Over the last few years, Voice AI agents have moved quickly from experimentation into production. Early adoption centered on customer support, basic IVR modernization, sales automation, meeting summaries, and general-purpose voice assistants. These early use cases were low-stakes enough to tolerate imperfection. That is changing. Real-time Voice
Testing an AI voice agent is nothing like testing a standard application. You’re validating a live, real-time pipeline where WebRTC audio streaming, speech-to-text, LLM reasoning, and text-to-speech synthesis work together within milliseconds, every time a user speaks. Traditional QA processes and frameworks weren’t built for this. They
For organizations prioritizing data privacy and zero variable cloud costs related to inference, it is entirely possible to build a voice agent using off-the-shelf open source tools. In this post, we will outline a practical Voice AI stack that avoids vendor lock-in while still supporting real-time, natural
This project shows how to prototype a real-time voice AI Android app using Gemini 2.0’s Live API over WebSockets as an open-source proof of concept before committing to full production infrastructure. By combining low-level audio control on Android, duplex audio streaming, and multimodal AI, we built an
