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Enterprise procurement teams need approval automation that can be explained to auditors, edited by business analysts, and trusted in regulated industries. Archiet generates a governed procurement agent: BPMN workflow, DMN policy table, bounded LLM for document reading, and a complete audit trail from day one.
Enterprise procurement approval is a high-volume, policy-governed process with real financial exposure. A £50,000 purchase order approved incorrectly is a material risk. A spend approval process that cannot be audited is a compliance risk. A routing decision that an auditor cannot explain is an EU AI Act risk. And yet most 'AI for procurement' tools use opaque ML models to route approvals — the opposite of what enterprise procurement and compliance teams need.
The generated procurement agent implements a three-layer architecture. The AI layer reads purchase orders (PDF, XML, or ERP export) and extracts structured fields: amount, vendor, department, cost centre, budget line, and approval history. The DMN layer evaluates the extracted fields against your procurement policy: amount thresholds by department, vendor approval status, budget utilisation, and delegation-of-authority matrix. The BPMN layer routes the PO to the correct approver(s), manages multi-level escalation, sends notifications, and writes the audit record. The AI never decides the outcome — the policy table does.
Enterprise procurement operates under a Delegation of Authority (DoA) matrix: department managers approve up to £10K, directors up to £50K, CFO up to £250K, board above that. This matrix lives in the DMN policy table — not in the AI model. Procurement analysts can update the thresholds without a code deployment. The generated operator console includes a DoA editor with change history. When a new director joins, procurement updates the matrix in the console; the agent immediately applies the new authority levels.
The generated agent includes integration stubs for SAP Ariba, Oracle Fusion, Coupa, and generic REST webhook sources. Purchase orders arrive via API or email parsing; the agent extracts structured data, evaluates policy, routes for approval, and writes the outcome back to the source system via webhook. The integration layer uses environment variable configuration — no code changes needed to connect a different ERP. For SAP integration specifically, the generated code includes RFC-compatible message structures aligned with SAP's procurement document schema.
Procurement AI in regulated industries (financial services, healthcare, public sector) is subject to EU AI Act scrutiny. The governed agent architecture satisfies the three key requirements for high-risk AI systems: transparent decision logic (DMN table, not a neural network weight), human oversight (escalation paths and override capability in the BPMN process), and audit trail (every PO decision logged with the matched rule, the approver, and the timestamp). The generated AI governance pack includes the risk assessment, model card for the extraction LLM, and data-lineage diagram showing where PO data flows.
Yes. The BPMN process supports sequential multi-level approval: Level 1 (department manager), Level 2 (finance director), Level 3 (CFO), each with configurable timeouts and auto-escalation. If a manager doesn't respond within 48 hours, the BPMN timer event escalates to the next level automatically. The escalation path is defined in the BPMN XML, not in code — business analysts can modify it in Camunda Modeler or bpmn.io.
Extraction confidence scores are evaluated before policy routing. Low-confidence extractions (below a configurable threshold, default 0.85) are held for human verification before the DMN evaluation runs. The operator console shows the extracted fields alongside the original document so an AP clerk can correct errors. Corrections are logged and used to improve extraction performance over time.
ServiceNow and Workday provide configurable workflow engines — but you must configure the workflow in their proprietary tooling, and the AI capabilities (if any) are vendor-controlled. Archiet generates the workflow as standard BPMN + DMN that you own and can run on any BPMN engine (Camunda, Flowable, or the included Python runtime). The DMN policy table is a portable JSON file, not a vendor-specific configuration. You are not locked into a vendor's ecosystem.
Most AI invoice automation is a black box — the model decides, you can't explain why. Archiet generates a governed invoice approval agent: the approval flow is BPMN (auditable), the decision logic is a DMN policy table your finance team can edit without code changes, and the AI only reads documents — it never decides the outcome.
Insurance claims triage requires speed, accuracy, and full auditability. Archiet generates a governed claims triage agent: FNOL intake, AI-powered data extraction from claim documents, deterministic DMN severity routing that actuaries control, and a complete audit trail for regulatory examination.
Loan approval decisions need to be explainable to regulators and customers. Archiet generates a governed loan adjudication agent: the BPMN workflow defines the process, the DMN credit policy table defines the approval criteria, and the AI only reads documents — it never decides the outcome.
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