1. Define the document-processing boundary
An Azure document intelligence workflow should start with where documents enter, where structured data leaves and which systems are allowed to interact with the processing path. Email, SFTP, APIs and cloud storage can all be valid ingress points, but the security and operational assumptions differ.
2. Separate extraction from business validation
AI document services can identify document content and structure. Business validation should remain explicit: required fields, totals, identifiers, reference lookups and workflow-specific acceptance rules can be evaluated after extraction.
3. Design for asynchronous processing and exceptions
Document workloads arrive unevenly. Queue-oriented processing can help absorb bursts, isolate failures and allow retries without turning every document into a synchronous user request. The exception path should be part of the architecture, not an afterthought.
4. Make processing state observable
Operations teams need to know whether a document was received, extracted, validated, rejected, retried or delivered. Useful telemetry is about workflow state and failure reason, not merely infrastructure health.
5. Treat data lifecycle as an implementation requirement
Region, retention, deletion, identity, logging and integration boundaries should be agreed for the production implementation. They should not be inferred from a marketing diagram because different customers can have different security and contractual requirements.
6. Keep downstream delivery stable
The value of document intelligence is realised when trusted data reaches the system of record or workflow that needs it. A stable output schema and explicit error handling make the integration easier to operate as document layouts change.
Bring us your document workflow.
Tell us the document type, volume, validation requirements and target system. We will map the right MicroExtract implementation approach.