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Brand Mention Inventories — Record Quality Audit, String Normalisation & Weighted Mean
x402
Brand Mention Inventories — Record Quality Audit, String Normalisation & Weighted Mean 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).
Audits record quality, normalises strings, and computes secondary-measure weighted means for brand mention inventory data (mention_count, source_count) across up to 200 rows in three bundled analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean 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 analytical reports covering: (1) a record quality audit flagging issues such as nulls, out-of-range values, and duplicate row IDs; (2) string normalisation results for company_group and other string fields; and (3) a secondary-measure weighted mean of the selected numeric field (mention_count or source_count) weighted by the other measure. All computed locally from supplied data, no external lookups.
Example use cases
Monthly brand mention data cleanup
“I've got 80 rows of brand mention inventory data from last month with inconsistent company group names and some null values — can you run a record quality audit, normalise the company_group strings, and give me a weighted mean of mention_count using source_count as the weight?”
Pre-dashboard data quality check
“Before I load this brand mention data into our dashboard, can you check all 150 records for quality issues, normalise the company names, and compute a source-count weighted average of mention counts so I know the data is clean?”
Comparing brand visibility across groups
“I have mention data for three company groups over the past quarter — can you audit the records, normalise the group name strings so variants match, and calculate a weighted mean of mention_count weighted by source_count for the field 'mention_count'?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of brand mention inventory data and need three bundled offline analytical operations — record quality auditing, string normalisation, and weighted mean computation — in a single low-cost call ($0.01 USDC). It is ideal when you do not need live data lookup, predictions, or external enrichment, and want fast, deterministic local analysis of existing mention_count and source_count data.
Side effects
- Fewer than 1 row or more than 200 rows submitted — request rejected
- Row missing required fields (row_id, company_group, date, mention_count, source_count) — validation error
- date field does not match YYYY-MM-DD pattern — schema validation failure
- field enum value not 'mention_count' or 'source_count' — rejected
- All values for the selected numeric field are null — weighted mean cannot be computed
- Payment not included or insufficient USDC — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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