Complete Invoice Processing API Guide - Automate in 15 Minutes

The Invoice Processing Problem

If you've ever tried to automate invoice processing, you know the pain. Traditional OCR solutions break when invoice layouts change. Template-based systems require constant maintenance. And don't get me started on the accuracy issues with handwritten amounts.

Common OCR Problems: 85% accuracy on structured invoices, complete failure on handwritten fields, and zero adaptability to new layouts.

That's where AI-powered extraction changes the game. Instead of rigid templates, you define what data you want, and the AI figures out where to find it - regardless of layout variations.

Quick Start: Your First Invoice Extraction

Before jumping into code, you need to set up your extraction schema. Here's the complete process:

Prerequisites

Create Your Account

Define Your Schema

Use the visual schema builder to define what fields you want to extract (invoice_number, total_amount, vendor_name, etc.)

Get Your Processor ID

Copy the processor UUID from your dashboard - you'll need this for API calls

Set Up Your Schema →

Once you have your processor set up, here's how to submit an invoice for processing:

Quick Start - Submit Invoice

// Submit invoice for processing (after creating your processor)
const formData = new FormData();
formData.append('document', invoiceFile);
formData.append('processor_id', 'your-processor-uuid'); // From your dashboard

const response = await fetch('https://ninjadoc.ai/api/extract', {
  method: 'POST',
  headers: {
    'X-API-Key': 'nj_your_api_key_here'
  },
  body: formData
});

const job = await response.json();
console.log('Job started:', job.id);

That's it! Once you've defined your schema in the dashboard, the API handles everything else. No training data, no template configuration, no layout mapping required.

Real-World Example: Processing Vendor Invoices

Here's a production-ready function that handles the complete invoice processing workflow:

Production-Ready Invoice Processing

async function processInvoice(invoiceFile, processorId) {
  try {
    // Step 1: Submit document for processing
    const formData = new FormData();
    formData.append('document', invoiceFile);
    formData.append('processor_id', processorId);

    const submitResponse = await fetch('https://ninjadoc.ai/api/extract', {
      method: 'POST',
      headers: {
        'X-API-Key': process.env.NINJADOC_API_KEY
      },
      body: formData
    });

    if (!submitResponse.ok) {
      throw new Error(`Submit failed: ${submitResponse.status}`);
    }

    const job = await submitResponse.json();
    console.log('Processing started, job ID:', job.id);

    // Step 2: Poll for completion
    let result;
    do {
      await new Promise(resolve => setTimeout(resolve, 2000)); // Wait 2 seconds
      
      const statusResponse = await fetch(`https://ninjadoc.ai/api/jobs/${job.id}/status`, {
        headers: {
          'X-API-Key': process.env.NINJADOC_API_KEY
        }
      });
      
      result = await statusResponse.json();
    } while (result.status === 'queued' || result.status === 'processing');

    if (result.status !== 'completed') {
      throw new Error(`Processing failed: ${result.status}`);
    }

    // Step 3: Extract and validate data
    const extractedData = result.data.reduce((acc, field) => {
      acc[field.field_name] = field.value;
      return acc;
    }, {});

    // Validate critical fields
    if (!extractedData.invoice_number || !extractedData.total_amount) {
      throw new Error('Missing critical invoice data');
    }

    return {
      success: true,
      data: extractedData,
      confidence: result.data.map(f => ({ [f.field_name]: f.confidence })),
      processing_time: result.processing_time_ms
    };

  } catch (error) {
    console.error('Invoice processing failed:', error);
    return {
      success: false,
      error: error.message
    };
  }
}

Handling Different Invoice Layouts

The beauty of AI extraction is layout adaptability. Once you've defined your field schema in the dashboard, the same processor works across all invoice variations - different vendors, countries, or formats.

Traditional OCR

• Requires template per layout
• Breaks with design changes
• Manual coordinate mapping
• 60-80% accuracy

AI Extraction

• One schema for all layouts
• Adapts to design changes
• Semantic understanding
• 95%+ accuracy

Production Deployment Tips

1. Implement Retry Logic

Network issues happen. Implement exponential backoff for failed requests to ensure reliable processing.

2. Monitor Confidence Scores

Set confidence thresholds (we recommend 0.85+) and flag low-confidence extractions for manual review.

3. Batch Processing

Process multiple invoices in parallel, but respect rate limits. Start with 10 concurrent requests.

Cost Optimization

Smart processing can significantly reduce costs. Here are proven strategies:

💡 Pro Tips for Cost Reduction

• Preprocess images: Compress and optimize before sending
• Cache results: Store extracted data to avoid reprocessing
• Selective extraction: Only extract fields you actually need
• Batch similar documents: Group invoices by vendor for better efficiency

Next Steps

You now have everything needed to implement AI-powered invoice processing. Remember to first create your processor in the dashboard to define your extraction schema, then use the code examples above for your API integration.

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