AI Workflow Automation

Back-office workflows fail in predictable ways: data is retyped between systems, approvals stall in inboxes, and nobody can reconstruct who decided what. Automating them requires reliable extraction from messy real-world documents and workflow logic that degrades gracefully to human review instead of silently guessing.

• •Scanned and free-form documents defeat template-based parsing
• •Extraction confidence must gate automation: low-confidence fields need human review, not silent acceptance
• •Approval chains span multiple systems and identities and must survive escalations and delegation
• •Every automated decision needs an audit trail that reconstructs inputs, rules, and approvers
• •Integrations with existing line-of-business systems must be additive, not rip-and-replace

We built the engine as an event-driven middleware layer: an extraction service with per-field confidence scoring, a workflow engine for routing and approvals, and a review UI where humans resolve exactly the cases automation flags.

• Used Azure Document Intelligence for layout-aware extraction with per-field confidence scores
• Routed low-confidence extractions into a React review queue with side-by-side document view
• Modeled approval chains declaratively, with steps, escalation timers, and delegation rules as configuration
• Connected existing systems through an Azure Functions integration layer instead of replacing them
• Wrote an append-only audit log capturing every extraction, rule evaluation, and approval event