Beyond LLMs: Engineering Real-Time Human Interaction with Tavus Griffin ✨

Imagine you're sitting in front of your computer at 11:47 PM ⏳.

You've been debugging a production issue for the past three hours. Your application is behaving unpredictably, the logs aren't particularly helpful, and you decide to ask an AI assistant for a second opinion.

You open a conversation.

You start explaining the problem.

Before you've finished describing the architecture, the assistant interrupts with a response.

You pause.

It starts speaking.

You correct one of its assumptions.

It stops, processes your correction, and generates another response.

Technically, everything works.

The speech recognition is accurate. The language model understands your question. The generated response is relevant.

Yet something feels fundamentally unnatural.

The assistant understands your words, but it doesn't quite understand the conversation.

It doesn't know whether your pause means you're thinking, whether your expression suggests confusion, or whether you're about to interrupt with something important.

It is processing an interaction as though human communication were simply a sequence of completed requests.

And that's where things get interesting.

What if an AI system didn't have to wait for you to finish speaking before it started understanding you ⁉️

What if it could continuously perceive your voice, observe visual signals, maintain conversational context, and coordinate its own speech and expressions while you're still interacting with it ⁉️

Not another chatbot with a voice.

Not another language model connected to an animated avatar.

But an entirely different approach to building interactive intelligence.

This is the architectural direction explored by Tavus through Griffin, its Human Interaction Model (HIM).

And understanding why Griffin matters requires us to look beyond language models and examine a much more interesting engineering challenge:

How do you build an AI system that doesn't just generate responses, but participates in a continuously evolving human interaction ⁉️

Hello DEV Family! 👋

This is ❤️‍🔥 Hemant Katta ⚔️

So let's dive deep into Human Interaction Models and take a closer look.

The Architectural Problem: Conversational Intelligence Isn't Human Interaction

For years, conversational AI systems have largely followed a familiar architectural pattern.

A user provides an input. The system processes it. A model generates a response. The application delivers that response and waits for the next input.

For text-based applications, this approach works remarkably well.

A typical voice-based architecture extends the same principle using three major components :

- Automatic Speech Recognition (ASR) - Large Language Model (LLM) - Text-to-Speech (TTS) 

The resulting pipeline looks something like this :

 ┌───────────────────────────┐ │ User Speech │ └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ │ Audio Capture │ └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ │ Speech Recognition │ └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ │ Text Representation │ └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ │ Large Language Model │ └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ │ Response Text │ └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ │ Text-to-Speech │ └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ │ Audio Playback │ └───────────────────────────┘ │ └───────────────↺ User Speech 

Each component performs a well-defined responsibility.

ASR converts spoken language into text. The LLM interprets the request and generates a response. TTS converts the response into audible speech.

From a software engineering perspective, this is a sensible design.

It provides modularity, component isolation, independent optimization, and relatively straightforward debugging.

However, it also introduces a fundamental limitation.

The architecture is optimized around completing conversational turns, not continuously participating in them.

Consider a simple example.

You're explaining a production incident to an AI assistant.

You say :

We've been experiencing intermittent database failures, and I suspect the problem is somewhere between the connection pool and... 

Then you pause.

You're thinking.

A conventional voice application might interpret that silence as the end of your turn and begin generating a response.

But a human listener doesn't necessarily interpret silence as completion.

A pause might indicate :

- The speaker is thinking. - The speaker is searching for the right technical term. - The speaker expects acknowledgment. - The speaker is preparing to continue. - The speaker has actually finished speaking. 

These situations can sound similar at the audio level but represent different conversational intentions.

This creates a problem that cannot be solved simply by increasing the language model's parameter count.

The system needs to understand the interaction itself.

The latency problem

In a strictly sequential pipeline, the overall response time can be approximated as :

This is a simplified model. Real implementations can overlap some operations through streaming, but the equation illustrates how multiple processing stages contribute to delay.

Even if every component is individually optimized, coordination overhead remains.

More importantly, the system may still be waiting for a conversational endpoint before deciding what to do next.

This leads to a broader question :

What if perception, reasoning, and generation could operate concurrently instead of waiting for one another to finish ⁉️

That is the architectural direction behind Griffin.

Introducing Griffin: From Conversational Models to Human Interaction Models

On October 1, 2026, Tavus introduced Griffin, describing it as its first Human Interaction Model.

The concept is interesting because it changes the unit of interaction.

Instead of treating conversation as a sequence of independent input-output operations, Griffin is designed to understand and generate face-to-face interaction continuously.

It combines audio and visual perception, conversational decision-making, and audiovisual generation into a full-duplex video-to-video system.

