 Extract information from invoices and credit notes: Azure Document Intelligence in Foundry Tools

 Identify discussion topics in customer service call recordings: Azure AI Search

 Detect customer sentiment and opinions: Azure AI Search

To minimize development time, you should select services that provide prebuilt extraction and enrichment rather than building custom models.

For invoices and credit notes, the best fit is Azure Document Intelligence because it is purpose-built for intelligent document processing and includes prebuilt models (such as invoice-style extraction) that return structured fields (invoice number, vendor, totals, dates, line items). This directly satisfies “extract information” from financial documents without custom ML training.

For customer service call recordings, the raw input is audio, but the task is to identify “discussion topics,” which is an information discovery and text-enrichment problem once the calls are transcribed. Azure AI Search is the correct recommendation because it can index large collections of content and apply built-in AI enrichment (for example, key phrase extraction and entity extraction) to help derive and query “topics” across many records.

For sentiment and opinions, Azure AI Search is again the best choice in this constrained list because it can run built-in enrichment skills over ingested text (such as transcripts or notes) to annotate content with sentiment-related metadata and then make that enriched data searchable and filterable at scale. This approach delivers fast time-to-value: ingest → enrich → index → query, without standing up a custom training pipeline.