Ninjadoc AI vs AWS Textract: Why Document Intelligence Matters More Than Just OCR

This guide helps you decide when to use AWS Textract (raw OCR output) versus Ninjadoc AI (evidence‑first document Q&A). If your team is spending time writing and maintaining parsing logic on top of OCR, the sections below show how a question‑answering approach delivers structured results with confidence and coordinates.

Raw OCR provides text and boxes; you still write the logic to decide which text matters. A Q&A approach eliminates brittle parsing by letting you ask, "What is the final amount due?" and returning the value with confidence and visual proof you can overlay on the PDF.

Below are analytical patterns frequently observed in production. These neutral examples highlight engineering trade‑offs and how a question‑answering approach mitigates them, no anecdotes required.

These scenarios highlight why many teams are exploring AWS Textract alternatives for their document automation workflows. Have you encountered similar issues? The good news is that intelligent extraction can turn these challenges into strengths.

Let's get hands-on and look at how the code differs when implementing document extraction for invoice processing. I'll walk you through typical examples of AI-powered invoice extraction using AWS Textract versus Ninjadoc AI. This isn't just about lines of code, it's about the development time, maintainability, and reliability in real document automation workflows. If you're searching for document extraction code examples, these will show the contrast between traditional OCR approaches and modern AI solutions.

import boto3
import re

def extract_invoice_total(document_bytes):
    textract = boto3.client('textract')
    
    response = textract.analyze_document(
        Document={'Bytes': document_bytes},
        FeatureTypes=['TABLES', 'FORMS']
    )
    
    # Manual parsing logic - this is where it gets complicated
    total_patterns = [
        r'total[:\s]*\$?([0-9,]+\.?[0-9]*)',
        r'amount due[:\s]*\$?([0-9,]+\.?[0-9]*)',
        r'balance[:\s]*\$?([0-9,]+\.?[0-9]*)'
    ]
    
    for block in response['Blocks']:
        if block['BlockType'] == 'LINE':
            text = block['Text'].lower()
            for pattern in total_patterns:
                match = re.search(pattern, text)
                if match:
                    # Hope this is the right total... no confidence here
                    return {
                        'value': match.group(1),
                        'confidence': 'unknown',
                        'coordinates': block['Geometry']
                    }
    
    return None  # Failed to find total - common with varying layouts

import requests

def extract_invoice_total(document_bytes):
    response = requests.post(
        'https://api.ninjadoc.ai/v1/extract',
        headers={
            'Authorization': 'Bearer YOUR_API_KEY',
            'Content-Type': 'application/json'
        },
        json={
            'document': document_bytes,
            'processor_id': 'invoice_processor',
            'questions': ['What is the total amount due?']
        }
    )
    
    if response.status_code == 200:
        data = response.json()
        return {
            'value': data['extracted_data'][0]['value'],
            'confidence': data['extracted_data'][0]['confidence'],
            'coordinates': data['extracted_data'][0]['geometry']
        }
    return None

// 1. Set up BFF route: /api/ninjadoc/* (see SDK docs)
// 2. Use SDK in browser (API key stays server-side)

import { createBrowserClient } from '@ninjadoc-ai/sdk';

const client = createBrowserClient({
  baseUrl: window.location.origin // Points to your BFF
});

const result = await client.extractDocument(file, 'invoice_processor');
const processedDoc = await client.processDocument(result.id);

return {
  value: processedDoc.regions[0].metadata.extractedText,
  confidence: processedDoc.regions[0].confidence,
  coordinates: processedDoc.regions[0].boundingBox
};

Breaking down these code examples reveals the true difference: Textract demands you build the intelligence, while Ninjadoc delivers it out of the box. Which approach aligns better with your document automation goals?

Choosing between AWS Textract and Ninjadoc AI depends on your specific needs for document processing. There's no one-size-fits-all answer, but I'll break it down based on common use cases, team capabilities, and long-term goals. If you're wondering when to use AWS Textract or looking for an alternative for advanced AI extraction, consider these factors to make an informed decision.

• Can you ask new questions without re‑training or re‑mapping?
• Do answers include evidence by default (coordinates + confidence)?
• How does it handle layout drift and template variation?
• Can you set confidence thresholds and route low‑confidence cases?
• Is integration a single API call with structured JSON outputs?

Get hands‑on fast, no templates, no custom parsing. Ask your first question and see the evidence.

1. Upload a PDF.
2. Ask: "What is the final amount due?" or any question you care about.
3. Toggle the overlay to see the exact region the answer came from.

Cost is more than API fees; TCO also includes developer time and maintenance. Ninjadoc offers pay-as-you-go pricing with transparent credit costs. Credits never expire, so you only pay for what you use. Estimates below mirror the exact billing model used in the app.

cost_per_doc = pages * 25 + fields * 5  (typical: ~10 fields)

Ultimately, the best choice depends on your scale and needs. Use the billing page to model your volume with transparent pay-as-you-go pricing and per‑document estimates.