Skip to content
D
GoBD-compliant §203 StGB-compliant Q1

Invoice Capture Agent

Read incoming invoices, verify mandatory fields, extract data - archived in audit-compliant format.

Reads incoming invoices (PDF, scan, XRechnung, ZUGFeRD), verifies mandatory fields per Paragraph 14 UStG.

Analyse your process
Airbus Volkswagen Shell Renault Evonik Vattenfall Philips KPMG

LLM document extraction, rule-based mandatory-field checks, audit-proof archiving

The agent extracts invoice data via LLM from PDF, XRechnung (German e-invoice standard) and ZUGFeRD (hybrid PDF/XML e-invoice), validates Paragraph 14 UStG mandatory fields and the e-invoice schema deterministically, and archives XML and PDF in audit-proof form with timestamp.

Outcome: Invoice throughput reduced from 12 to 18 minutes to under 2 minutes per document, 15 to 25 euros of process cost saved per invoice per Gennai benchmark, and the 39 percent error rate of manual capture eliminated.

62% Rules Engine
38% AI Agent
0% Human

The split between LLM extraction and rule-based validation explains the high automation rate:

10,000 invoices a month, five FTEs retyping by hand

Invoice receipt ties up more capacity in most finance functions than any other single process in accounts payable. A company with 10,000 incoming invoices per month employs three to five full-time staff just to open documents, retype data and check mandatory fields. At the same time, the European e-invoicing mandate from 2027 is accelerating the format transition. Anyone not automating now is building up process debt that gets more expensive every quarter.

Manual capture costs companies EUR 15 to 25 (USD 16 to 27) per invoice

According to the Billentis E-Invoicing Report, the fully loaded cost of a manually processed incoming invoice in the European mid-market is between EUR 15 and 25 (USD 16 to 27) - depending on company size, number of approval stages and rework rate. That number includes personnel costs, error correction, archiving and the most invisible line item: early payment discounts lost to slow processing. According to APQC, top-performing AP teams reach 98 percent first-pass accuracy, while bottom-quartile organisations only reach 88 percent - over 60 percent of all invoice errors originate from manual data entry. Every such error triggers a correction cycle that doubles throughput time.

At 10,000 invoices per month, this adds up to EUR 150,000 to 250,000 (USD 162,000 to 270,000) in annual process costs - before the first posting even enters the ERP.

The European e-invoicing mandate sharpens the pressure from 2027

Since January 2025, every company in Germany must be able to receive structured e-invoices. The transition period allows PDF and paper until the end of 2026. From 2027, sending e-invoices in B2B traffic becomes mandatory for most companies - only businesses with up to EUR 800,000 (USD 864,000) in prior-year revenue receive a deferral until 2028. The permitted formats are XRechnung and ZUGFeRD from EN 16931 onwards (source: German Federal Ministry of Finance FAQ, 2025). Similar mandates are in motion in France, Italy and Spain.

Concretely this means: your accounts payable function will receive more and more XML files instead of scanned PDFs in the coming months. A process built on visual inspection and manual data entry cannot absorb this format shift. Anyone who keeps capturing manually must either add headcount or accept throughput times that break early payment discount deadlines.

A rule-based agent replaces 90% of the manual steps

The Invoice Capture Agent breaks capture into eight individual decision steps. Only two of them - document type classification and data extraction - need AI classification for unstructured inputs such as scans or photographed receipts. The remaining six steps are fully rule-based: mandatory field checks against the statutory VAT (US: sales tax equivalent) invoice catalogue, duplicate checks via invoice number and amount, schema validation of the e-invoice format, vendor matching via master data, audit-compliant archiving and handover to the next process step.

This ratio - six rule-based steps, two AI-supported - explains the high automation rate. The agent makes no value judgements. It reads, checks against defined criteria and forwards. On deviations, it escalates to the responsible clerk instead of interpreting itself. That is Decision Layer stage 1: rules decide, AI extracts, humans handle the exceptions.

