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
LLMs alone can’t “act.” They generate text. The key to success, and the way to avoid the 80% of AI projects that never leave the prototype stage, is moving beyond conversation to orchestration. This means integrating LLM reasoning with automation frameworks, enabling explainable outcomes and human oversight,
The choice between conversation-based and turn-based Voice AI agent patterns is a strategic business decision, not just a technical detail. Beyond what your agent will say, you must decide how it will run. This architectural choice defines how your voicebot will scale, what it will cost to
