Getting Started with Document Extraction

Document extraction is the process of converting unstructured documents (PDFs, images, scanned files) into structured, machine-readable data. With Smole, this process takes minutes instead of weeks.

Why Document Extraction Matters#

Every day, businesses process thousands of documents: invoices, receipts, contracts, forms, and reports. Traditionally, extracting data from these documents required either manual data entry (slow and error-prone) or building complex custom pipelines (expensive and time-consuming).

Modern AI-powered extraction changes this equation entirely.

How Smole Works#

Smole uses a three-step process:

1. Upload your document - Send any PDF, image, or scanned document to our API
2. Define your schema - Tell us what data you want to extract using a simple JSON schema
3. Get structured data - Receive clean, validated JSON that matches your schema

Your First Extraction#

Here's a simple example extracting data from an invoice:

{
  "schema": {
    "vendor_name": "string",
    "invoice_number": "string",
    "total_amount": "number",
    "line_items": [
      {
        "description": "string",
        "quantity": "number",
        "unit_price": "number"
      }
    ]
  }
}

The API will return structured data like:

{
  "vendor_name": "Acme Corp",
  "invoice_number": "INV-2024-001",
  "total_amount": 1250.00,
  "line_items": [
    {
      "description": "Widget Pro",
      "quantity": 5,
      "unit_price": 250.00
    }
  ]
}

Best Practices#

• Start simple - Begin with a few key fields, then expand your schema
• Use descriptive field names - The AI uses field names as hints for extraction
• Test with varied documents - Ensure your schema works across different document formats
• Validate your output - Always verify extracted data for critical workflows

Next Steps#

Ready to try it yourself? Head to the Playground to test document extraction with your own files, or check out our API documentation for integration details.