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Account Research Coverage Analyzer — Record Quality Audit, String Normalisation & Keyed Dataset Comparison
x402
Account Research Coverage Analyzer — Record Quality Audit, String Normalisation & Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-21).
Runs three local analytical reports (record quality audit, string normalisation, and keyed dataset comparison) on up to 200 rows of account research coverage data containing researched_accounts and target_accounts fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Account research coverage — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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 analytical reports delivered together: (1) a record quality audit flagging nulls, out-of-range values, and structural issues; (2) a string normalisation report standardising company_group and other string fields; (3) a keyed dataset comparison report showing row-level differences between the two supplied datasets. No external lookups or predictions are made — all analysis is performed locally on the supplied data.
Example use cases
Sales ops data quality check
“I have two CSVs of account research coverage data — one is our primary dataset and one is last month's — each with up to 150 rows containing row_id, company_group, date, researched_accounts, and target_accounts. Can you audit the record quality, normalise the company group strings, and compare them by key to find any discrepancies?”
Pre-pipeline CRM data validation
“Before we push this batch of account research records into our CRM, can you run a quality audit and string normalisation pass on these 80 rows of researched_accounts and target_accounts data grouped by company_group? Also compare them against our baseline dataset to flag any mismatches.”
Account coverage parity report across groups
“We have two sets of account coverage records — group-a and group-b — each with about 60 rows dated through Q4 2026, with researched_accounts and target_accounts counts. Can you run all three reports: record quality audit, string normalisation, and keyed comparison, and tell me where the two datasets diverge?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-08-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of account research coverage data in a structured tabular format and need all three analytical passes — quality audit, string normalisation, and keyed comparison — bundled in a single low-cost call ($0.01 USDC). It is ideal for sales ops, RevOps, or data engineering teams that want a quick pre-processing validation step before loading account research data into a CRM or data warehouse. Prefer this over building custom scripts when you need a repeatable, schema-validated audit without any external API dependency or live data lookup.
Side effects
- Exceeding 200 rows in either the rows or other array returns a validation error
- Missing required fields (row_id, company_group, date, researched_accounts, target_accounts) per row causes rejection
- Dates not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array (minItems: 1 not met) returns an error
- Malformed JSON body causes a 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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