Decagon, in collaboration with Modal, has developed a cutting-edge real-time voice AI platform that delivers high-quality customer interactions by overcoming significant technical challenges. The launch of Decagon Voice involved creating compact and efficient AI models through advanced supervised fine-tuning and reinforcement learning, achieving state-of-the-art accuracy and response quality for voice interactions. To meet the demanding requirements of sub-second latency and seamless responsiveness, Modal's team provided deep-engine level optimizations, including custom draft models and re-engineered inference pipelines. These efforts resulted in a substantial reduction in latency and improvements in intent recognition, setting a new standard in voice AI technology. The partnership also focused on enhancing model generalization through diverse datasets and sophisticated training methodologies. Additionally, Modal's training solutions enabled frictionless model development, allowing Decagon to efficiently iterate and deploy models across various customer conversations. The collaboration underscores the complexity of building scalable, real-time AI systems and highlights the ongoing advancements in conversational intelligence.