# Contractor-to-Employee Conversion Signal Intelligence (`quanmatrix/contractor-employee-conversion-intelligence`) Actor

Turn job-posting snapshots into company and role signals that suggest contractor demand is shifting toward permanent employment.

- **URL**: https://apify.com/quanmatrix/contractor-employee-conversion-intelligence.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** Jobs, Business, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $8.40 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Contractor-to-Employee Conversion Signal Intelligence

Turn job-posting snapshots into company and role signals that suggest contractor demand is shifting toward permanent employment.

### Why use this Actor

Recruiting and workforce teams need to detect when employers shift the same roles from contract to permanent hiring. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

### Key features

- Employment-type transition intelligence instead of job rows.

- Company and role-level snapshot matching.

- Permanent-vs-contract demand shift scoring.

- Recurring workforce strategy actions.

- Reusable Dataset-based workflow.

- Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

- Writes one auditable report to the default Dataset and `INTELLIGENCE_REPORT`.

### Example

Use the prefilled example or replace `currentItems` with rows from an upstream Actor. On recurring runs, provide the prior period in `previousItems` or `previousDatasetId`. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits `agentAction`.

### Use cases

- Recruiting agencies.
- Workforce planning.
- Staffing firms.
- Sales intelligence.
- AI agents.

### Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.012 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

### Limitations

- Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
- Scores are decision-support signals, not predictions or guarantees.
- Keep stable identifiers across snapshots for best change detection.
- Zero-direct-competition status is rechecked before publication because the Store changes continuously.

### Workflow

`upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction`.

### Input

Provide `currentItems` as JSON records or point `currentDatasetId` to an Apify Dataset. For recurring comparisons, provide `previousItems` or `previousDatasetId`. Keep stable entity identifiers across snapshots whenever possible. `maxItems` limits Dataset reads. Optional Gen2 fields can compare the current decision metric with a prior analysis and estimate economic impact only when the user explicitly supplies an impact value.

### Output

The default Dataset receives one structured intelligence report with record counts, new and removed records, ranked entity signals, decision confidence, regression status, executive decision, recommended action, and transparent economic-impact fields when enabled. The same report is stored in `INTELLIGENCE_REPORT` for downstream automations and agent workflows.

# Actor input Schema

## `currentItems` (type: `array`):

Current source or normalized rows to analyze.

## `currentDatasetId` (type: `string`):

Optional Apify Dataset ID used when currentItems is not supplied.

## `previousItems` (type: `array`):

Optional previous snapshot rows for period-over-period comparison.

## `previousDatasetId` (type: `string`):

Optional previous Apify Dataset ID used instead of previousItems.

## `maxItems` (type: `integer`):

Maximum records loaded from a Dataset input.

## `previousAnalysis` (type: `object`):

Optional prior Gen2 output used to calculate decision-metric deltas and regression.

## `valuePerImpactUnitUsd` (type: `number`):

Optional user-supplied economic value per impact unit. Leave empty to avoid monetary estimation.

## `monthlyRuns` (type: `integer`):

Optional expected monthly run count used only with valuePerImpactUnitUsd for economic impact estimation.

## Actor input object example

```json
{
  "currentItems": [
    {
      "jobId": "J1",
      "company": "Acme",
      "role": "Data Engineer",
      "employmentType": "Permanent",
      "conversionWeight": 1,
      "date": "2026-09-10"
    },
    {
      "jobId": "J2",
      "company": "Acme",
      "role": "Data Engineer",
      "employmentType": "Permanent",
      "conversionWeight": 1,
      "date": "2026-09-10"
    },
    {
      "jobId": "J3",
      "company": "Beta",
      "role": "Designer",
      "employmentType": "Contract",
      "conversionWeight": 0,
      "date": "2026-09-10"
    }
  ],
  "previousItems": [
    {
      "jobId": "P1",
      "company": "Acme",
      "role": "Data Engineer",
      "employmentType": "Contract",
      "conversionWeight": 0,
      "date": "2026-08-01"
    },
    {
      "jobId": "P2",
      "company": "Beta",
      "role": "Designer",
      "employmentType": "Contract",
      "conversionWeight": 0,
      "date": "2026-08-01"
    }
  ],
  "maxItems": 20000,
  "monthlyRuns": 1
}
```

