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Account Research Coverage — Record Quality Audit, String Normalisation & IQR Outlier Flags
x402
Account Research Coverage — 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 2026-10-15).
Audits record quality, normalises strings, and flags IQR-based outliers for account research coverage data (researched_accounts, target_accounts) across up to 200 rows, producing 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Account research coverage — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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
Three independent reports: (1) a record quality audit identifying missing, malformed, or incomplete fields; (2) a string normalisation report standardising company_group and other string fields; and (3) IQR-based outlier flags identifying statistically anomalous values in the selected numeric field (researched_accounts or target_accounts), all computed locally without any external lookups.
Example use cases
Sales pipeline coverage data quality check
“I have a spreadsheet of researched vs target accounts per company group over the last month — can you run a full quality audit on it, normalise the company group names, and flag any rows where the researched_accounts numbers look like statistical outliers?”
CRM account data cleanup before reporting
“Before I send my quarterly account coverage report, can you check these 150 rows of researched_accounts and target_accounts data for record quality issues and string inconsistencies in the company_group field, and highlight anything that looks out of range using IQR outlier detection?”
Anomaly detection in account research metrics
“Something looks off in our target_accounts numbers this week — can you run an IQR outlier analysis on the target_accounts field across these records grouped by company group and date, and also give me the record quality and string normalisation reports while you're at it?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-08-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a low-cost, self-contained, three-in-one data quality pass on account research coverage records — specifically combining record auditing, string normalisation, and IQR outlier detection in a single call. Ideal for sales ops, RevOps, or BI teams preparing account coverage data for reporting or CRM ingestion. Prefer this over general-purpose data tools when your data involves researched_accounts and target_accounts fields grouped by company and date, and you want no external data lookups or predictions — just local analytical computation.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, company_group) causes row-level rejection
- Invalid date format (non-YYYY-MM-DD) triggers schema validation failure
- Numeric values outside ±1,000,000,000,000 boundary are rejected
- Empty rows array returns an error
- Unsupported field enum value causes request failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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