# Inventory Administration Data Quality Suite — Audit, Normalize & Deduplicate

> Inventory Administration Data Quality Suite — Audit, Normalize & Deduplicate is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).

Runs three local analytical reports on inventory session data: record quality audit, string normalization, and exact row deduplication for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-operations-21-191?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-25
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/inventory-administration-data-quality-suite-audit-normalize-deduplicate-67d0fe1e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_qyhZdFA2zDwl9MHF7ya7p

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability inventory-administration-data-quality-suite-audit-normalize-deduplicate-67d0fe1e -d '<json body>'
```

Example prompt: Can you audit the quality, normalize the strings, and remove exact duplicate rows in my inventory session data? I have records with row_id, team_group, date, count_sessions, and discrepancy_count fields for groups like 'group-a' and 'group-b' across dates in October 2026 — up to about 50 rows.

## When to prefer this

Choose this endpoint when you need three bundled data quality operations — audit, normalization, and deduplication — on inventory session records in a single low-cost call. Ideal for small-to-medium datasets (up to 200 rows) where you want offline, deterministic analysis without external dependencies. Best suited for inventory administration data specifically structured around team groups, session counts, and discrepancy counts.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, team_group, date, count_sessions, discrepancy_count) — validation error
- Invalid date format (non-YYYY-MM-DD) — schema validation failure
- Numeric fields outside allowed range (±1,000,000,000,000) — rejected
- Empty rows array — minimum 1 item required

## How this service works

Inventory administration — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication for supplied inventory administration records (count_sessions, discrepancy_count). 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 malformed, missing, or out-of-range values; (2) a string normalization report showing standardized versions of text fields like team_group; and (3) an exact row deduplication report identifying and removing fully duplicate rows. All analysis is performed locally with no external lookups or predictions.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "team_group",
     "date",
     "count_sessions",
     "discrepancy_count"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "team_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "count_sessions": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "discrepancy_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "team_group": "group-a",
     "count_sessions": 10,
     "discrepancy_count": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "team_group": "group-b",
     "count_sessions": 20,
     "discrepancy_count": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "team_group": "group-a",
     "count_sessions": 15,
     "discrepancy_count": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "team_group": "group-b",
     "count_sessions": 30,
     "discrepancy_count": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/inventory-administration-data-quality-suite-audit-normalize-deduplicate-67d0fe1e/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)