Manifesto

Our Vision

Our vision is to build the world’s most advanced human-grade voice agents, capable of replacing not just first-tier but also complex second- and third-tier customer service roles. We aim to create real-time audio communication that is indistinguishable from a human, moving beyond simple "voice bots" to agents that can handle intricate processes, use tools effectively, and navigate human nuances without the "robotic" latency of the past.

What needs to shift for organizations to get the full value of Voice AI
‍For widespread adoption of Voice AI we believe the industry must solve three critical challenges:

The Latency Barrier: Communication must be instantaneous. We must eliminate the "turn-taking" lag to allow for seamless conversations and real-time feedback loops.

Contextual Intelligence: Voice agents shouldn't just speak as realistic as possible; they should understand complex instructions and execute tool calls with the same precision as humans. They should also be able to navigate around missing data in systems.

Infrastructure Sovereignty: For wide-scale adoption in regulated industries (Health, Insurance, Finance), the technology must be self-hostable. AI in regulated industries cannot rely solely on US-based vendors; it must be deployable within a customer's own secure environment from firms that are not tied to.

Why do we believe Speech-to-Speech will unlock peak performance of realtime Voice AI applications?

Traditional "pipeline" models (Speech-to-Text → LLM → Text-to-Speech) are fundamentally flawed for realtime applications. We believe native Speech-to-Speech is the only path forward for the following reasons:

Record Latency: Without having to transcribe spoken word into text to understand it, we can drastically reduce latency.

Preservation of Nuance: Text is a poor medium for tone and emotion. A simple "Okay" can have five different meanings based on tonality. Native speech processing captures these latent acoustic features that text-based intermediates discard. If you want your service representatives to be empathetic and de-escalate heated situations. So should your AI application. For this you need understanding of emotion and reaction with emotion.

Minimal Word Error Rate: STT / TTS Pipelines suffer from high word error rates, that create a cascade of follow up errors. Any number, any e-mail, any address that is misunderstood, will break the effectiveness of your Voice AI application. With Speech to Speech Technology we have the highest performance on understanding and can scale that to 27 languages.

Intelligence Retention: By projecting audio to the model, we unlock the model's full pre-trained world knowledge, reasoning, and tool-calling capabilities without the degradation typically seen in pure audio-based models.

What we commit to change in the industry

Moving beyond the surface: Engineering the core infrastructure for the next generation of enterprise voice intelligence.

Solving the "Unsolved" Basics: We are committed to fixing the "simple" problems the giants have ignored—such as the correct pronunciation of complex numbers and technical terminology—through innovation and smart approaches rather than just brute-force compute.

From Linear Flows to Dynamic Agents: We are moving away from rigid decision trees. We commit to building our models that, allowing for intelligent, seamless intent classification and tool use without interrupting the natural flow of conversation.

The Integration Shift (Stack vs. Platform): We are convinced that the era of the "all-in-one" voice platform is ending for the enterprise. We commit to providing the stack that allows companies to integrate voice deep into their own technology stack. Instead of a surface-level wrapper, our technology becomes a core infrastructure component, giving enterprises full control over their data, their logic, and their customer experience. For L2 and L3 Support a deeper integration is fundamental.

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