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Customer Segment Observations — Record Quality Audit + String Normalisation + Daily Sum Series
x402
Customer Segment Observations — 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 2026-10-20).
Runs three local analytical reports (record quality audit, string normalisation, and daily sum series) on up to 200 rows of customer segment observation data containing account_count and revenue_amount fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Customer segment observations — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum series for supplied customer segment observations records (account_count, revenue_amount). 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: (1) a record quality audit flagging malformed, missing, or out-of-range values; (2) a string normalisation report standardising company_group text fields; (3) a daily sum series aggregating the selected numeric field (account_count or revenue_amount) by date. All computed locally from supplied data with no external lookups.
Example use cases
Pre-reporting data quality check
“Before I send our monthly segment report, can you run a record quality audit, string normalisation, and daily sum series on account_count for these 150 customer segment rows I'm uploading? I need to catch any bad records and see the day-by-day totals.”
Revenue trend aggregation by day
“I have about 80 rows of customer segment observations with company_group and revenue_amount. Can you run the daily sum series on revenue_amount plus a quality audit and string normalisation on the company group names?”
Cleaning inconsistent segment labels
“Our company_group strings are a mess — mixed casing, trailing spaces, inconsistencies. Can you run string normalisation plus a quality audit and daily sum series on account_count for these 60 segment observation records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-16-198?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled local analytical operations — quality audit, string normalisation, and daily sum series — on customer segment observation data (account_count, revenue_amount) in a single low-cost call. Ideal when you want to clean, validate, and summarise up to 200 segment rows without any external API calls or live data lookups, and when cost efficiency (0.01 USDC for all three reports) is important.
Side effects
- Rows array empty or missing — validation error
- More than 200 rows submitted — rejected with maxItems error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Numeric values outside ±1,000,000,000,000 range — rejected
- Invalid field selector (not account_count or revenue_amount) — enum validation error
- Missing required fields (row_id, company_group, date) — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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