Building voice AI that scales for enterprises

In the rapidly evolving landscape of artificial intelligence, voice technologies represent one of the most promising yet challenging frontiers for business implementation. A recent technical discussion between Arjun Desai of Cartesia and Rohit Talluri of AWS revealed critical insights into how enterprises can effectively deploy voice AI at scale. Their conversation cuts through the hype to address the practical realities organizations face when implementing voice assistants and conversational AI beyond simple consumer applications.

Key Points

• Voice AI adoption requires addressing core infrastructure challenges including latency management, system reliability, and seamless user experiences that traditional ML platforms aren't designed to handle effectively.

• Successful enterprise voice implementation demands specialized architecture with components like real-time streaming capabilities, advanced language understanding, and context management systems working in concert.

• The technology stack for production voice AI differs significantly from experimental environments, requiring robust monitoring, testing frameworks, and deployment strategies that can support millions of concurrent users.

• Voice applications present unique scaling challenges across multiple dimensions including user volume, conversation complexity, domain knowledge breadth, and multi-modal integration requirements.

• The partnership model between AWS and specialized vendors like Cartesia provides enterprises with both fundamental infrastructure and domain-specific optimization needed for effective deployment.

Expert Analysis

The most compelling insight from this discussion is how voice AI fundamentally changes the infrastructure requirements for machine learning deployments. Unlike conventional ML models that can operate with acceptable batch processing latency, voice applications demand near-instantaneous responsiveness while maintaining context across complex, multi-turn conversations.

This matters tremendously for enterprises because it explains why many initial voice AI initiatives fail to gain traction. Organizations that attempt to retrofit existing ML infrastructure for voice applications often encounter insurmountable performance issues. Voice AI requires purpose-built architectures with streaming-first design principles—not just at the model level, but throughout the entire technology stack. Companies that recognize this fundamental difference are positioning themselves to capitalize on voice as a truly transformative interface rather than merely implementing another customer service channel.

Beyond the Discussion: Real-World Implications

What the discussion didn't fully explore is how voice AI deployment strategies differ across industries. Financial services organizations, for example, face stringent compliance requirements that necessitate comprehensive logging and verification systems. A major U.S. bank recently implemented voice authentication that reduced fraud by