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Brand Mention Inventories — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Brand Mention Inventories — Record Quality Audit + String Normalisation + 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 2026-10-20).
Audits record quality, normalises strings, and flags IQR-based outliers for brand mention inventory data (mention_count, source_count) across up to 200 rows, returning 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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
Returns 3 independent analytical reports: (1) a record quality audit identifying missing, malformed, or invalid values; (2) a string normalisation report for company_group and other string fields; and (3) IQR-based outlier flags for the selected numeric field (mention_count or source_count). All analysis is performed locally with no external lookups.
Example use cases
Pre-analysis data quality check
“I have a dataset of brand mentions across different company groups for the past quarter — can you run a record quality audit, normalise the company_group strings, and flag any outliers in mention_count using IQR? I have about 150 rows with row_id, company_group, date, mention_count, and source_count.”
Outlier detection in source counts
“Before I report on brand coverage, can you check my brand mention inventory for IQR outliers specifically in the source_count field, normalise any inconsistent company names, and give me a full quality audit? I'll supply around 80 rows.”
Marketing data cleaning pipeline
“I'm cleaning up our brand mention tracking data before importing it — can you audit record quality, normalise the company_group strings, and flag IQR-based outliers in mention_count for the 200 rows I have?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined analytical operations — record quality audit, string normalisation, and IQR outlier detection — on brand mention inventory data in a single call. It is ideal for pre-analysis data cleaning pipelines where mention_count and source_count are the key metrics and you want consistent, reproducible local analytics without any external data lookups or predictions.
Side effects
- Input exceeds 200 rows — request rejected
- Missing required fields (row_id, company_group, date, mention_count, source_count) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (-1e12 to 1e12) — schema rejection
- Empty rows array — minimum 1 item required
- Invalid field enum value — must be mention_count or source_count
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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