How it Works
- Communication Protocol (WebRTC) - Provides realtime two-way audio and video streaming. This layer captures raw microphone and camera input directly from the user and transports it to Trugen’s inference engine with minimal latency. It also streams back rendered video avatar output in realtime, enabling fluid face-to-face interaction.
- STT (Speech-to-Text) - Converts the user’s spoken audio into text instantly using advanced streaming transcription. STT runs continuously and incrementally to capture partial and final utterances, allowing early reasoning before the user finishes speaking.
- Turn Detection - Detects natural conversation boundaries such as pauses, interruptions, and handovers. This enables the agent to know when to listen, when to speak, and how to gracefully interrupt or yield-replicating natural human conversational flow.
- LLM (Language Model) - Generates intelligent, contextually relevant responses using large language models powered by your preferred provider. The LLM understands conversation memory, tone, context, and intent to produce meaningful and personalized responses.
- Knowledge Base Integration - Enhances reasoning with structured and unstructured organizational knowledge including documents, FAQs, APIs, databases, and custom content, ensuring answers are factual, brand-aligned, and grounded in real data instead of generic assumptions.
- TTS (Text-to-Speech) - Converts the generated response into natural, expressive speech. TTS models produce high-quality voice output in realtime, supporting multiple languages, tones, and emotional expressions.
- Avatar Video Generator (Huma-01) - Generates expressive video frames synchronized with speech. Using Trugen’s Huma-01 neural avatar model, the system produces realistic facial expressions, micro-expressions, lip sync, gaze direction, and emotional nuance-resulting in human-like communication.
This tightly optimized pipeline enables sub-second agent reactions, face-to-face realism, and natural conversational dynamics unmatched by traditional chatbot systems.
STT, LLM, TTS, and Knowledge Base modules are agent-agnostic, meaning each agent can choose its own model provider, configuration, or customization independently.