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Brand Mention Inventories — Record Quality Audit + String Normalisation + Adjacent Row Changes
x402
Brand Mention Inventories — Record Quality Audit + String Normalisation + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Runs three local analytical reports on brand mention inventory rows: record quality audit, string normalisation, and adjacent row change detection for mention_count or source_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied brand mention inventories records (mention_count, source_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports for the submitted rows: (1) a record quality audit flagging data issues per row, (2) a string normalisation report highlighting inconsistencies in text fields like company_group, and (3) an adjacent row changes report showing value shifts between consecutive rows for the selected numeric field (mention_count or source_count). All computed locally with no external lookups.
Example use cases
Brand mention data quality check
“I have a CSV of brand mention records with columns row_id, company_group, date, mention_count, and source_count — can you run the record quality audit, string normalisation, and adjacent row changes reports on all 150 rows using mention_count as the target field?”
Detecting source count anomalies between rows
“Please analyse my brand mention inventory dataset of 80 rows and run all three reports focusing on source_count — I want to see the quality audit, any string normalisation issues in company_group, and where source counts jump between adjacent rows.”
Cleaning company group labels in mention data
“My brand mention inventory has inconsistent company_group strings across 60 rows — can you run the string normalisation report along with the quality audit and adjacent row changes for mention_count so I can see all the issues at once?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have brand mention inventory data (with mention_count and source_count) and need three simultaneous local analytical reports — quality audit, string normalisation, and adjacent row change detection — without any external data lookups, predictions, or live enrichment. Ideal for data cleaning and QA pipelines on batches up to 200 rows at very low cost ($0.01 for all three reports).
Side effects
- Missing required fields (row_id, company_group, date, mention_count, source_count) cause validation errors
- Submitting more than 200 rows exceeds the row limit
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- Numeric values outside the allowed range cause field errors
- Invalid field enum value (not mention_count or source_count) rejected
- Payment failure via x402 protocol returns 402 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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