Product Overview

The real-time interactive digital human platform serves a wide range of industry application scenarios, building a real-time digital human capability system that combines a natural interaction experience with outstanding expressiveness.

Through multimodal information intake and multi-dimensional intent recognition, the system continuously perceives user state and scenario context throughout a conversation, and combines scenario-based persona strategies to orchestrate language, motion, and facial expressions into a unified, coordinated output:

• Multimodal understanding driving multimodal coordination: Language expression, facial expressions, and body movements are scheduled in a unified manner, so that semantics, tone, and behavioral expression stay naturally consistent, while interaction rhythm, response timing, and expressive intensity are dynamically adjusted according to the real-time interaction state. How a conversation advances and concludes is jointly determined by user state and scenario context, moving beyond fixed scripted playback.
• Persona-driven naturalized content generation: Based on the preset persona, interaction scenario, and user state, interaction content that fits the character identity and expression style is dynamically generated; context coherence, information validity, and expressive appeal are all taken into account, ensuring responses are natural, accurate, and well-targeted while avoiding persona drift, empty content, and mechanical repetition.

Based on the above capabilities, the platform delivers a real-time interactive experience with a natural atmosphere, substantial content, and vivid expression, enabling digital humans to reach or surpass the comprehensive interaction performance of real people in specific scenarios, and providing a deeply closed-loop digital human interaction foundation for ToB applications across all industries.

• Real-time natural language interaction
  • Answer on demand, interruptions handled with ease: The digital human understands questions users ask in everyday language and can converse as easily as in natural chat. Users can add information midway, change the direction of a question, or directly interrupt the current answer, and the digital human continues the conversation based on the new content. It understands every kind of expression and handles every interruption. Multilingual understanding makes the conversation as natural as talking with a real person.
  • Application scenario: In a product explanation scenario, while the digital human is introducing a product, a user suddenly asks, "Do you have a model suitable for elderly users?" The digital human pauses the original explanation and answers the new question first.
• Human-like emotional feedback
  • Reading joy and anger, responding with empathy: The digital human adjusts its way of expressing itself according to the content and emotion of what the user says, and its expressions and movements change accordingly, such as frowning, nodding, and thinking.
  • Application scenario: A user says, "I'm not in a good mood today, can we talk?" The digital human shows a worried frown and speaks comforting words.
• Second-level low-latency response
  • What you see is what you get, no waiting in conversation: While the user is speaking, the system receives information in a streaming manner and quickly completes speech recognition, content understanding, and answer generation. The digital human can begin responding after a pause of about a second, so users do not have to wait long, making the interaction as natural and smooth as talking with a real person.
  • Application scenario: The system receives information while the user is still speaking, so a complete answer can start immediately once the user finishes.
• Real-time multimodal generation
  • Synchronized audio and visuals, expressions and movements seamlessly integrated: The digital human not only understands the content of the customer's conversation, but also visually understands the customer's environment and scenario. It can respond with voice, facial expressions, and body movements, and can also provide real-time subtitles.
  • Application scenario: While speaking, a user holds up a sweater they knitted and asks whether it looks good. The digital human can answer, "This red sweater looks great, you knit beautifully," and raise a hand to give a thumbs-up.
• Long-term memory
  • Recognizing people across sessions, understanding you better the more you talk: The digital human retains necessary information from previous conversations, such as the user's name, interests, preferences, or an unfinished topic from last time. When the user returns, the conversation can continue from where it left off instead of reintroducing background information.
  • Application scenario: A user talked about liking a certain singer last week; in today's conversation, the digital human can say, "This show also featured that singer you like."
• Flexible deployment and integration
  • Fast deployment, integration anywhere: Supports cloud rendering and on-device rendering, supports full-chain integration of ASR, LLM, and TTS, and can be integrated via Android SDK, iOS SDK, Web SDK, and other methods.
  • Application scenario: An enterprise connects an internal knowledge base through the API, and the digital human can answer questions about product prices, inventory, and promotion rules that are updated in real time.