A real business case rarely arrives as one clean document. It may include a PDF form, scanned evidence, screenshots, a call recording, and a video clip—all describing different parts of the same situation.

The hard part is not generating a summary. It is extracting structured data with enough evidence and confidence to drive a real process without hiding uncertainty.

This session builds an end-to-end case-intake pipeline in TypeScript using Azure Content Understanding. We will define a reusable schema, process multimodal content with an analyzer, and collect structured results across documents, images, audio, and video.

Then we will move beyond extraction. The pipeline will validate required fields, preserve supporting evidence, apply confidence gates, redact sensitive data, and route uncertain results to a person for review. We will also compare when Azure Document Intelligence or a custom LLM pipeline is the better fit based on content type, customization, cost, and operational risk.

Attendees will leave able to model a multimodal extraction schema, implement an analyzer workflow, design confidence and review boundaries, and select the appropriate Azure service for a production content pipeline.

Structured output is useful. Structured output with evidence, validation, and a recovery path is something a business process can trust.

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.