billing, technical, account, and other — before I lock in the dataset?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-support-intent-duplicates?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, local duplicate-detection audit on a small labelled support-intent dataset (up to 200 records) without invoking a hosted model or foundation-model pipeline. It is ideal for data quality checks before training naive-Bayes or similar lightweight classifiers on support ticket categories.
Side effects
- Fewer than 2 records supplied returns a validation error
- More than 200 records exceeds the maxItems limit and is rejected
- Records missing required fields (id, text, actual) cause schema validation failure
- Invalid 'actual' enum value outside billing/technical/account/other causes rejection
- Empty or whitespace-only text fields may produce degenerate duplicate results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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