# Company Headcount Observations — String Normalisation + Date by Category Pivot

> Company Headcount Observations — String Normalisation + Date by Category Pivot 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 company name strings and pivots headcount/open_roles observations by date and category group, producing two analytical reports from supplied row data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-12-035?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-date-by-category-ac9ae795
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Js1bdQS-K_vpi5gpKiDhO

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-date-by-category-ac9ae795 -d '<json body>'
```

Example prompt: I have up to 200 rows of company headcount observations — each with a row_id, company_group, date, headcount, and open_roles — can you normalize the company group strings and produce a date-by-category pivot for the headcount field?

## When to prefer this

Choose this endpoint when you have up to 200 rows of company headcount observation records that need both company group string normalization and a date-by-category pivot in a single low-cost call. It is ideal for HR analytics workflows where raw export data has inconsistent company labels and you need a structured time-series pivot without any external data lookups or predictions.

## Known failure modes

- Rows exceeding 200 items will be rejected — trim input to 200 rows maximum
- Missing required fields (row_id, company_group, date, headcount, open_roles) cause validation errors
- Dates not matching YYYY-MM-DD pattern are rejected
- Null values in headcount or open_roles are accepted but may produce gaps in pivot output
- Invalid field enum value (anything other than 'headcount' or 'open_roles') causes a schema error

## How this service works

Company headcount observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot 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

Two analytical reports: (1) a string-normalised version of the company_group labels across all supplied rows, and (2) a pivot table of the selected field (headcount or open_roles) indexed by date and company_group category — all computed locally from the supplied data with no external lookups.

## 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
  },
  "field": {
   "enum": [
    "headcount",
    "open_roles"
   ],
   "type": "string",
   "default": "headcount"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/company-headcount-observations-string-normalisation-date-by-category-ac9ae795/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)