This means the model is designed to:

- Listen while speaking. - Interpret visual and auditory context. - Respond to interruptions. - Produce conversational acknowledgments. - Adjust its expressive behavior. - React to changes in the interaction. - Maintain awareness of pauses and conversational timing. 

The distinction between a conversational language model and a Human Interaction Model is important.

A language model primarily generates and processes language.

A Human Interaction Model attempts to coordinate language, perception, timing, and expressive behavior as parts of a continuous interaction.

Griffin's two major subsystems

Tavus describes Griffin through two interconnected architectural components.

A. Continuous Conversational Modeling (CCM)

This is responsible for perception, conversational intelligence, and interaction control.

It processes incoming audio and video, maintains conversational context, determines when and how to respond, and produces controls for audiovisual generation.

B. Audio-Visual Generation

This subsystem converts conversational decisions into synchronized speech and video.

It contains two major generation components:

- Streaming Speech Generation. - Streaming Video Generation. 

The conceptual architecture looks like this:

 ┌──────────────────────────────┐ │ Human Participant │ └──────────────────────────────┘ │ ┌─────────┴─────────┐ ▼ ▼ ┌────────────────────┐ ┌────────────────────┐ │ Incoming Audio │ │ Incoming Video │ └────────────────────┘ └────────────────────┘ │ │ └─────────┬─────────┘ ▼ ┌──────────────────────────────┐ │ Continuous Conversational │ │ Modeling │ └──────────────────────────────┘ │ ┌────────────┴────────────┐ ▲ ▲ ┌────────────────────┐ ┌────────────────────┐ │ Conversation │ │ System │ │ History │ │ Instructions │ └────────────────────┘ └────────────────────┘ │ ▼ ┌──────────────────────────────┐ │ Text and Speech Controls │ └──────────────────────────────┘ │ ▼ ┌──────────────────────────────┐ │ Nonverbal Controls │ └──────────────────────────────┘ │ ┌─────────┴─────────┐ ▼ ▼ ┌────────────────────┐ ┌────────────────────┐ │ Streaming Speech │ │ Streaming Video │ │ Generation │ │ Generation │ └────────────────────┘ └────────────────────┘ │ │ ▼ ▼ ┌────────────────────┐ ┌────────────────────┐ │ Audio Latents │ │ Video Latents │ └────────────────────┘ └────────────────────┘ │ │ └─────────┬─────────┘ ▼ ┌──────────────────────────────┐ │ Output Decoding │ └──────────────────────────────┘ │ ▼ ┌──────────────────────────────┐ │ Synchronized Audio and │ │ Video │ └──────────────────────────────┘ 

This is a conceptual representation of the publicly described architecture, not a reconstruction of Tavus's unpublished internal implementation.

The most important characteristic is concurrency.

Perception doesn't necessarily stop when the model starts speaking.

Generation doesn't need to wait until the entire user interaction has concluded.

Conversational decisions can evolve as new information arrives.

This is a different architectural philosophy from simply connecting a speech recognizer to an LLM and an avatar renderer.

Full-Duplex Interaction: Conversation as a Continuous State Machine

Let's examine one of the most important engineering challenges: turn-taking.

Traditional voice applications commonly use Voice Activity Detection (VAD), silence thresholds, and endpointing logic to estimate when someone has finished speaking.

A simplified implementation might look like this:

def detect_turn_end(speech_active, silence_duration): SILENCE_THRESHOLD = 0.8 if not speech_active and silence_duration > SILENCE_THRESHOLD: return True return False 

This is useful for basic voice interfaces.

However, it doesn't understand conversational intent.

A person can pause without finishing their thought.

They can acknowledge another speaker without surrendering the conversational floor.

They can interrupt because they need to correct something important.

These are not merely audio events. They are interaction events.

Modeling conversational states

One way to understand this challenge is through a state-machine abstraction.

A conversational application might maintain states such as:

- Listening. - Interpreting. - Responding. - Interrupted. - Backchanneling. - Idle. 

An illustrative state diagram :

 ┌──────────────────────────┐ │ Listening │ └──────────────────────────┘ │ ▼ ┌──────────────────────────┐ │ Interpreting │◄──────────────┐ └──────────────────────────┘ │ │ ▲ │ Response │ │ Continue │ decision │ │ listening │ ▼ │ │ ┌──────────────────────────┐ │ │ Responding │ │ └──────────────────────────┘ │ │ │ │ Response │ │ User │ complete │ │ interruption │ │ ▼ │ │ ┌──────────────────────────┐ │ │ │ Interrupted │ │ │ └──────────────────────────┘ │ │ │ │ │ │ Process │ │ │ new input │ │ └──────────────────┘ │ ▼ ┌──────────────────────────┐ │ Listening │ └──────────────────────────┘ ┌──────────────────────────┐ │ Interpreting │ └──────────────────────────┘ │ │ Short acknowledgment ▼ ┌──────────────────────────┐ │ Backchannel │ └──────────────────────────┘ │ │ Continue interaction ▼ ┌──────────────────────────┐ │ Listening │ └──────────────────────────┘ │ │ Session termination ▼ [End] 

This is an application-level abstraction, not Griffin's disclosed internal state machine.

