Build a Natural Language IVR with Telnyx Call Control and AI Inference

The "build a natural language IVR with Telnyx Call Control and AI Inference" example replaces traditional phone trees with a more conversational voice IVR. By using Python/Flask and Telnyx's Call Control, AI Inference, and KV store, callers can speak their needs and be routed to the appropriate department. The app handles inbound calls by answering with Call Control, gathering speech input, and using AI inference to generate a dynamic greeting and route the caller's intent to the correct department.

If the AI inference fails, the app defaults to a static greeting or keyword matching. After a certain number of turns, the call transfers to a default operator. The app uses Telnyx primitives for Call Control and AI Inference, a KV store for menu configuration, and an agent state machine to track call state and retry logic. The AI Inference component uses OpenAI's chat completions API for dynamic greetings and intent routing.

Webhooks are verified with signature verification to prevent spoofed requests. The agent state machine is implemented in an IVRAgent class. The example provides management APIs for updating the menu configuration, retrieving it, listing active agents, and a health check. To use the example, clone the code repository, set up a virtual environment, install the required packages, fill in your Telnyx API credentials, and run the app.

For production use, consider adding a persistent KV store, caller ID personalization, call queues for holds, authentication on management endpoints, and monitoring for LLM latency and fallback rates.