Question

A logistics company uses an Azure AI Content Understanding custom analyzer to extract shipping fields (tracking number, weight, destination) from scanned bills of lading. Downstream, an agent auto-approves shipments based on the extracted values. Auditors flag that some low-quality scans produced incorrect destination values that were still auto-approved. You must ensure that uncertain extractions are routed to a human reviewer before the agent acts, without discarding well-extracted documents. What should you do?

Accepted Answer

Use the per-field confidence scores returned by the analyzer and route any document with a field confidence below a defined threshold to a human-in-the-loop review queue.

Answer

Switch the analyzer output format from structured JSON to markdown so the agent can read the raw layout text and infer correctness itself.

Answer

Increase the number of few-shot labeled examples in the analyzer schema so that all destination values are always extracted with 100% accuracy.

Answer

Enable OCR on the analyzer so that scanned documents are converted to text, which guarantees correct field extraction on low-quality images.