Business workflows rarely arrive as one clean document. A case may contain a PDF form, scanned evidence, a call recording, screenshots, and a video clip. The challenge is not producing a summary; it is extracting structured data with enough evidence and confidence to drive a real process.

This session builds one end-to-end case-intake pipeline in C# with Azure AI Content Understanding. We define a reusable schema, submit multimodal content to an analyzer, collect structured results, validate required fields, apply confidence gates, redact sensitive data, and route uncertain results to human review. Brief comparisons show when Document Intelligence or a custom LLM pipeline is the better fit.

Attendees will leave able to model a multimodal extraction schema, implement the analyzer workflow, design confidence and review boundaries, and choose the appropriate Azure service based on content type, customization, cost, and operational risk.

Audience: Engineers, data practitioners, and architects building production content pipelines.
Format: 45–60-minute Azure-focused technical talk with live C# demonstrations.
Demo: A multimodal case file processed through schema extraction, validation, confidence gates, PII handling, and human review.
Evidence: Previously delivered at AI Community Day on December 5, 2025 as a 45-minute session.
Materials: Talk-specific repository, slides, and recording are not yet published.
Vendor scope: Microsoft-specific; uses Azure AI Content Understanding and compares adjacent Azure services.