{"id":242269,"date":"2026-02-16T22:46:17","date_gmt":"2026-02-16T22:46:17","guid":{"rendered":"https:\/\/evertise.net\/?p=131146"},"modified":"2026-02-16T22:46:17","modified_gmt":"2026-02-16T22:46:17","slug":"tavus-introduces-raven-1-bringing-multimodal-perception-to-real-time-conversational-ai","status":"publish","type":"post","link":"https:\/\/ipsnews.net\/business\/2026\/02\/16\/tavus-introduces-raven-1-bringing-multimodal-perception-to-real-time-conversational-ai\/","title":{"rendered":"Tavus Introduces Raven-1, Bringing Multimodal Perception to Real-Time Conversational AI"},"content":{"rendered":"

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SAN FRANCISCO, CA – <\/span>Tavus<\/span><\/a>, the human computing company building lifelike AI humans that can see, hear, and respond in real time, <\/span>launched Raven-1 into GA today<\/span><\/a>, a multimodal perception system that enables AI to understand emotion, intent, and context the way humans do.<\/span><\/p>\n

Raven-1 captures and interprets audio and visual signals together, enabling AI systems to understand not just what users say, but how they say it and what that combination actually means. The model is now generally available across all Tavus conversations and APIs.<\/span><\/p>\n

Conversational AI has made rapid progress in language generation and speech synthesis, yet understanding remains a persistent gap. Most systems process speech by converting it into transcripts. The transformation that strips away tone, pacing, hesitation, and expression- everything that makes the communication colorful and meaningful. Without those signals and the perception of how something is said, AI is forced to guess at intent, and those guesses break down exactly when they matter most. The sarcastic “great” becomes indistinguishable from the genuine one.<\/span><\/p>\n

Raven-1 takes a different approach. Instead of analyzing audio and visual signals in isolation, it fuses them into a unified representation of the user’s state, intent, and context, producing natural language descriptions that downstream language models can reason over directly.<\/span><\/p>\n

A New Model for Conversational Perception<\/b><\/h2>\n

Raven-1 is a multimodal perception system built for real-time conversation in the Tavus Conversational Video Interface (CVI). Rather than outputting rigid categorical labels like “happy” or “sad,” Raven-1 works just like humans think to produce interpretable natural language descriptions of emotional state and intent at sentence-level granularity.<\/span><\/p>\n

Key capabilities include:<\/span><\/p>\n