
AmineI Built Invoice Parsing That's 15x Faster (Here's How) The Problem Every B2B...
Every B2B SaaS that deals with finances hits the same wall: How do you extract data from messy invoice PDFs?
I spent 6 months manually parsing invoices for a finance automation tool. It was hell:
import pytesseract
text = pytesseract.image_to_string('invoice.pdf')
# Parse with regex...
60% accuracy, 3 seconds ❌ Too slow, too inaccurate
const result = await vision.documentTextDetection(invoice);
Better accuracy, still 2 seconds ❌ Still too slow for real-time UX
const result = await openai.chat.completions.create({
model: "gpt-4-vision-preview",
messages: [{role: "user", content: [...]}]
});
❌ Accurate but $0.50/page, 5 seconds
Built Invoice2JSON - custom ML model optimized for invoices.
Pre-processing Pipeline
Vision Transformer
Field Extraction
Post-processing
Rust Backend
Python: 847ms average
Rust: 142ms average (6x faster)
Edge Processing
Cloudflare Workers in 200+ cities
Reduced latency by 40%
Model Quantization
FP32 model: 234ms
INT8 model: 142ms (same accuracy!)
Async Architecture
Webhook-first design
No blocking requests
Scales to 10K req/sec
Before vs After comparison:
| Metric | Before | After | Improvement |
|---|---|---|---|
| Speed | 3,247ms | 142ms | 23x faster |
| Accuracy | 62% | 99.9% | 61% increase |
| Cost/page | $0.50 | $0.03 | 94% cheaper |
const Invoice2JSON = require('invoice2json');
const client = new Invoice2JSON('sk_...');
const invoice = await client.parse('./invoice.pdf');
console.log(invoice.data);
// Output (142ms later):
// {
// vendor: "Acme Corp",
// total: 1250.00,
// date: "2024-01-15",
// line_items: [...],
// confidence: 0.998
// }
Free tier: invoice2json.com (25 invoices/month)
Drop them in the comments! Happy to share more technical details.