AI-Powered Invoice Data Extraction
OCR plus AI reads invoice numbers, line items and tax rates from any format, catches duplicates and posts to SAP. Demo, figures modelled.
- Industry
- Finance & Accounting
- Build, estimated
- 6 weeks
At a glance
- It starts with
- Invoice Received
- The machine handles
- 11 of 12 steps
- A person keeps
- Manual Review
- Bottom line
- -85%Processing Time
The problem
Open a PDF invoice and find the invoice number — takes you two seconds. A computer in the nineties would have failed at it.
Today, the same thing costs one AI API call and lands at 95 to 99 percent accuracy. Even when the supplier writes "Ref no." instead of "Invoice number". Even when the invoice is a crooked phone photo.
So the question is no longer whether this works. It's why three people in your company spend every day transferring the same five fields into SAP — at 500 invoices a month, that's 33 hours of typing per week.
This showcase walks the full route from "PDF in the inbox" to "posted in SAP" — with duplicate blocking, plausibility checks, and a human only where the AI is uncertain.
Invoice processing consumes significant resources in many organizations. Typical scenario: Monthly, over 500 invoices from more than 200 suppliers arrive via email, mail, and fax. Three full-time employees spend their workdays transferring data from PDFs, scanned documents, and images manually into SAP. Invoices come in wildly different formats: invoice numbers positioned top left on some, bottom right on others, various languages. Every wrong number means reconciliation problems at month-end. Error rates hover around 8% - leading to duplicate payments, missed early payment discounts, and frustrated suppliers. Average processing time per invoice is 4 minutes, adding up to 33 hours of pure data entry per week. Audits are problematic since traceability is lacking. Annual costs for late fees and missed discounts easily exceed €50,000. The monotonous work leads to high department turnover.
The process, step by step
Scroll through. The diagram stays put and highlights the step you are on.
- 01
Invoice Received
Email / Upload
A new invoice is received via email or manually uploaded, triggering the automation process.
- 02
OCR Extraction
Google Cloud Vision
Google Cloud Vision OCR analyzes the PDF/image and extracts all relevant data: vendor, invoice number, date, line items, amounts.
- 03
AI Validation
OpenAI GPT-4
OpenAI GPT-4 validates the extracted data against business rules and historical patterns. Inconsistencies are detected.
- 04
Duplicate Check
PostgreSQL
The system checks the PostgreSQL database to see if this invoice already exists, preventing duplicate payments.
- 05Branch
Duplicate?
Gateway decision: Was a duplicate found? If yes, the process is aborted and the invoice is rejected.
- 06
Duplicate Rejected
Already Exists
The invoice was identified as a duplicate and is automatically rejected. The process ends here.
- 07Branch
Valid?
Gateway decision: Is all data complete and valid? If no, the invoice is routed for manual review.
- 08Human decides
Manual Review
Error Detected
An employee manually reviews and corrects the invoice. After correction, it's sent through AI validation again.
- 09
Auto-Categorization
AI Cost Center Assignment
AI automatically categorizes the invoice and assigns it to the correct cost center based on historical data.
- 10
SAP Integration
Data to ERP
The validated and categorized invoice data is automatically transferred to SAP and stored in the ERP system.
- 11
Notification
Team Informed
The accounting team receives a notification about the successfully processed invoice with all relevant details.
- 12
Complete
Invoice Processed
The invoice is fully processed, stored in the system, and ready for payment approval.
What changes
Possible setup, not a packaged product
The figures are modelled target values for a possible setup, derived from industry benchmarks and our own tests. They do not come from a customer project; real values depend on your business. We do not sell this setup as a finished product: we measure where your bottleneck is, then build with your team. For systems that require certification (e.g. HIS, gematik, DATEV-certified) we work with specialised partners.
Before vs. After
| Aspect | Before | After |
|---|---|---|
| Data Entry | Before: Manual data entry from PDF invoices | After: Automatic OCR extraction with AI validation |
| Processing Time | Before: 15-20 minutes per invoice | After: 2-3 minutes end-to-end |
| Error Rate | Before: Up to 8% in data entry | After: Below 0.5% |
| Duplicate Detection | Before: No automatic detection | After: Intelligent duplicate detection |
Technology
Technology Stack
Integrations
Systems this setup can connect to
- SAP S/4HANAERP System
- Direct integration via SAP BTP API for invoice booking and cost center assignment
- Google Cloud VisionOCR Engine
- State-of-the-art OCR technology for reliable text recognition from any document
- OpenAI GPT-4AI Validation
- Intelligent validation and categorization of invoice data
- PostgreSQLDatabase
- High-performance database for duplicate detection and data storage
Frequently Asked Questions
What would this look like at your company?
This workflow is a demo. Whether it pays off for you depends on where things get stuck at your company. The method page shows how to find out. To place a workflow of your own, use the free assessment. No call needed.
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