Call Center AI is an AI-powered call center solution developed by Microsoft using Azure and OpenAI GPT. It allows sending phone calls from AI agents via API or direct calls to the bot's configured phone number, ideal for insurance, IT support, customer service, and more. Key features include real-time streaming conversations to minimize delays, multi-language and voice tone support, SMS integration, nuanced comprehension with GPT-4o and GPT-4o-mini models, secure handling of sensitive data via RAG best practices, domain-specific understanding, structured claim schemas, automated to-do lists, content filtering, and jailbreak detection. It offers customizable prompts, human fallback, call recording, brand-specific voices, and cloud-native Azure deployment for scalable, low-maintenance automation.
Call Center AI is an innovative, open-source project hosted on GitHub under the Microsoft organization, designed to revolutionize customer service through AI-driven telephony. Leveraging Azure Communication Services, Cognitive Services, and OpenAI's GPT models (specifically GPT-4o and the more efficient GPT-4o-mini), this solution enables the creation of intelligent virtual agents capable of handling inbound and outbound phone calls seamlessly. The core functionality allows developers to initiate calls programmatically via a simple API endpoint or configure a dedicated phone number for direct user interactions, making it versatile for applications like insurance claims processing, IT support troubleshooting, and general customer inquiries.
The system supports real-time streaming of conversations, ensuring minimal latency and natural flow without awkward pauses. It handles disconnections gracefully by resuming sessions where they left off and stores all interactions for compliance, auditing, and future reference. Multi-language support covers various locales (e.g., French, Chinese), with customizable voice tones and even brand-specific neural voices via Azure Custom Neural Voice. SMS integration allows for supplementary information exchange, such as sending confirmations or gathering additional details post-call. This setup provides 24/7 availability, improving accessibility and user satisfaction for low-to-medium complexity queries.
Powered by state-of-the-art LLMs, the bot achieves deep comprehension of nuanced queries, including domain-specific jargon in fields like insurance or IT. It follows retrieval-augmented generation (RAG) protocols to securely incorporate private documents without exposing sensitive data, adhering to best practices for data privacy and compliance. The structured claim schema ensures organized data collection (e.g., timestamps, locations, emails), while automated tools generate to-do lists, detect and filter inappropriate content, and identify potential jailbreak attempts. Historical conversation data can be used to fine-tune the LLM over time, enhancing personalization and accuracy. Redis caching optimizes performance, reducing redundant computations.
Extensive customization options include tailored prompts, feature flags for A/B testing, and seamless handover to human agents when needed. Call recording (optional, via Azure Storage) aids quality assurance, and integrations with Azure Application Insights provide comprehensive monitoring of metrics like latency and token usage. Public APIs expose claim data for reporting, and future roadmap items include automated callbacks and IVR workflows. The architecture supports elastic scaling in a serverless model, minimizing operational overhead while handling variable workloads.
The high-level architecture follows the C4 model, with users and agents interacting via the core app, which orchestrates audio exchange through Azure Communication Services. At the component level, it integrates embeddings (ADA), LLM completions (OpenAI), speech-to-text/text-to-speech (Cognitive Services), vector search (AI Search for RAG), and persistent storage (Cosmos DB). Event-driven processing via Event Grid and queues ensures reliability.
Deployment is cloud-native on Azure, with Bicep templates for infrastructure as code. A pre-built container image is available on GitHub Container Registry for rapid setup. Local development is supported via Rust/Python environments and Azure Dev Tunnels, allowing quick iteration without full cloud provisioning. Prerequisites include Azure CLI and a Communication Services resource with a phone number. The project emphasizes proof-of-concept status, with notes on production hardening like multi-region support and security audits.
A French-language demo video on YouTube showcases interactions, including claim data extraction, conversation synthesis, and reminder creation during an accident report scenario. Post-call reports visualize history, claims, and todos. Example API usage demonstrates initiating calls with JSON payloads specifying bot details, tasks, and schemas. Costs are estimated at around $720/month for moderate usage (1000 calls of 10 minutes), covering services like OpenAI tokens, speech processing, and storage.
For optimization, provisioning dedicated Azure OpenAI instances reduces latency, and OpenLLMetry integration tracks LLM performance. Fine-tuning with anonymized historical data (using Azure AI tools) improves domain adaptation. Moderation levels are configurable via Azure OpenAI Content Filters, and schema/task customization allows per-use adaptations. The project avoids heavy LLM frameworks for direct control over streaming and tools, using the OpenAI SDK natively.
This solution stands out for its end-to-end integration of AI telephony, offering a scalable path from prototype to production while prioritizing responsible AI practices like harm detection and data anonymization.
