{"uid":"cap_lX-pce-GBs-LsF4tKeE6p","slug":"business-directory-coverage-record-quality-audit-string-normalisation-f494fda9","name":"Business Directory Coverage — Record Quality Audit + String Normalisation + Keyed Dataset Comparison","description":"Business directory coverage — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied business directory coverage records (listed_entities, verified_entities). 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 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three analytical reports (record quality audit, string normalisation, and keyed dataset comparison) on up to 200 rows of business directory coverage data containing listed_entities and verified_entities fields.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and keyed dataset comparison on these two batches of business directory coverage data I have — one with our listed and verified entity counts from last month and one from this month, up to 200 rows each?","exampleUseCases":[{"title":"Pre-ingestion data quality check","prompt":"Before we load this month's business directory coverage export into our pipeline, can you run a quality audit on these 150 rows — each has a row_id, company_group, date, listed_entities count, and verified_entities count — and flag any malformed or missing values?"},{"title":"Normalising company group name strings","prompt":"I have a business directory dataset with inconsistent company_group name formatting across 120 rows — can you run the string normalisation report on it so we get clean, standardised group names before we join it to another table?"},{"title":"Spotting coverage drift between two snapshots","prompt":"I've got two versions of our business directory coverage data — last quarter and this quarter, both around 180 rows — can you run the keyed dataset comparison to show me which rows have changed in listed_entities or verified_entities between the two snapshots?"}],"resultDescription":"Three independent analytical reports returned for the supplied rows: (1) a record quality audit highlighting missing, null, or out-of-range values; (2) a string normalisation report standardising company_group and other string fields; and (3) a keyed dataset comparison report showing row-level differences between the primary and comparison datasets based on shared keys. All processing is local with no external lookups.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, company_group, date, listed_entities, verified_entities) on any row — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","listed_entities or verified_entities values outside allowed numeric range — schema rejection","Empty rows array (minItems: 1 not met) — request rejected"],"whenToPreferThis":"Choose this endpoint when you need offline, deterministic data quality analysis on business directory coverage records without any live lookup or external API call. It is ideal for pre-ingestion validation, string standardisation pipelines, and diff-style comparison between two snapshots of the same dataset. At $0.01 USDC for three reports on up to 200 rows it is cost-effective for batch auditing workflows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:48:57.029Z","isFirstParty":false,"canonicalSlug":"business-directory-coverage-record-quality-audit-string-normalisation-f494fda9"}