Question

A financial services company stores thousands of recorded customer support calls as audio files. They need a pipeline that produces, for each call, a structured output containing the full transcript, a concise summary, the detected primary topic, and the customer's stated account issue category (chosen from a fixed list of categories) so these fields can ground an Azure AI Foundry agent. They want to minimize custom code and avoid chaining multiple separate services manually. Which approach best meets these requirements?

Accepted Answer

Define an Azure AI Content Understanding audio analyzer with a field schema that specifies the transcript, summary, topic, and a classification field constrained to the category list, then run the audio files through the analyzer.

Answer

Send each audio file to the Speech-to-text batch transcription API, then write custom code to call a language model separately for summary, topic, and category extraction.

Answer

Index the raw audio files directly into Azure AI Search using integrated vectorization and rely on hybrid search to surface the summary and category at query time.

Answer

Use an Azure AI Vision analyzer with OCR enabled to read the audio waveform images and extract the required fields.