Implementing an interaction controller in TypeScript

The following example demonstrates how a developer might model conversational transitions.

type ConversationState = | "listening" | "interpreting" | "responding" | "interrupted" | "backchannel"; type InteractionEvent = | { type: "speech_detected" } | { type: "response_ready" } | { type: "user_interrupted" } | { type: "acknowledgment" } | { type: "response_completed" }; function transition( state: ConversationState, event: InteractionEvent ): ConversationState { switch (event.type) { case "speech_detected": return "interpreting"; case "response_ready": return "responding"; case "user_interrupted": return "interrupted"; case "acknowledgment": return "backchannel"; case "response_completed": return "listening"; default: return state; } } 

This is intentionally simplified.

A production-grade implementation would need more sophisticated event handling, concurrency control, generation cancellation, timeout management, and state reconciliation.

Why interruption handling is a systems problem

Imagine an AI assistant is explaining a database migration.

Halfway through its response, the user says :

Wait, we're actually using PostgreSQL, not MySQL.

The system now has to :

- Detect the interruption. - Stop or invalidate the existing response. - Capture the new information. - Update conversational context. - Reconsider the response. - Resume with the corrected information. 

This is not simply a matter of generating another sentence.

It requires coordination between input processing, conversational state, output generation, and playback.

A useful engineering abstraction is a generation-version mechanism.

class GenerationCoordinator { private version = 0; async generate( generateResponse: () => Promise<string> ): Promise<string | null> { const currentVersion = ++this.version; const response = await generateResponse(); if (currentVersion !== this.version) { return null; } return response; } interrupt(): void { this.version++; } } 

The version counter ensures that an obsolete asynchronous result is not accepted after a newer interaction event.

This is a simplified cancellation guard, not a complete streaming cancellation implementation.

In a real system, the application must also cancel active generation tasks, manage playback buffers, and prevent stale media packets from reaching the user.

The deeper lesson: conversational responsiveness requires control over the lifecycle of generated output, not just the quality of the output itself.

Multimodal Perception: Audio Is Only Half the Conversation

Human communication carries information through much more than spoken language.

Consider the following signals :

- Facial expressions. - Eye gaze. - Body movement. - Vocal tone. - Conversational pauses. - Environmental context. - Objects and visual material shown to another participant. 

A person saying I understand while looking confused may communicate something different from someone saying the same words with an expression of confidence.

An audio-only system has limited access to this context.

A multimodal system can additionally process visual information.

Griffin is designed to perceive incoming audio and video together.

Tavus describes visual understanding involving gaze, facial expression, gestures, and environmental context.

Representing multimodal input

From a software engineering perspective, audio and video can be represented as independent streams associated through timestamps.

For example :

interface AudioFrame { timestamp: number; samples: Float32Array; sampleRate: number; } interface VideoFrame { timestamp: number; imageData: Uint8Array; width: number; height: number; } interface InteractionContext { sessionId: string; audio: AudioFrame[]; video: VideoFrame[]; } 

This is a generic application-level representation, not Griffin's internal data schema.

The important property is temporal alignment.

Audio and video events must be associated with the correct point in the interaction.

Otherwise, the system could incorrectly associate a facial expression with a different spoken statement.

Temporal synchronization

Suppose an audio event occurs at time

, and a corresponding video frame is captured at time

Their temporal difference is:

A system can use a synchronization tolerance to determine whether two events should be considered temporally associated.

However, real-time media introduces several complications:

- Camera capture delay. - Audio buffering. - Network jitter. - Clock drift. - Variable frame rates. - Packet loss. - Different processing speeds across modalities. 

A robust multimodal system therefore needs more than two independent input pipelines.

It needs a synchronization strategy that preserves the relationship between them.

A basic timestamp-aware synchronizer

Here's a simplified Python example :

from collections import deque class MediaSynchronizer: def __init__(self, tolerance_ms=50): self.audio = deque() self.video = deque() self.tolerance = tolerance_ms def add_audio(self, timestamp, data): self.audio.append((timestamp, data)) def add_video(self, timestamp, data): self.video.append((timestamp, data)) def get_aligned_pair(self): if not self.audio or not self.video: return None audio_ts, audio_data = self.audio[0] video_ts, video_data = self.video[0] difference = abs(audio_ts - video_ts) if difference <= self.tolerance: self.audio.popleft() self.video.popleft() return audio_data, video_data return None 

This example illustrates the principle, not a production synchronization algorithm.

