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Account Research Coverage — Record Quality Audit, String Normalisation & Missing-Value Fill
x402
Account Research Coverage — Record Quality Audit, String Normalisation & Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Audits data quality, normalises strings, and fills missing values in account research coverage records (researched_accounts, target_accounts) for up to 200 rows, returning 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Account research coverage — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied account research coverage records (researched_accounts, target_accounts). 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 for the supplied rows: (1) a record quality audit flagging malformed, out-of-range, or inconsistent values; (2) a string normalisation report showing standardised company_group and other string fields; and (3) a missing-value fill report with imputed values for the selected field (researched_accounts or target_accounts) using the chosen method (zero, mean, or median). No external data sources or live lookups are used.
Example use cases
Pre-pipeline coverage data cleanup
“I have 50 rows of account research coverage data with company groups, dates, researched_accounts and target_accounts — some values are missing or the company names are inconsistently capitalised. Can you run a quality audit, normalise the strings, and fill any missing researched_accounts using median imputation?”
Weekly sales ops data health check
“Every Monday I want to validate our latest batch of target account coverage records — up to 150 rows with row_id, company_group, date, researched_accounts and target_accounts. Please run all three reports: quality audit, string normalisation, and fill missing target_accounts values using mean imputation.”
Spotting bad data before a board report
“Before I present our account research coverage to leadership, can you audit these 80 rows for data quality issues, normalise the company group names, and use zero-fill for any missing researched_accounts values so I can see exactly what's missing?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-08-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, offline, local-engine data quality pass on account research coverage records — specifically when you want all three of record quality auditing, string normalisation, and missing-value imputation in a single low-cost call. Ideal for pre-ingestion validation, weekly data health checks, or preparing CRM coverage data for reporting. Not appropriate when you need live data enrichment, external company lookups, predictive modelling, or processing more than 200 rows at once.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- date field does not match YYYY-MM-DD pattern — validation error
- row_id missing or empty — required field error
- numeric values outside allowed range (±1 trillion) — schema rejection
- invalid method enum value (not zero/mean/median) — parameter error
- invalid field enum value (not researched_accounts/target_accounts) — parameter error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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