# Actor output Schema

## `results` (type: `string`):

Decision-ready intelligence report.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "currentItems": [
        {
            "jobId": "J1",
            "company": "Acme",
            "role": "Data Engineer",
            "employmentType": "Permanent",
            "conversionWeight": 1,
            "date": "2026-09-10"
        },
        {
            "jobId": "J2",
            "company": "Acme",
            "role": "Data Engineer",
            "employmentType": "Permanent",
            "conversionWeight": 1,
            "date": "2026-09-10"
        },
        {
            "jobId": "J3",
            "company": "Beta",
            "role": "Designer",
            "employmentType": "Contract",
            "conversionWeight": 0,
            "date": "2026-09-10"
        }
    ],
    "previousItems": [
        {
            "jobId": "P1",
            "company": "Acme",
            "role": "Data Engineer",
            "employmentType": "Contract",
            "conversionWeight": 0,
            "date": "2026-08-01"
        },
        {
            "jobId": "P2",
            "company": "Beta",
            "role": "Designer",
            "employmentType": "Contract",
            "conversionWeight": 0,
            "date": "2026-08-01"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/contractor-employee-conversion-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "currentItems": [
        {
            "jobId": "J1",
            "company": "Acme",
            "role": "Data Engineer",
            "employmentType": "Permanent",
            "conversionWeight": 1,
            "date": "2026-09-10",
        },
        {
            "jobId": "J2",
            "company": "Acme",
            "role": "Data Engineer",
            "employmentType": "Permanent",
            "conversionWeight": 1,
            "date": "2026-09-10",
        },
        {
            "jobId": "J3",
            "company": "Beta",
            "role": "Designer",
            "employmentType": "Contract",
            "conversionWeight": 0,
            "date": "2026-09-10",
        },
    ],
    "previousItems": [
        {
            "jobId": "P1",
            "company": "Acme",
            "role": "Data Engineer",
            "employmentType": "Contract",
            "conversionWeight": 0,
            "date": "2026-08-01",
        },
        {
            "jobId": "P2",
            "company": "Beta",
            "role": "Designer",
            "employmentType": "Contract",
            "conversionWeight": 0,
            "date": "2026-08-01",
        },
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/contractor-employee-conversion-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "currentItems": [
    {
      "jobId": "J1",
      "company": "Acme",
      "role": "Data Engineer",
      "employmentType": "Permanent",
      "conversionWeight": 1,
      "date": "2026-09-10"
    },
    {
      "jobId": "J2",
      "company": "Acme",
      "role": "Data Engineer",
      "employmentType": "Permanent",
      "conversionWeight": 1,
      "date": "2026-09-10"
    },
    {
      "jobId": "J3",
      "company": "Beta",
      "role": "Designer",
      "employmentType": "Contract",
      "conversionWeight": 0,
      "date": "2026-09-10"
    }
  ],
  "previousItems": [
    {
      "jobId": "P1",
      "company": "Acme",
      "role": "Data Engineer",
      "employmentType": "Contract",
      "conversionWeight": 0,
      "date": "2026-08-01"
    },
    {
      "jobId": "P2",
      "company": "Beta",
      "role": "Designer",
      "employmentType": "Contract",
      "conversionWeight": 0,
      "date": "2026-08-01"
    }
  ]
}' |
apify call quanmatrix/contractor-employee-conversion-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/contractor-employee-conversion-intelligence"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/wZQX61Wol9Lj1VCG1/builds/vNeneXUOVGHqLa7rC/openapi.json