Your Team Is a Very Expensive OCR Machine
Somewhere in your company, someone opens a PDF, reads it, types the contents into another system. Then they do it again. Hundreds of times per week.
Manual invoice processing costs EUR 10-25 per document. AI extraction brings that under EUR 4.
That’s not a marginal improvement. That’s a different cost structure entirely.
Human data entry runs a 2-3% error rate on a good day. Modern AI extraction drops that below 0.5%.
Fewer correction cycles. Fewer angry supplier emails. Fewer audit headaches. This is production-ready infrastructure deployed across thousands of companies.
The Four-Stage Pipeline
Every document processing system follows the same architecture: ingest, extract, validate, integrate. The complexity lives in each stage’s details.
Stage 1: Document ingestion
Documents arrive from everywhere. Email attachments. Scanned uploads. API feeds. Shared drives.
Your ingestion layer normalizes them into a consistent format. PDFs, images, and scanned documents go through OCR first. Born-digital PDFs skip that step.
Cloud OCR APIs charge roughly EUR 1.50 per 1,000 pages for basic extraction. Self-hosted open-source OCR on GPU infrastructure costs about EUR 0.09 per 1,000 pages.
Sixteen times cheaper. At 10 million pages monthly, cloud APIs cost EUR 15,000 while self-hosted runs under EUR 1,000.
Stage 2: Field extraction
This is where AI does its real work. The model identifies and extracts specific fields: invoice number, date, line items, totals, vendor details.
Two approaches work for SMBs.
Template-based extraction uses predefined rules for known document formats. Fast and cheap but breaks when formats change.
LLM-based extraction uses language models to understand context and extract fields regardless of layout. More flexible but higher per-document cost.
We use a hybrid for most clients. Template matching handles the 80% of documents with predictable formats. LLM fallback covers everything else.
Weird layouts, handwritten notes, formats nobody’s seen before. The LLM handles them all. Modern AI extraction achieves accuracy above 99%, a massive jump from legacy OCR’s 60-75%.
Stage 3: Validation
Never trust extraction output blindly. Every field gets validated against business rules.
Does the invoice total match the sum of line items? Is the vendor in your approved supplier list?
Does the PO number exist in your ERP? Cross-reference extracted data against your source systems.
Flag anything that fails for human review. This is your human-in-the-loop checkpoint.
A validation rule that flags 40% of documents isn’t helping. It’s relocating the bottleneck. Tune your thresholds based on real data, not assumptions.
Stage 4: System integration
Validated data flows into your existing systems via API. Your ERP gets invoice data. Your accounting system gets journal entries.
API-first integration is the safest approach. Your existing systems stay as they are. The pipeline sits alongside them, pushing data through documented interfaces.
One logistics client went from 60 hours per week of manual processing to 3. Not a productivity improvement. A structural change.
Two employees were reassigned to work that actually requires judgment. The pipeline paid for itself inside four months.
Choosing Your Stack
The right setup depends on three things: document volume, data sensitivity, and budget.
Under 1,000 documents per month? Cloud APIs are the obvious call. Azure Document Intelligence costs about EUR 10 per 1,000 pages for prebuilt models.
Higher volumes or sensitive data push toward self-hosted solutions. Open-source models like PaddleOCR or Tesseract 5 combined with fine-tuned extraction models give full control.
Regulated industries often need on-premise deployment. GDPR requires you to know exactly where data lives. Processing sensitive documents through third-party cloud APIs creates compliance questions you’d rather avoid.
The Confidence Threshold Decision
This architectural choice makes or breaks your system. What confidence score triggers automatic processing? What score routes to human review?
Too low and you process garbage. Too high and everything goes to humans, defeating the purpose.
We start at 95% confidence for automatic processing. Documents below that hit a review queue. Corrections feed back into the model as training data.
Over 4-8 weeks, you’ll have enough data to calibrate. Most clients settle between 90% and 97% depending on error tolerance.
Monitoring Matters More Than You Think
Your pipeline isn’t a set-and-forget system. Track extraction accuracy weekly. Monitor which document types cause the most failures.
Common degradation sources: vendors changing invoice formats, new document types entering the system, seasonal volume spikes that stress infrastructure.
Build alerting around three metrics. Confidence scores trending down. Review queue growing beyond capacity.
Processing latency exceeding thresholds is your third warning sign.
The best systems improve over time. Human corrections become training data. Confidence thresholds get calibrated against real-world performance.
Where It Breaks
We’ve seen pipelines fail for predictable reasons. Bad scan quality is number one. If your scanner produces blurry images, no amount of AI will fix it.
Missing validation rules are number two. A pipeline that can’t detect duplicate invoices will happily process them twice.
Scope creep kills the third batch of projects. Start with one document type and get it working at 95%+ accuracy.
Then add the next type.
Don’t try to handle invoices, purchase orders, contracts, and shipping manifests in your first sprint. You’ll ship nothing.
What It Costs
A pilot (4-8 weeks, single document type) runs EUR 15,000-25,000. Production deployment covering multiple document types and system integration lands between EUR 30,000 and EUR 70,000.
The ROI calculation is straightforward. If your team processes 500 documents weekly at 15 minutes each, that’s 125 hours per week.
At EUR 35/hour loaded cost, you’re spending EUR 4,375 weekly on manual processing. Automate 90% of that and you save over EUR 200,000 per year. Sound worth exploring?
For a broader view of AI use cases with similar ROI, read our overview of AI applications for SMBs. If you’re exploring how AI fits into your broader workflow, our AI workflow integration guide covers the full picture.
Processing hundreds of documents manually each week? Let’s build a pipeline that handles it. We’ll scope the project based on your actual documents and volumes.
