# Ai Label Leads Industry And Fit Score

**Use case:** 

Score a scraped lead list: industry, company size bucket and a 1 to 10 fit score against your ideal customer, written as new columns so you can sort and filter the list. Uses built-in example companies; point it at your Google Maps or LinkedIn scraper output.

## Input

```json
{
  "fileFormat": "auto",
  "data": [
    {
      "name": "Brightline Electrical Ltd",
      "website": "brightline-electrical.co.uk",
      "snippet": "Domestic and commercial electricians covering Kent and Medway. NICEIC approved, 12 staff."
    },
    {
      "name": "Pixel Forge Studios",
      "website": "pixelforge.io",
      "snippet": "Indie game studio, 5 people, building a co-op roguelike for Steam."
    },
    {
      "name": "Harbour Dental Group",
      "website": "harbourdental.com",
      "snippet": "Seven private dental practices across the south coast, 90 staff, new patients welcome."
    },
    {
      "name": "Northgate Logistics",
      "website": "northgatelogistics.com",
      "snippet": "Pallet distribution and same-day courier, 40 vehicles, national coverage."
    }
  ],
  "prompt": "Our ideal customer is a UK local service business with 5 to 50 staff that takes bookings from the public (trades, clinics, salons, cleaners). For this company, give the industry, a size bucket, and a fit score from 1 (poor fit) to 10 (ideal), with a one-sentence reason.\n\nCompany: {{name}}\nWebsite: {{website}}\nAbout: {{snippet}}",
  "outputFields": [
    {
      "name": "industry",
      "type": "string",
      "description": "two or three words"
    },
    {
      "name": "sizeBucket",
      "type": "string",
      "description": "exactly one of: solo, 2-10, 11-50, 51-200, 200+, unknown"
    },
    {
      "name": "fitScore",
      "type": "number",
      "description": "1 to 10"
    },
    {
      "name": "fitReason",
      "type": "string",
      "description": "one sentence"
    }
  ],
  "model": "anthropic/claude-haiku-4.5",
  "previewRows": 0,
  "maxRows": 1000,
  "rowsPerRequest": 5,
  "concurrency": 4,
  "maxInputCharsPerField": 4000,
  "maxOutputTokensPerRow": 150,
  "temperature": 0,
  "skipIfEmpty": true,
  "includeOriginalFields": true,
  "exportFormats": [
    "xlsx"
  ]
}
```

## Output

```json
{
  "id": {
    "label": "Id",
    "format": "number"
  },
  "product": {
    "label": "Product",
    "format": "text"
  },
  "review": {
    "label": "Review",
    "format": "text"
  },
  "sentiment": {
    "label": "Sentiment",
    "format": "text"
  },
  "topic": {
    "label": "Topic",
    "format": "text"
  },
  "aspects": {
    "label": "Aspects",
    "format": "array"
  },
  "aiModel": {
    "label": "Model",
    "format": "text"
  },
  "aiError": {
    "label": "Error",
    "format": "text"
  }
}
```

## About this Actor

This example demonstrates how to use [Dataset AI Enrich (LLM Classify, Extract, Summarise Rows)](https://apify.com/nerolabs/dataset-ai-enrich.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/nerolabs/dataset-ai-enrich.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
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This Task's input is already configured above — use it as-is rather than inventing a new one.

- **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).
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For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/nerolabs/dataset-ai-enrich.md

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).