How to Automate Data Entry from Documents

Manual data entry is one of the most common productivity drains in business. Someone receives a document (an invoice, a form, a report), opens it on one screen, and types the data into a system on another screen. It's slow, error-prone, and doesn't scale.

The math is simple: if you process 100 documents per week at 15 minutes each, that's 25 hours of manual work every week. Over a year, that's 1,300 hours, most of which can be automated.

Where Manual Data Entry Happens#

Almost every department has document-to-system workflows:

• Accounts Payable: Typing invoice data into accounting software
• HR: Entering employee details from onboarding forms
• Sales: Copying data from contracts into CRM
• Operations: Transferring data from reports into dashboards
• Compliance: Logging details from regulatory documents

The documents are different, but the pattern is the same: read a document, type the data somewhere else.

How Document Extraction Replaces Data Entry#

Instead of a person reading and typing, an API reads the document and returns structured data:

Before: Document → Person reads → Person types → System
 
After:  Document → API extracts → System

The API step takes seconds instead of minutes, doesn't make typos, and works around the clock.

How It Works#

1. Define what data you need: Create a JSON schema describing the fields to extract
2. Send the document: Upload via API (PDF, image, Word doc, spreadsheet)
3. Get structured JSON: Receive typed, validated data ready for your systems

Example: Automating Invoice Entry#

Before: An AP clerk opens each invoice PDF, finds the vendor name, invoice number, line items, and total, then types them into the accounting system. 15-20 minutes per invoice.

After: The invoice is uploaded to the API, which returns:

{
  "vendor": "Office Supplies GmbH",
  "invoice_number": "INV-2025-0942",
  "date": "2025-12-01",
  "line_items": [
    { "description": "A4 Paper (Case)", "quantity": 10, "unit_price": 24.99, "total": 249.90 },
    { "description": "Toner Cartridge", "quantity": 4, "unit_price": 45.00, "total": 180.00 }
  ],
  "subtotal": 429.90,
  "tax": 81.68,
  "total": 511.58
}

This data feeds directly into the accounting system via its API. Total time: seconds.

Example: Automating HR Onboarding#

Before: HR receives employee forms (sometimes paper, sometimes PDF), and manually enters name, address, bank details, emergency contacts, and tax information into the HRIS.

After:

{
  "type": "object",
  "properties": {
    "employee": {
      "type": "object",
      "properties": {
        "full_name": { "type": "string" },
        "date_of_birth": { "type": "string", "format": "date" },
        "address": { "type": "string" },
        "email": { "type": "string", "format": "email" },
        "phone": { "type": "string" }
      }
    },
    "employment": {
      "type": "object",
      "properties": {
        "position": { "type": "string" },
        "department": { "type": "string" },
        "start_date": { "type": "string", "format": "date" },
        "salary": { "type": "number" }
      }
    },
    "bank_details": {
      "type": "object",
      "properties": {
        "bank_name": { "type": "string" },
        "iban": { "type": "string" },
        "bic": { "type": "string" }
      }
    },
    "emergency_contact": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "relationship": { "type": "string" },
        "phone": { "type": "string" }
      }
    }
  }
}

Scan the form, upload it, and the structured data flows into the HRIS automatically.

Calculating ROI#

For a team processing 500 documents per month at an average of 15 minutes each:

• Manual: 125 hours/month = ~0.75 FTE
• Automated: Near zero ongoing effort after setup
• Annual savings: 1,500 hours and €15,000-50,000 in labor costs

Getting Started#

Step 1: Identify Your Highest-Volume Document#

Start with the document type you process most frequently. Common starting points:

• Vendor invoices (accounts payable)
• Customer order forms (sales)
• Expense receipts (finance)
• Application forms (HR)

Step 2: Define Your Schema#

List the fields you currently type manually. These become your JSON schema:

{
  "type": "object",
  "properties": {
    "field_you_type_1": { "type": "string" },
    "field_you_type_2": { "type": "number" },
    "repeating_rows": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "column_1": { "type": "string" },
          "column_2": { "type": "number" }
        }
      }
    }
  }
}

Step 3: Test with Real Documents#

Upload a few real documents in the Playground and verify the extraction matches what you'd type manually.

Step 4: Connect to Your System#

Use the extracted JSON to populate your target system (accounting software, CRM, HRIS, ERP, or database) via its API or import function.

Step 5: Scale Up#

Once the first document type is automated, repeat for the next. Each additional document type is just a new schema. The infrastructure stays the same.

Common Questions#

What about documents with poor quality? OCR handles scanned documents and photos. Quality affects accuracy, but even phone photos of receipts produce reliable results.

What if the document format changes? Schema-based extraction adapts to layout changes. Unlike template-based parsers that break when a field moves, the extraction understands content contextually.

What about handwritten documents? Printed text on handwritten forms (like checkboxes, printed fields) is extracted reliably. Fully handwritten documents are more challenging but improving.

Try It Now#

Upload a document you currently process manually in the Playground. Define a schema matching the fields you type, and see how closely the extraction matches your manual work.

For API integration, see the documentation.