# Company Headcount Observations — String Normalisation + Two-Measure Correlation

> Company Headcount Observations — String Normalisation + Two-Measure Correlation 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 group strings and computes a two-measure Pearson/rank correlation between headcount and open_roles across up to 200 supplied company observation rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-12-033?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-two-measure-97dfb133
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ee-xCpLOiLMqhGEEAyvYJ

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-two-measure-97dfb133 -d '<json body>'
```

Example prompt: I have a list of up to 200 company headcount observations with company group names, dates, headcount numbers, and open roles — can you normalise the company group strings and tell me the correlation between headcount and open_roles across all those rows?

## When to prefer this

Choose this endpoint when you have tabular company headcount data in hand (up to 200 rows) and need both string normalisation on company group labels and a statistical correlation between headcount and open_roles in a single low-cost call. It is best suited for offline/batch data preparation workflows where no live lookup, prediction, or external enrichment is needed. Prefer it over general-purpose analytics tools when you want a deterministic, reproducible, cheap analytical pass on supplied data without sending it to a large external model.

## Known failure modes

- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — request rejected
- date field does not match YYYY-MM-DD pattern — validation error
- row_id missing or empty — validation error
- Both headcount and open_roles are null for all rows — correlation cannot be computed
- Malformed JSON body — parse error

## How this service works

Company headcount observations — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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 two analytical reports derived from the supplied rows: (1) a string normalisation report for the company_group field identifying and standardising variant forms, and (2) a two-measure correlation report quantifying the statistical relationship between headcount and open_roles across all submitted observations. No external data is fetched; analysis is performed entirely on the supplied 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-two-measure-97dfb133/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)