Speech-to-Speech Models That Is Truly Human-on-Calls

Our foundational model supports a 1 million-token context window, enabling it to understand and retain long conversations, documents, or voice interactions with high consistency, memory, and depth. This makes it especially powerful for multi-turn, multimodal applications like voice agents and intelligent automation.

The typical round-trip latency from voice input to final voice output is under sub 300 milliseconds. This ultra-low latency enables real-time, human-like conversations where the agent can listen, reason, and respond naturally without awkward delays.

ROSE uses a unique experiential learning algorithm that allows it to improve with every conversation. Unlike static models, ROSE gains experience over time — learning from user interactions, adapting to preferences, and continuously refining its responses. It builds memory, recognizes recurring patterns, and evolves contextually — just like a human would.

Our average cost is ≈ $0.045/minute, tiered by volume and feature set. For custom deployments and protocol‑specific use cases, please connect with our team for a tailored quote.