Cross-Company Talent Flow & Competitor Poaching Analyzer

Build source-to-destination talent-flow intelligence between competing companies from recurring employee datasets. Returns structured, machine-readable results for repeatable research, monitoring, automation, Apify Tasks, API workflows, and AI agents. Returns structured.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "personId": "p1",
      "sourceCompany": "Beta",
      "destinationCompany": "Acme",
      "role": "Director AI",
      "seniorityWeight": 12
    },
    {
      "personId": "p2",
      "sourceCompany": "Beta",
      "destinationCompany": "Acme",
      "role": "Engineer",
      "seniorityWeight": 4
    },
    {
      "personId": "p3",
      "sourceCompany": "Gamma",
      "destinationCompany": "Beta",
      "role": "VP Sales",
      "seniorityWeight": 12
    }
  ],
  "previousItems": [
    {
      "personId": "p0",
      "sourceCompany": "Beta",
      "destinationCompany": "Acme",
      "role": "Engineer",
      "seniorityWeight": 4
    }
  ],
  "maxItems": 20000
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current records: Current source or normalized rows to analyze.
• currentDatasetId — Current Apify Dataset ID: Optional Apify Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional previous Apify Dataset ID used instead of previousItems.
• maxItems — Maximum records: Maximum records loaded from a Dataset input.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.015000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Build source-to-destination talent-flow intelligence between competing companies from recurring employee datasets.

Why use this Actor

Employee datasets show who works where, while competitive strategy needs the direction and concentration of talent moving from one rival to another. 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

• Explicit source-to-destination competitor flow network.

• Poaching-cluster detection rather than generic hires/departures.

• Seniority-weighted talent movement.

• Cross-company flow actions.

• 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

• competitive intelligence.
• executive recruiting.
• workforce strategy.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.015 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 normalized JSON rows or select a cloud Dataset with currentDatasetId. For change analysis, add previousItems or previousDatasetId. The Actor reads Datasets with limited READ permission. Keep identifiers stable across periods and normalize source fields where possible. The prefilled example is intentionally small so the first run can be validated before scaling.

Output

The default Dataset receives one decision-ready intelligence report with record counts, snapshot additions and removals, ranked entity signals, secondary-dimension breadth, a bounded signal score, and agentAction. The same report is stored as INTELLIGENCE_REPORT for downstream automation. Results are designed for Tasks, schedules, webhooks, dashboards, and agent workflows rather than as a replacement for source evidence.

Gen2 decision intelligence

This Actor preserves its original analysis and adds a decision layer with baseline awareness, regression detection, confidence, GO/WARN/BLOCK executive output, and an optional economic-impact estimate. Economic estimates are produced only when the user supplies valuePerImpactUnitUsd; the result states the calculation basis instead of inventing monetary value.