Early Voice AI deployments were built on a straightforward pattern: Speech To Text, LLM, Text To Speech. That pipeline was enough to produce compelling prototypes for customer support, sales automation, and meeting summaries. The pattern holds well until it meets a regulated environment. Telecom platforms, telehealth systems,
Over the last few years, Voice AI agents have moved quickly from experimentation into production. Early adoption centered on customer support, basic IVR modernization, sales automation, meeting summaries, and general-purpose voice assistants. These early use cases were low-stakes enough to tolerate imperfection. That is changing. Real-time Voice
Testing an AI voice agent is nothing like testing a standard application. You’re validating a live, real-time pipeline where WebRTC audio streaming, speech-to-text, LLM reasoning, and text-to-speech synthesis work together within milliseconds, every time a user speaks. Traditional QA processes and frameworks weren’t built for this. They
Recently, I read an article on LinkedIn that captured something many experienced developers have been feeling: software development is changing rapidly in the age of generative AI, but not always in ways we fully understand. One quote especially resonated with me: “An MIT professor called AI ‘a
