Extractors

What is an Extractor?

An extractor is a reusable configuration that defines:

1. Schema - What fields to extract and their types
2. Model Tier - Which AI model to use (Mini, Pro, or Max)
3. Validation Rules - Optional constraints on extracted values

Think of an extractor as a template that tells Automat exactly what information to pull from your documents.

Creating an Extractor

Via Dashboard

1. Navigate to your project in the Automat Dashboard
2. Click Create Extractor
3. Name your extractor (e.g., “Invoice Extractor”)
4. Define your schema using the visual editor or JSON
5. Test with sample documents
6. Publish when ready

Schema Definition

Schemas define the structure of extracted data:

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{
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  "invoice_number": {
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    "type": "string",
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    "description": "The unique invoice identifier"
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  },
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  "date": {
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    "type": "string",
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    "description": "Invoice date in YYYY-MM-DD format"
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  },
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  "total_amount": {
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    "type": "number",
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    "description": "Total amount due"
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  },
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  "line_items": {
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    "type": "array",
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    "items": {
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      "type": "object",
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      "properties": {
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        "description": { "type": "string" },
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        "quantity": { "type": "number" },
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        "unit_price": { "type": "number" }
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      }
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    }
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  }
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}

Supported Field Types

Model Tiers

Choose the right model tier for your use case:

Using Extractors

Get Extractor ID

Every extractor has a unique ID (e.g., ext_abc123). Find it in:

• The extractor detail page in your dashboard
• The URL when viewing an extractor

Make Extraction Requests

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const result = await client.extract({
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  extractorId: 'ext_abc123', // Your extractor ID
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  file: document,
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});

Versioning

Extractors support versioning for production stability:

• Draft - Work in progress, can be modified freely
• Published - Locked version for production use
• Multiple published versions can exist simultaneously

Best Practices

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{
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  "effective_date": {
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    "type": "string",
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    "description": "The date the contract becomes effective, not the signing date"
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  }
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}

Next Steps