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call center ai

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Basic Information

Call Center AI is an open source proof-of-concept that provides an AI-powered automated call center implemented on Azure and integrated with OpenAI GPT models. The repository enables sending outbound calls via an API and answering inbound calls on a configured phone number. It is designed for scenarios such as insurance, IT support and customer service and can collect structured claim data during conversations. The project includes deployment automation for Azure, a local development mode, and configuration templates for language, voice, claim schema and moderation. It demonstrates streaming voice interactions, conversation storage, retrieval-augmented generation, and basic operational telemetry. The README documents prerequisites, deployment steps, configuration options, and examples to run the service locally or in Azure using container images, Bicep, Makefiles and optional Twilio SMS integration.

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App URL
https://github.com/microsoft/call-center-ai
Github Repository
https://github.com/microsoft/call-center-ai/blob/main/README.md

App Details

Features
Real-time inbound and outbound voice calls with a dedicated phone number and streamed conversations that resume after disconnection. Multi-language speech-to-text and text-to-speech support with configurable voices and optional custom neural voice endpoints. Integration with Azure Communication Services, Cognitive Services, Azure OpenAI (gpt-4.1 and gpt-4.1-nano), AI Search for RAG, embeddings, Redis cache, Azure Cosmos DB for persistence, and Azure Storage and Event Grid for media and events. Customizable prompts, claim schemas and call objectives. Feature flags for runtime configuration, call recording toggle, human agent fallback, content moderation and jailbreak detection. Deployment automation via Makefile and IaC, local test script for offline testing, Application Insights telemetry and cost/monitoring guidance.
Use Cases
The solution automates routine customer interactions, enabling 24/7 handling of low to medium complexity calls and reducing load on human agents. It collects structured claim or case data, generates summaries, reminders and to-do items, and exposes per-caller reports for review. RAG and embeddings let the assistant use company documents to ground answers, improving accuracy for domain-specific queries. Configurable prompts, languages and voice cloning help maintain brand tone and consistent user experience. Monitoring hooks and telemetry enable performance and latency analysis. The repository also provides guidance to iterate, fine-tune models with anonymized historical data, and transition toward production readiness with additional security and operational hardening.

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