# Company Headcount Observations — String Normalisation & Exact Row Deduplication

> Company Headcount Observations — String Normalisation & Exact Row Deduplication is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Normalises strings and deduplicates exact rows in company headcount observation records (headcount, open_roles fields) for up to 200 rows, returning 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-12-020?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/company-headcount-observations-string-normalisation-exact-row-36ca8411
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_vGcixS8DJ3ZfY0EFYIpdf

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 company-headcount-observations-string-normalisation-exact-row-36ca8411 -d '<json body>'
```

Example prompt: Can you clean up these company headcount records — normalise the string fields and remove any exact duplicate rows? I have about 50 rows with company_group, date, headcount, and open_roles columns that need tidying before I run my analysis.

## When to prefer this

Choose this endpoint when you have up to 200 company headcount observation rows that need both string normalisation and exact duplicate removal in a single lightweight call, without requiring live data lookups, external enrichment, or ML-based predictions. It is ideal for pre-processing headcount and open_roles data before loading into analytics pipelines or dashboards.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, company_group, date, headcount, open_roles) cause row-level validation errors
- Invalid date format (must be YYYY-MM-DD) causes parsing failure
- Malformed JSON body returns 400 error
- Payment not included or insufficient USDC triggers 402 payment required
- headcount or open_roles values outside allowed numeric range rejected

## How this service works

Company headcount observations — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied company headcount observations records (headcount, open_roles). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Returns 2 independent analytical reports covering: (1) string normalisation results showing standardised company_group and other text fields, and (2) exact row deduplication results identifying and removing duplicate records — all processed locally without any external lookups or predictions, for up to 200 input rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/company-headcount-observations-string-normalisation-exact-row-36ca8411/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)