A concrete scenario: a mid-sized machine builder receives 4,500 incoming invoices per month - one third as ZUGFeRD PDF, one third as XRechnung XML, the rest as classic PDF scans from smaller suppliers. The agent processes the structured formats fully automatically in under three seconds per document. For unstructured scans, the LLM pipeline extracts invoice number, amount and service period with a confidence score. Below the defined threshold, the document goes to a clerk. In practice this affects less than 5% of volume.

Audit-compliant archiving becomes a by-product of the process

German GoBD (German record-keeping standard) bookkeeping principles (2025 update) require archiving both the XML file and the PDF for e-invoices - only one of the two is no longer sufficient. Every document needs a qualified timestamp and must be stored immutably. Tax record retention laws mandate an eight-year retention period.

In manual processes, archiving is a separate work step that can be forgotten or performed incorrectly. The agent archives every processed document automatically as part of the capture process. XML and PDF are timestamped and stored immutably. Every validation decision - mandatory fields present, format valid, no duplicate - is logged in the decision record. This log forms the procedural documentation that the tax auditor demands at a standard audit.

Archiving is therefore not additional effort but a by-product of automated capture. That is the difference between a tool that scans invoices and an infrastructure that governs the entire document inbox.

Micro-Decision Table

Who decides in this agent?

8 decision steps, split by decider

62%(5/8)
Rules Engine
deterministic
38%(3/8)
AI Agent
model-based with confidence
0%(0/8)
Human
explicitly assigned
Human
Rules Engine
AI Agent
Each row is a decision. Expand to see the decision record and whether it can be challenged.
Classify document type Invoice, credit note, reminder or quotation? AI Agent

LLM classification of unstructured inputs

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Verify mandatory fields Are all mandatory fields per Paragraph 14 UStG present? Rules Engine Vendor

Deterministic checklist against statutory mandatory fields

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Challengeable by: Vendor

Data extraction Invoice number, amount, currency, service period? AI Agent

LLM extraction from unstructured documents

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Duplicate check Does this invoice already exist in the system? Rules Engine

Database match on invoice number, vendor and amount

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Vendor matching (exact) Can the vendor be uniquely identified? Rules Engine

Exact match via VAT ID or vendor number

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Vendor matching (fuzzy) Which vendor matches when the name differs? AI Agent Vendor

Fuzzy match for name variants or new vendors

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Challengeable by: Vendor

E-invoice validation Is the XRechnung/ZUGFeRD format schema-compliant? Rules Engine

Schema validation against official specification

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

GoBD-compliant archiving Are XML and PDF stored immutably with timestamp? Rules Engine Auditor

Rule-based archiving per GoBD 2025

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Challengeable by: Auditor

Decision Record and Right to Challenge

Every decision this agent makes or prepares is documented in a complete decision record. Affected parties (employees, suppliers, auditors) can review, understand, and challenge every individual decision.

Which rule in which version was applied?
What data was the decision based on?
Who (human, rules engine, or AI) decided - and why?
How can the affected person file an objection?
How the Decision Layer enforces this architecturally →

Does this agent fit your process?

We analyse your specific finance process and show how this agent fits into your system landscape. 30 minutes, no preparation needed.

Analyse your process

Governance Notes

GoBD-compliant §203 StGB-compliant

Not a high-risk system under the EU AI Act. GoBD relevance: high - invoice receipt is the starting point of every tax-relevant process. Paragraph 14 UStG defines the mandatory fields, which the agent checks deterministically. GoBD 2025 requires archiving both the XML and the PDF portion of e-invoices - PDF alone is no longer sufficient. Paragraph 203 StGB is relevant for client data of professional secrecy holders: LLM inference runs exclusively in EU data centres.

§203 StGB-relevant data is encrypted end-to-end and never passed to AI models in plain text.

Process Documentation Contribution

The Invoice Capture Agent automatically documents: which mandatory fields were checked, which classification the AI Agent made, which duplicate check was performed, and how archiving was carried out. This documentation is part of the GoBD procedural documentation and can be presented to the tax auditor as technical evidence.

