Loka transformed customer voice interactions by building a conversational AI agent with Amazon Nova 2 Sonic that keeps customers engaged with natural, responsive experiences. Their AWS-based solution achieves high speech reasoning accuracy on Big Bench Audio while delivering significantly lower costs and faster response times than traditional voice AI pipelines. In this post, we demonstrate the architecture and approach Loka used to solve a common frustration: robotic, slow voice assistants that cause customers to hang up, damaging brand reputation and driving up support costs.

Traditional voice assistants follow a three-step process that creates the fundamental problem. First, they convert your speech into text using Speech-to-Text systems. Next, they process that text through a Large Language Model (LLM). Finally, they convert the text response back into speech using Text-to-Speech technology. This pipeline introduces compounding delays at every step. The result is often a 3 to 5 second pause before you hear a response. That delay destroys the feeling of natural conversation. It makes interrupting or correcting the assistant feel clunky and frustrating.

Consider a real scenario at an automotive dealership. A customer calls and says, “I’m looking for that SUV you advertised, but not the hybrid one. I can only come in after 5 PM.” The assistant needs to parse multiple pieces of information simultaneously. It must understand the intent, negation, and scheduling constraints. Traditional systems struggle with this complexity because they lose crucial information during conversion. Tone, hesitation, and urgency disappear when speech becomes text. The dealership context makes these limitations painfully clear. Customers expect immediate, helpful responses when they call. A five-second pause feels like an eternity in a sales conversation. Worse, if the assistant misunderstands and needs clarification, delays compound. The conversation becomes tedious rather than helpful.

Beyond the technical delays, there’s an economic problem. Serving thousands of locations requires strict cost control. Traditional real-time voice systems can become cost-prohibitive at scale, particularly when processing continuous audio streams. The combination of poor experience and high cost has limited voice AI adoption. Businesses need a better solution.

Recent advances in AI have unlocked a fundamentally different approach. Developers can now send audio streams directly to speech-to-speech models that handle understanding, reasoning, and generation as a unified system. By processing audio end-to-end, these models capture tone, emotion, and subtle cues that traditional text-only pipelines miss.

To validate this approach, rigorous testing was essential. We used Big Bench Audio, a benchmark that measures reasoning over speech inputs. Amazon Nova 2 Sonic achieved a speech reasoning score of 87.0. This outperformed Gemini 2.5 Flash Native Audio (Live API) at 71.0 and exceeded GPT Realtime’s 83.0. These scores confirmed that native audio processing doesn’t sacrifice intelligence for speed. The model could handle complex, multi-part requests in real dealership scenarios.