A real implementation would need bounded queues, late-frame policies, clock normalization, jitter buffering, and careful handling of dropped packets.

The engineering challenge is to maintain temporal coherence without allowing buffering to introduce unacceptable interaction latency.

Continuous Conversational Modeling: The Intelligence Behind the Interaction

Perception alone is insufficient.

A system may recognize speech and detect visual events, but it still needs to decide what those events mean for the conversation.

This is where Griffin's Continuous Conversational Modeling engine becomes particularly interesting.

Tavus describes CCM as making conversational decisions at regular sub-second intervals rather than waiting for a complete conversational turn.

At each interval, the system evaluates the evolving interaction and determines what to communicate and how to communicate it.

Its controls influence both speech and nonverbal behavior.

For example, the system may decide to:

- Continue listening. - Produce a short acknowledgment. - Begin responding. - Yield the conversational floor. - React to an interruption. - Change expressive behavior. - Adjust its response based on visual context. 

Continuous state estimation

A useful conceptual model is to represent the interaction at time 𝑡 through a state vector:

Where:

represents incoming audio information.

represents incoming visual information.

represents relevant conversational history.

represents the current interaction state.

The system then produces an action:

Where,

represents a conversational decision.

These equations are explanatory abstractions, not disclosed Griffin model equations.

They illustrate the distinction between perception and action.

A model doesn't merely need to identify what happened.

It needs to determine what should happen next.

Why temporal understanding matters

Consider three situations :

Situation A :

The user pauses briefly while explaining a complex concept.

The system should probably continue listening.

Situation B :

The user says mm-hm while the AI is speaking.

The system should recognize that this may be a backchannel rather than an interruption.

Situation C :

The user says Wait, that's not what I meant`

The system should recognize a meaningful conversational interruption and adapt.

All three situations involve incoming audio.

Their conversational meanings are different.

This is why full-duplex conversational modeling is a more complicated problem than detecting whether speech is present.

The objective is not merely speech recognition. It is continuous interaction-state interpretation.

Streaming Speech Generation: Understanding Tavec

One of the technically interesting components in Griffin's published architecture is its streaming speech-generation pipeline.

Griffin-Lite uses an autoregressive diffusion transformer for speech generation.

Rather than waiting for an entire response to be generated, the system progressively produces speech as conversational controls arrive.

An important part of this architecture is Tavec, Tavus's continuous audio codec.

Understanding the continuous latent representation

Tavus describes Tavec v1.7 with the following characteristics:

Here is your table formatted neatly in Markdown, ready to paste into your DEV blog.

Unlike conventional discrete-token codecs that rely on codebooks, Tavec uses continuous latent representations.

The codec maps audio into a compact latent sequence that can be decoded incrementally.

Its conceptual pipeline looks like this:


┌──────────────────────────────────┐
│ 48 kHz Audio Waveform │
└──────────────────────────────────┘
│
▼
┌──────────────────────────────────┐
│ Convolutional Encoder │
└──────────────────────────────────┘
│
▼
┌──────────────────────────────────┐
│ │
│ Continuous Latent │
│ Representation │
│ │
└──────────────────────────────────┘
│
▼
┌──────────────────────────────────┐
│ Streaming Latent Chunks │
└──────────────────────────────────┘
│
▼
┌──────────────────────────────────┐
│ Causal Decoder │
└──────────────────────────────────┘
│
▼
┌──────────────────────────────────┐
│ Reconstructed Audio │
└──────────────────────────────────┘


Understanding the compression

Consider one minute of mono audio sampled at 48 kHz.

The total number of raw samples is:

Tavec's published representation uses 100 latent frames per second.

Therefore, one minute corresponds to:

latent frames.

With 40 values per frame:

continuous latent values.

This is a substantially more compact representation than the original sample sequence.

However, actual storage and transmission costs depend on numerical precision, metadata, framing, and implementation details.

Why causal decoding matters

A causal decoder processes available latent information without requiring future frames.

Conceptually:

Where:

is the current latent input.

is the previous decoder state.

is the reconstructed audio output.

The decoder carries state across chunks, allowing successive segments to produce a continuous waveform.

This is useful in streaming applications because playback can begin before the entire utterance has been generated.

An illustrative streaming decoder interface

The following Python abstraction demonstrates stateful chunk decoding.

It is not an implementation of Tavec.

`python
class StreamingAudioDecoder:

Source：https://dev.to/hemant_007/beyond-llms-engineering-real-time-human-interaction-with-tavus-griffin-9ea
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