An AI-assisted application review workflow for eligibility validation, duplicate detection, policy enforcement, evidence retrieval, and recommendation generation.
Impact
consistent, evidence-backed application assessment and routing
Hiring signal
Rules-aware agent architecture, duplicate detection, governed data integration, retrieval grounding, decision routing, and workflow automation.
My role
Developer · review architecture, eligibility and duplicate logic, backend integration, Fabric data flow, retrieval grounding, and decision routing
Status
An interactive trace is available.
The problem
Review teams operated across fragmented systems and manual checks. Eligibility attributes, duplicate indicators, applicant profiles, and historical evidence had to be evaluated consistently before a recommendation or escalation could be issued.
My approach
The workflow uses Microsoft Fabric as the governed data foundation, deterministic services for eligibility and duplicate checks, Azure AI Search for reference retrieval, and Azure AI Foundry for evidence-grounded review synthesis and recommendation generation.
Architecture judgment
Separating deterministic gates from model-based reasoning keeps mandatory rules explicit and auditable. Microsoft Fabric provides a governed data foundation, while Azure AI Search supplies the reference context required for evidence-backed recommendations.
Architecture
The complete execution path, service boundaries, data stores, security controls, validation stages, and operational telemetry.
Implementation
Technology
Interactive trace
Run the trace to see the main workflow stages.