Explore voice-first conversational AI through honest conversations with practitioners. Hear from AI builders, infra engineers, product strategists, and more for the latest insights on what it takes to build best-in-class conversational AI experiences.
Check out video episodes and subscribe to the Convo AI Newsletter at podcast.convoai.world
Real-Time Translation Masterclass: Preserving Meaning Across Languages
What does it take to translate a conversation as it happens? In this special montage episode of the Convo AI World Podcast, we bring together selected conversations with Palabra's Artem Kukharenko, Soniox's Klemen Simonic, Rime's Lily Clifford, Akool's Jeff Lu, and Professor Mérouane Debbah to explore the challenges of real-time translation. The episode begins with the human promise of speaking across language barriers, then examines the decisions a translation system must make before a sentence is complete. Klemen explains the tension between responding quickly and waiting for enough information to preserve meaning. Palabra's perspective adds the reality of mixed-language conversations, where people can switch between languages without announcing the switch. Lily and Mérouane broaden the discussion to dialects, regional data, and cultural context. Their insights show why supporting a language involves more than adding its name to a list. Jeff takes the conversation beyond words, exploring how translated video can preserve a speaker's voice and facial expression. From everyday multilingual interactions to enterprise requirements around privacy and customization, the masterclass connects technical choices to the experience of the people using them. It closes with a vision of translation becoming a seamless part of conversation and the work still needed to get there.
Check out video episodes and subscribe to the Convo AI Newsletter at podcast.convoai.world
20 Oct 2026
Small Models, Specific Jobs: ScaleDown on Building Efficient Voice AI
In this episode of the Convo AI World Podcast, Hermes Frangoudis speaks with Neal Patel, co-founder of ScaleDown, about choosing models for the specific jobs inside an AI application. Neal explains how his work forecasting data center demand led him to focus on token efficiency. At ScaleDown, that means building small language models for tasks such as classification, extraction, and summarization. He describes how the team combines existing model components with task-specific neural heads, and where these specialists can work alongside a larger model that handles planning and conversation. For voice AI builders, the discussion covers routing, parallel subagents, conversation history, and post-call analysis. Neal explains the difference between query-aware compression and a generic summary, and why preserving the information needed for an answer matters more than simply shrinking a prompt. He also explores using classifiers for evaluation, where consistency can matter as much as speed. The conversation closes with ScaleDown's integration tools and roadmap, plus Neal's view of how model selection could become more automated. Throughout, he makes a useful distinction: specialized models may reduce variability on a defined task, but they are not fully deterministic.
Check out video episodes and subscribe to the Convo AI Newsletter at podcast.convoai.world
15 Sept 2026
Beyond Evaluations: Trinitite on Building Trustworthy AI Agents
In this episode of the Convo AI World Podcast, Hermes Frangoudis sits down with Dustin Allen, co-founder of Trinitite, to explore why evaluating an agent is only part of making it reliable in production. Dustin traces Trinitite's origins to a productivity app for accountants and finance teams, where a damaged spreadsheet exposed the need to replay actions, restore data, and understand what went wrong. He explains why his team now treats AI delivery, governance, and security as connected parts of the same system, and how Trinitite uses deterministic inference to make its own assessments more repeatable and failures easier to investigate. The conversation gets practical about voice AI: where to place controls in a cascading pipeline, how asynchronous monitoring can preserve conversational flow, and why sensitive tool calls may need checks before an action proceeds. Dustin also discusses multimodal systems, developer tools, and the challenge of observing agents running locally on a device. For teams building voice agents or enterprise AI, the episode offers a grounded discussion of continuous monitoring, remediation, and the limits of guardrails, with an emphasis on learning from failures and keeping people involved in deciding which risks matter most.
Check out video episodes and subscribe to the Convo AI Newsletter at podcast.convoai.world
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