Guides
Enterprise Document AI Architecture
A reference view of how document intelligence fits between your intake channels and your systems of record.
By DocMetis · Published · Updated · 8 min read
Layered view
A document AI platform sits between intake channels and systems of record, and is best designed as distinct layers so each can change independently.
- Intake — email, portals, scanners, mobile capture, APIs, SFTP, EDI
- Processing — pre-processing, classification, extraction, understanding
- Decision — validation rules, cross-document reasoning, confidence policy
- Human — review queues, annotation, approvals
- Orchestration — workflow, routing, SLAs, escalation
- Integration — ERP, core banking, claims, CRM, RPA, data warehouse
- Observability — accuracy, throughput, exception analytics, audit trail
Deployment and data residency
Where the documents may physically be processed is often the first constraint in banking, government and healthcare. Practical options include managed cloud, private cloud in-region, on-premise, and hybrid designs where classification runs centrally while sensitive extraction stays inside the customer network. Choose the model before selecting the components — it constrains everything downstream.
Integration patterns
Three patterns cover most deployments: synchronous API calls for real-time capture in a customer journey; batch processing for high-volume overnight loads; and event-driven posting where completed documents publish an event that downstream systems consume. Exception queues should be first-class in all three.