This solution accelerator enables organizations to derive actionable insights from conversational data using Azure AI services and generative AI. It targets analysts and users needing to understand patterns, topics, and key phrases within large volumes of customer interactions, offering an interactive web interface for exploration and decision-making.

How It Works

The solution orchestrates Azure AI Foundry, Azure AI Content Understanding, Azure OpenAI Service, and Azure AI Search to process conversational data. It employs speech-to-text transcription, key phrase extraction, and topic modeling, powered by Azure OpenAI's GPT and embedding models, to transform raw conversations into structured, searchable knowledge.

Quick Start & Requirements

• Deployment: Via GitHub Codespaces, VS Code Dev Containers, or local environment using Azure Developer CLI (azd).
• Prerequisites: Azure subscription with permissions for resource creation, and availability of Azure AI Foundry, Azure OpenAI Service, Azure AI Search, and Azure AI Content Understanding in the chosen region. Quota availability for GPT and embedding models is critical.
• Setup: Deployment takes 7-10 minutes.
• Links: Deploy in GitHub Codespaces, Deploy in Dev Containers

Highlighted Details

• Leverages Azure AI Foundry for a comprehensive AI solution.
• Supports interactive chat experiences for querying insights.
• Includes sample synthetic data generated by Azure OpenAI.
• Configurable deployment settings for Azure region, models (GPT-4o, GPT-4o-mini), and capacity.

Maintenance & Community

This is a Microsoft-maintained solution accelerator. Further community or roadmap details are not explicitly provided in the README.

Licensing & Compatibility

The solution itself is likely governed by Microsoft's standard terms for Azure services and open-source contributions. Users must comply with the Product Terms applicable to Microsoft Azure Services. Commercial use is subject to Azure service terms.

Limitations & Caveats

The solution is dependent on Azure services, and costs will vary based on usage. Users must ensure sufficient quota for Azure OpenAI models before deployment. The solution is not designed for high-risk applications where failure could lead to severe harm.