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Travel Administration Data Quality Audit: Record Quality, String Normalisation & IQR Outlier Flags
x402
Travel Administration Data Quality Audit: Record Quality, 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 2026-10-26).
Audits up to 200 travel administration records (trip_count, recorded_cost) producing three reports: record quality audit, string normalisation, and IQR-based outlier detection.
Price / call$0.01
Success—
StatusUnknown
How this service works
Travel administration — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied travel administration records (trip_count, recorded_cost). 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 identifying missing values, invalid formats, or constraint violations per row; (2) a string normalisation report showing standardised versions of string fields like team_group; (3) an IQR outlier flags report identifying rows where the chosen numeric field (trip_count or recorded_cost) falls outside the interquartile range bounds. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Corporate travel expense anomaly detection
“I have 180 rows of corporate travel data with trip counts and recorded costs per team group. Can you run an IQR outlier check on recorded_cost and also do a full record quality audit and string normalisation on the team_group column?”
Monthly travel data cleansing before reporting
“Before I submit our monthly travel report, can you audit these 200 travel administration records for quality issues, normalise the team_group strings, and flag any outliers in trip_count using IQR? I need all three reports.”
Identifying bad data in multi-team travel dataset
“We've got travel records across about 12 team groups and I suspect there are some dodgy entries. Can you run a record quality audit, string normalisation, and IQR outlier detection on the trip_count field across these rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-operations-14-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 travel administration records and need all three of: data quality auditing, string normalisation, and IQR outlier detection in a single $0.01 call. It is ideal for pre-reporting data cleansing pipelines, travel expense validation workflows, or ad-hoc data hygiene checks on team-level trip and cost data. Prefer this over general-purpose data tools when your data specifically contains trip_count and recorded_cost fields organised by team_group and date.
Side effects
- Exceeding 200 rows returns an error — batch must be split
- Missing required fields (row_id, date, trip_count, recorded_cost) causes validation failure
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- Numeric values outside ±1,000,000,000,000 bounds rejected
- Empty rows array not accepted (minItems: 1)
- Invalid field enum value (must be 'trip_count' or 'recorded_cost') causes error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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