Audit-ready. Cert-Ready. In your infrastructure.
Enterprise AI Agents that make finance decisions auditable, reproducible, and audit-ready. The Decision Layer documents every decision path - from document intake to posting proposal. Full source code access.
Finance decisions in enterprises depend on individual expertise. Depreciation classifications, expense receipts, correction postings, tax assessments - the domain ruleset is complex and application varies by person, location, and interpretation.
The consequences hit the balance sheet directly: Correction postings that could have been avoided. Depreciation classifications that only the auditor challenges. Expense receipts treated differently across offices. Tax assessments that aren't reproducible.
For auditors, internal revision, and shared service centers, this is a systematic risk: Every undocumented decision is a potential audit finding. Every rule interpretation that varies is a consistency risk.
A Finance Agent is a specialised AI agent for document processing and bookkeeping. It reads documents with contextual language understanding, assesses them against versioned rule sets (tax law, accounting standards, internal policies), and produces documented posting proposals. The Decision Layer routes every micro-decision: autonomous at high confidence and clear rules, to a specialist at edge cases.
DATEV and SAP remain your leading systems. The Finance Agent sits in front - it delivers decision-ready outputs including a complete chain of reasoning. Every decision produces a full decision record: input, applied rule, rule version, confidence, decision path, outcome. This is not retroactive documentation - it is the technical proof of how each decision was made.
The Document Agent reads, understands, and assesses documents with real language understanding - no rigid rule chains, no template matching. Invoices, credit notes, cancellations, expense receipts, travel expense reports. The Decision Layer checks completeness, plausibility, and tax classification. At high confidence: automatic posting proposal. At edge cases: escalation to the specialist.
Depreciation logic varies by asset type, tax classification, and region. The Decision Layer makes the logic explicit: asset type classification, tax classification check, application of depreciation table per current version, documentation of reasoning, handoff as structured posting proposal. Reduced misclassifications and auditable depreciation logic.
Varying interpretation of deductibility is one of the most common audit points. The Decision Layer standardizes: receipt completeness check, attendee list verification, purpose and business relevance check, application of deductibility rules per current version. Reproducible, rule-compliant treatment instead of situational interpretation.
Eliminate correction postings before they happen. The Workflow Agent orchestrates the quality assurance process: automatic plausibility check before posting, comparison against reference values and historical patterns, escalation for anomalies, documentation of every check in the audit trail.
1. Read and Understand - The agent reads documents with contextual language understanding. Vendor, amount, service description, tax-relevant features. No rigid template matching - real document comprehension.
2. Assess - Account, cost centre, tax classification, depreciation start, expense receipt criteria. Every assessment is based on a versioned rule set - not on the experience of individual accountants.
3. Decide - The Decision Layer routes: autonomous at high confidence and clear rules, to a specialist at edge cases or low confidence. You define the thresholds - not the AI.
4. Document - Every micro-decision produces a decision record: input, applied rule, rule version, confidence, decision path, outcome, timestamp. This is not retroactive documentation - it is the technical proof of how each decision was made.
Incoming document → Agent extracts → Decision Layer
(Invoice, data & context decomposes into steps
Credit note, │
Expense report) ┌─────────────────┼─────────────────┐
│ │ │
Rule engine AI Agent Human
(clear rule) (confidence) (exception)
│ │ │
Tax rate per Account Specialist
regulation proposal + reviews and
score decides
│ │ │
└─────────────────┼─────────────────┘
│
Audit trail per step
Rule · Version · DeciderThe Decision Layer decomposes the booking process into individual decision steps. For each step, it defines: Does the agent decide, a ruleset, or a human?
The agent reads, understands, and assesses documents with real language understanding. The Decision Layer routes each assessment:
Document → Extraction → Classification → Domain Assessment → Confidence Score → Posting Proposal or Human Escalation
Every decision produces a complete, immutable record: input, model, assessment, confidence score, applied rule with version, decision path, outcome. This is not retroactive documentation - it is the technical proof of how each decision was made.
What cost models for enterprise AI look like and what self-hosting vs. cloud really costs is detailed in the TCO comparison in the Blueprint 2026.
Not "we have ISO." Not "we don't need ISO." Rather: Every agent is technically built to be certifiable and auditable at any time.
Controls are first-class data objects in the system - not documents in a folder. Every control has: a technical implementation (RLS policy, trigger, API check), an automatic evidence generator, an evidence history with timestamp, status, and version, and an auditor view with drill-down to the concrete implementation.
What this means concretely: Controls live in the database, not in Confluence. Evidence is generated automatically - no human collects screenshots. Drill-down from traffic-light status to the concrete RLS policy name and test SQL. The system proves itself.
Auditors see live status in the Auditor Portal. No Excel package, no PDF report, no "ask the developer."
Finance Agents that make automated decisions about postings, amounts, and tax matters are subject to the transparency and oversight requirements of the EU AI Act. Our architecture addresses these requirements as a design principle:
Transparency (Art. 13): Decision Layer documents every decision path. Audit trail shows input → model → assessment → outcome.
Human Oversight (Art. 14): Human-in-the-Loop architecturally enforced for risk decisions. Decision Layer routes automatically.
Recording Obligations (Art. 12): Complete audit trail with timestamps, input hashes, model versions, decision paths. Immutable, exportable.
Risk Management (Art. 9): Governance Layer with bias monitoring, confidence tracking, anomaly detection. Cert-Ready Controls with automatic evidence generation.
Our architecture is designed from the ground up for EU AI Act requirements. This is an architecture statement, not a compliance certificate.
1 week
Process analysis with your finance team. Map posting logic, document rule sets, understand system landscape. Prioritize use cases - typically: document processing or depreciation logic as entry point.
3-4 weeks
Production PoC. One agent, one finance process, live in your infrastructure. Decision Layer, governance, audit trail - from day one. Cert-Ready from the start, not retrofitted.
Ongoing
More agents for more finance processes. Correction postings, expense receipts, tax assessments, intercompany. The architecture grows - same governance, same auditability.
After 12-18 months, you operate your Finance Agents independently. Full access to source code, prompts, and rule versions. No vendor lock-in.
Correction postings preventively eliminated - not corrected after the fact.
Auditable decision logic for external audit and internal revision.
Consistent posting logic across all locations and entities.
Reduced tax and compliance risk.
Scalability without knowledge loss through staff turnover.
Cert-Ready by Design - structurally audit-ready, not documented retroactively.
Our article series for decision-makers automating finance processes with AI Agents.
Audit firms, tax advisory practices, shared service centres, and enterprises with in-house accounting. Anywhere high document volumes meet complex rule sets.
No. The agent processes routine cases autonomously and escalates exceptions to specialists. The result: fewer correction postings, consistent rule application, and complete documentation.
Every agent decision produces a complete decision record. Auditors see the full decision path in the Auditor Portal - down to the specific rule and version applied.
The agent applies versioned rule sets deterministically. Every decision documents the exact rule and version. If an error occurs, it lies in the rule set - not in its application. Rule errors are localisable, correctable, and versioned. Liability remains with the organisation, but the demonstrable duty of care increases significantly.
Whether document processing, depreciation logic, correction postings, or expense receipts - we start with one process, one agent, one week of discovery.
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