Tavus, the human computing company focused on building lifelike AI humans that can see, hear, and respond in real time, has officially launched Raven-1 into general availability. This new multimodal perception system represents a major step forward in helping AI understand emotion, intent, and context more naturally just like humans do.

Unlike traditional conversational AI tools that mainly rely on transcripts, Raven-1 captures and interprets both audio and visual signals together. As a result, AI systems can understand not only what users say, but also how they say it, and what that combination truly means in real-world conversations.

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Conversational AI has advanced quickly in speech generation and language output. However, understanding human communication remains one of the biggest missing pieces. Most systems still convert speech into plain text, which removes critical elements such as tone, pacing, hesitation, facial expressions, and emotional nuance. Because of this, AI often struggles to correctly interpret intent especially in sensitive or high-stakes situations. For instance, a sarcastic “great” becomes indistinguishable from a sincere one.

Raven-1 takes a completely different approach. Instead of analyzing audio and video separately, it fuses them into a unified representation of a user’s emotional state, intent, and conversational context. Then, it produces natural language descriptions that downstream language models can reason over directly.

A New Model for Conversational Perception

Built specifically for real-time interaction within the Tavus Conversational Video Interface (CVI), Raven-1 does not rely on rigid emotional labels like “happy” or “sad.” Instead, it generates interpretable, sentence-level descriptions of emotional state and intent, similar to how humans process communication.

Key capabilities include:

  • Audio-visual fusion of tone, prosody, facial expression, posture, and gaze
  • Natural language outputs aligned directly with LLMs
  • Temporal modeling that tracks emotional shifts during conversation
  • Sub-100ms audio perception latency with total pipeline latency under 600ms
  • Custom tool calling support for developer-defined emotional or attention-based events

Raven-1 also works alongside Sparrow-1 and Phoenix-4, forming a closed conversational loop where perception shapes responses and responses reshape the interaction moment-by-moment.

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Why Multimodal Perception Matters

Traditional emotion detection tools often flatten complex human feelings into fixed categories, missing layered and contextual emotion. Human communication is fluid, and a single statement can carry multiple emotions at once.

For example, when someone says, “Yeah, I’m fine,” while avoiding eye contact and speaking in a monotone, transcript-based systems may take the words literally. Raven-1, however, captures the full emotional signal, including incongruence between speech and expression.

Industry research suggests that up to 75% of medical diagnoses come from patient communication rather than tests, making perception-aware AI especially valuable in healthcare, therapy, coaching, interviews, and other high-impact fields.

Built for Real-Time Conversations

Designed from the ground up for speed, Raven-1 excels in short, ambiguous, emotionally loaded moments where traditional AI systems fail. Even a single word like “sure” can carry completely different meanings depending on delivery and Raven-1 ensures that meaning is not lost.

With Raven-1 now generally available across Tavus conversations and APIs, Tavus is pushing conversational AI closer to truly human-like understanding.

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