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Brand Mention Inventories — Record Quality Audit, String Normalisation & Category Frequencies
x402
Brand Mention Inventories — Record Quality Audit, String Normalisation & Category Frequencies 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 category frequency analysis, for up to 200 rows of mention_count/source_count data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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 structured analytical reports for the supplied rows: (1) a record quality audit flagging anomalies, missing values, and out-of-range fields; (2) a string normalisation report with cleaned and standardised company_group and other string fields; (3) a category frequency table showing counts and distributions by company_group and date categories. All computed locally with no external lookups.
Example use cases
Brand mention data quality check
“I have a dataset of brand mention inventory rows with company groups, dates, mention counts, and source counts — can you run a record quality audit on them and flag any bad or inconsistent records?”
Normalise company group strings
“My brand mention data has inconsistent company group names across rows — can you run the string normalisation report on these records so I get clean, standardised values?”
Summarise category frequencies by company group
“Can you generate a category frequency breakdown for my brand mention inventory? I want to see how often each company_group appears across my 150 rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need quick, offline data quality and normalisation analysis for brand mention inventory datasets of up to 200 rows, without requiring external API calls, live data lookups, or predictive modelling. It is ideal for batch data cleaning and auditing workflows where you want three complementary analytical perspectives (quality, normalisation, frequencies) in a single cheap call.
Side effects
- Submitting more than 200 rows will be rejected or truncated
- Missing required fields (row_id, company_group, date, mention_count, source_count) will cause row-level errors
- Dates not matching YYYY-MM-DD pattern will fail schema validation
- Numeric fields (mention_count, source_count) outside the allowed range will be rejected
- Empty rows array will fail minItems validation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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