DATEV Rechnungsautomatisierung mit KI: So funktioniert es

Your bookkeeper processes 400 invoices a month. Each one takes 3-4 minutes of manual data entry — vendor name, invoice number, date, amounts, tax rates, line items. That is 20 to 27 hours a month on copy-paste. Not analysis. Not advisory. Copy-paste.

The downstream effect is consistent: month-end close gets pushed back, junior staff work late, and mistakes appear exactly where they cost the most — VAT amounts, IBAN fields, line-item allocations.

Invoice automation does not replace DATEV. It feeds DATEV the data it needs, structured and validated, before a human ever opens the file.

How DATEV AI Integration Works

The workflow has four steps.

Step 1: The PDF arrives.

Whether it comes in via email attachment, a scanned paper invoice, or a supplier portal, the document lands in the system. OCR converts the image to text. You have had OCR for 15 years. What changes is what happens next.

Step 2: The extraction engine reads the document.

A language model — specifically Mistral AI, hosted on European servers — reads the document in context. Not pattern-matching against field positions. It understands that "Nettobetrag" and "Summe ohne MwSt." mean the same thing. It reads line items from a table even when the table format changes between suppliers. It pulls the IBAN from a footer even when it appears somewhere unexpected.

The system extracts: vendor name, vendor address, invoice number, invoice date, due date, line items with descriptions, net amounts, tax rates (7% or 19%), gross amounts, and IBAN.

Step 3: Every extracted field gets a confidence score.

This is where the system parts from simple OCR tools. Each field carries a score from 0 to 100 — how certain the model is about that field. A clearly printed invoice from a known supplier might score 97 across all fields. A blurry fax from a new vendor might score 61 on the IBAN field.

Fields below the confidence threshold — configurable per firm, typically 80 — go into a human review queue. Your accountant sees exactly which fields need checking and why. High-confidence invoices flow straight through.

Step 4: Validated data pushes to DATEV.

Structured, validated invoice data transfers to DATEV Unternehmen Online or DATEV Rechnungswesen via the DATEV integration connector. Your accountants see the booking in DATEV exactly as they would if they had typed it themselves. Their review process in DATEV does not change. They just have less to type.

Why "Rechnungsverarbeitung KI" Is Not Just Another OCR Tool

Standard OCR tools were built to read text. AI extraction was built to understand documents.

The difference shows up in two situations that firms with volume invoicing run into constantly.

Recurring invoices from the same vendor. Your SaaS subscriptions, your office supplies, your leased equipment — these come from the same vendors every month in roughly the same format. After the system processes a vendor a few times, it builds a context profile. On recurring invoices from known vendors, extraction accuracy in our system runs above 95%. The confidence scores reflect this. Most of those invoices never touch the review queue.

Non-standard layouts. A German mid-market company deals with invoices from 50, 100, sometimes 200 different suppliers. They do not all use the same template. Some put the invoice number in the header. Some put it in the footer. Some call it "Rechnungsnummer," some call it "Belegnummer," some call it "Ref. Nr." The language model handles this without rules programming. You do not configure a template per supplier.

Automatic Invoice Processing DATEV: The GDPR Question

Before any German mid-market company runs invoice data through an AI system, the same question comes up: where does the data go?

The answer is structural, not contractual. The AI Loopwise extraction engine runs on Hetzner servers in Germany. Mistral AI, the LLM used for document understanding, is hosted in France. No invoice data crosses the Atlantic. No data touches US infrastructure. Per-client schema isolation in PostgreSQL means your client data and your data do not share a database schema.

This is not a policy document that a vendor can change. It is how the system is built.

For firms operating under German tax law, this means no conflict with DSGVO obligations around client financial data. Your clients' invoice details stay in the EU, processed under EU law, by infrastructure subject to EU jurisdiction.

What Changes at Month-End

The feedback we hear most from firms running automated invoice processing is not about the hours recovered — though the 20+ hours per month is real. It is about what month-end close looks like.

When every invoice has been extracted and staged in DATEV throughout the month, close is not a scramble. There is no pile of invoices waiting to be typed in. No "we need to push close back two days because we still have 80 invoices to process."

Your accountants spend month-end reviewing booked transactions, not entering them.

For tax advisory firms, this changes how you can price recurring engagements. If the data entry work drops by 60-70%, the engagement economics shift. Some firms we work with have moved away from pricing around data-processing volume toward billing on advisory output instead. That is a different business model, not just a faster process.

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If you run DATEV Rechnungswesen or DATEV Unternehmen Online and want to see the extraction engine working on sample invoices from your client base, book a 30-minute demo. We will show you live extraction on real invoice formats, including the edge cases.