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Brand Mention Inventories — Record Quality Audit + IQR Outlier Flags
x402
Brand Mention Inventories — Record Quality Audit + IQR Outlier Flags 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 and flags IQR-based outliers in brand mention inventory data (mention_count and source_count) for up to 200 rows, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + IQR outlier flags: Record quality audit + IQR outlier flags for supplied brand mention inventories records (mention_count, source_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) a record quality audit identifying data issues such as missing values, malformed dates, or out-of-range entries across the supplied rows, and (2) IQR-based outlier flags identifying which rows have statistically anomalous mention_count or source_count values, all computed locally without external lookups.
Example use cases
Weekly brand monitoring data quality check
“Run a quality audit and IQR outlier check on my weekly brand mention data — I have 50 rows with row_id, company_group, date, mention_count, and source_count, and I want to know which records have quality issues or anomalous mention_count values.”
Competitive brand mention anomaly detection
“I'm comparing brand mentions across three company groups over the past month and I need you to flag any rows where the source_count looks like an IQR outlier — can you audit this set of 120 brand mention inventory records and give me two reports?”
Pre-analysis data cleansing for brand mentions
“Before I run my analysis, can you check these 80 brand mention inventory rows for record quality problems and flag outliers in both mention_count and source_count using IQR? I want to know which rows to drop or investigate further.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-014?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have brand mention inventory data (up to 200 rows) and need both a record quality audit and IQR outlier detection in a single call for a flat $0.01 USDC fee. It is ideal for pre-processing pipelines where you want to identify bad or anomalous records before downstream analysis, and when you need two distinct reports (quality + outliers) without setting up a local analytics environment.
Side effects
- Fewer than 1 or more than 200 rows causes validation failure
- Missing required fields (row_id, company_group, date, mention_count, source_count) causes rejection
- Invalid date format (not YYYY-MM-DD) triggers schema error
- field enum value not 'mention_count' or 'source_count' causes error
- Null values in numeric fields may limit IQR computation
- Payment of 0.01 USDC not completed blocks execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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