Assessment

Agent Readiness 87-94%
Governance Complexity 21-28%
Economic Impact 78-85%
Lighthouse Effect 26-33%
Implementation Complexity 26-33%
Transaction Volume Daily

Prerequisites

  • ERP system with API access (SAP, DATEV, Oracle FI or equivalent)
  • Inbound channel configuration (email, portal, EDI)
  • Vendor master data with VAT ID
  • GoBD-compliant document management system

Infrastructure Contribution

The Invoice Capture Agent builds the foundational infrastructure for the entire AP chain. The document classification (invoice vs. credit note vs. reminder) is reused by the Credit Note Agent. The e-invoice validation forms the basis for the Invoice Generation Agent. The GoBD-compliant archiving pattern is used by all Finance agents that process tax-relevant documents.

What this assessment contains: 9 slides for your leadership team

Personalised with your numbers. Generated in 2 minutes directly in your browser. No upload, no login.

  1. 1

    Title slide - Process name, decision points, automation potential

  2. 2

    Executive summary - FTE freed, cost per transaction before/after, break-even date, cost of waiting

  3. 3

    Current state - Transaction volume, error costs, growth scenario with FTE comparison

  4. 4

    Solution architecture - Human - rules engine - AI agent with specific decision points

  5. 5

    Governance - EU AI Act, GoBD/statutory, audit trail - with traffic light status

  6. 6

    Risk analysis - 5 risks with likelihood, impact and mitigation

  7. 7

    Roadmap - 3-phase plan with concrete calendar dates and Go/No-Go

  8. 8

    Business case - 3-scenario comparison (do nothing/hire/automate) plus 3×3 sensitivity matrix

  9. 9

    Discussion proposal - Concrete next steps with timeline and responsibilities

Includes: 3-scenario comparison

Do nothing vs. new hire vs. automation - with your salary level, your error rate and your growth plan. The one slide your CFO wants to see first.

Show calculation methodology

Hourly rate: Annual salary (your input) × 1.3 employer burden ÷ 1,720 annual work hours

Savings: Transactions × 12 × automation rate × minutes/transaction × hourly rate × economic factor

Quality ROI: Error reduction × transactions × 12 × EUR 260/error (APQC Open Standards Benchmarking)

FTE: Saved hours ÷ 1,720 annual work hours

Break-Even: Benchmark investment ÷ monthly combined savings (efficiency + quality)

New hire: Annual salary × 1.3 + EUR 12,000 recruiting per FTE

All data stays in your browser. Nothing is transmitted to any server.

Invoice Capture Agent

Initial assessment for your leadership team

A thorough initial assessment in 2 minutes - with your numbers, your risk profile and industry benchmarks. No vendor logo, no sales pitch.

30K120K
1%15%

All data stays in your browser. Nothing is transmitted.

Frequently Asked Questions

Which invoice formats are supported?

PDF (scanned and digital), XRechnung, ZUGFeRD, EDI. The AI Agent extracts from unstructured PDFs and scans. For e-invoices (XRechnung/ZUGFeRD), the structured XML data is processed directly and additionally validated against the schema.

What happens when mandatory fields are missing?

The agent documents exactly which mandatory fields per Paragraph 14 UStG are missing and routes the invoice to the clerk. The vendor can be asked to send a corrected invoice. The defective invoice is still archived in audit-compliant format.

How accurate is the automatic data extraction?

For structured e-invoices (XRechnung/ZUGFeRD), accuracy is 100% - the data is read directly from the XML. For unstructured PDFs and scans, the AI Agent typically achieves 95-98% accuracy. Every extraction includes a confidence score; below a defined threshold, manual review is triggered.

What Happens Next?

1

30 minutes

Initial call

We analyse your process and identify the optimal starting point.

2

1 week

Discover

Mapping your decision logic. Rule sets documented, Decision Layer designed.

3

3-4 weeks

Build

Production agent in your infrastructure. Governance, audit trail, cert-ready from day 1.

4

12-18 months

Self-sufficient

Full access to source code, prompts and rule versions. No vendor lock-in.

Implement This Agent?

We assess your finance process landscape and show how this agent fits your infrastructure.