# Company Headcount Observations — String Normalisation + Category Frequencies

> Company Headcount Observations — String Normalisation + Category Frequencies 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 string fields and computes category frequency distributions for up to 200 company headcount observation records (headcount, open_roles).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-12-024?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-category-e7cf1ca7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_nyl1N4kTF5BBSYIsn9_js

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-category-e7cf1ca7 -d '<json body>'
```

Example prompt: Can you normalize the string fields and compute category frequencies for these company headcount records? I have 4 rows: row-1 for group-a on 2026-10-01 with headcount 10 and 1 open role, row-2 for group-b on 2026-10-02 with headcount 20 and 2 open roles, row-3 for group-a on 2026-10-03 with headcount 15 and 3 open roles, and row-4 for group-b on 2026-10-04 with headcount 30 and 4 open roles.

## When to prefer this

Choose this endpoint when you need both string normalisation and category frequency analysis on company headcount observations in a single low-cost call. It is ideal for preprocessing HR or workforce datasets before importing into dashboards, detecting inconsistent company_group labels, or auditing the distribution of headcount and open_roles records across groups. Not suitable for live lookups, predictions, or datasets exceeding 200 rows.

## Known failure modes

- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, company_group, date, headcount, or open_roles) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric fields (headcount, open_roles) out of allowed range — validation error
- Empty rows array — minimum 1 item required
- Payment of 0.01 USDC not fulfilled — 402 response

## How this service works

Company headcount observations — String normalisation + Category frequencies: String normalisation + Category frequencies 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 for the supplied rows: (1) a string normalisation report for the headcount and open_roles observation records (standardising company_group and other string fields), and (2) category frequency counts showing how often each distinct company_group value appears across the submitted rows. Results are computed locally 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/company-headcount-observations-string-normalisation-category-e7cf1ca7/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)