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Brand Mention Inventories — Record Quality Audit + String Normalisation + Daily Sum Series
x402
Brand Mention Inventories — Record Quality Audit + String Normalisation + Daily Sum Series 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 analytical reports (record quality audit, string normalisation, and daily sum series) on supplied brand mention inventory data (mention_count, source_count) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum series 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 delivered together: (1) a record quality audit flagging issues in the supplied rows, (2) a string normalisation report cleaning and standardising company_group values, and (3) a daily sum series aggregating the selected field (mention_count or source_count) by date across all supplied rows. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Pre-analysis data quality check
“I have a batch of brand mention records from last quarter — row IDs, company groups, dates, and mention counts — and before I do any analysis I want to audit them for quality issues, normalise the company group strings, and see the daily mention_count sums. Can you run all three reports on my 120 rows?”
Monitoring source count daily trends
“Can you take my brand mention inventory data for October — about 80 rows with source_count and mention_count — and run the record quality audit, string normalisation, and daily sum series reports focused on source_count so I can see how many distinct sources we're being mentioned in each day?”
Cleaning messy company group labels
“My brand mention dataset has inconsistent company group names that need normalising, and I also want a quality audit and daily aggregation on mention_count. There are 150 rows with dates, row IDs, and both count fields — can you run all three reports on them?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-198?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 brand mention inventory rows and need three bundled analytical reports — quality audit, string normalisation, and daily aggregation — in a single cost-efficient call ($0.01 USDC). It is ideal for pre-analysis data cleaning and validation workflows where no live data lookup, external enrichment, or prediction is needed. Prefer it over single-report alternatives when all three report types are required simultaneously.
Side effects
- Exceeding 200 rows causes rejection — keep input to maxItems limit
- Missing required fields (row_id, company_group, date, mention_count, source_count) causes validation error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Invalid field enum value (not mention_count or source_count) causes error
- Empty rows array (minItems:1 not met) causes rejection
- Numeric values outside ±1,000,000,000,000 range cause validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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