AI Interaction Technology
AI Interaction Technology

Robotics ODM Technology Stack / AI Interaction Technology

Robots that see, hear, and respond like they understand you.

AI interaction technology gives robots the ability to perceive, understand, and respond naturally — fusing multimodal perception with large language models so your robot feels genuinely responsive, not scripted.

Core Capabilities
5 Modules
01
Multimodal perception fusion
Visual and audio-visual sensing
02
LLM semantic understanding
Flexible mainstream model integration
03
ASR speech recognition
Multi-language, dialect-aware
04
Far-field voice capture
Mic array + beamforming
05
On-device AI inference
Edge-optimized, low latency

What Is AI Interaction Technology

The stack that separates a moving machine from a true companion.

AI interaction is the stack that lets a robot understand its user and its surroundings — seeing what is in front of it, hearing what is said, and responding in a way that feels natural. It combines multimodal perception (vision and speech), large language model (LLM) semantic understanding, and on-device inference with cloud-edge collaboration. For pet, home, and service robots, this is what turns hardware into a genuine companion.

Core Capabilities

Five capabilities that make interaction feel natural.

01

Multimodal perception fusion

The robot sees and hears together, not separately. Visual capture, object recognition, and audio-visual fusion let it read both the environment and the user's state — knowing not just that someone is nearby, but what the situation looks like.

02

LLM semantic understanding

Intent recognition powered by large language models, with flexible integration of mainstream models. The robot understands what you mean, not just what you said, and can adapt as models improve.

03

ASR speech recognition

High-accuracy speech-to-text with multi-language and dialect support, so the robot understands users across languages and accents without a rigid command vocabulary.

04

Far-field voice capture

A microphone array with beamforming and echo cancellation picks up wake words and commands reliably, even in noisy rooms — so the robot responds the first time, not the third.

05

On-device AI inference

Models optimized for edge compute run locally, cutting latency and reducing cloud dependence for privacy-sensitive interactions — a balance of speed, cost, and data control.

Why This Approach Works

Balancing responsiveness, privacy, and capability.

The difference between a machine and a companion is how it interacts. Our approach balances three competing needs: responsiveness (on-device inference keeps latency low), privacy (local processing where it matters), and capability (cloud-edge collaboration taps larger models when needed). This is how a robot can feel natural to talk to without shipping every word to the cloud. With 14 years of OEM/ODM experience and a 90+ engineer R&D team, we have built AI products for nearly 100 million households across 60+ countries.

How It Works

From interaction design to natural conversation in four steps.

1
Interaction designWe define the interaction model: what the robot sees, hears, and how it responds.
2
Perception & speech integrationWe integrate vision, microphone array, ASR, and wake-word detection.
3
LLM & semantic layerWe connect intent recognition to mainstream LLMs for natural conversation.
4
On-device optimizationWe deploy and optimize models for edge hardware, balancing speed, privacy, and cost.

Use Cases

Where this solution delivers.

Companionship products

A differentiated, natural interaction experience that sets your product apart from scripted toys.

Pet, home & industry robots

One interaction layer that adapts across pet, home, and industrial scenarios.

AI-first teams

Rapid LLM integration and iteration, so your product keeps improving as models advance.

Frequently Asked Questions
What is multimodal interaction?

It is when a robot combines multiple inputs — vision, speech, and other sensors — to understand its environment and user, rather than relying on a single signal.

What role does an LLM play in a robot?

The LLM handles semantic understanding and intent recognition, so the robot can respond naturally to what a user means, not just scripted commands.

How does far-field voice capture work?

A microphone array with beamforming focuses on the speaker while echo cancellation removes background noise, so the robot can hear commands reliably in noisy rooms.

Can you integrate with a specific LLM?

Yes. We support flexible integration with mainstream large models, so you can use the model that fits your market, language, and cost requirements.

Make your robot feel genuinely responsive.

If you want your robot to see, hear, and respond naturally, we can help you design the perception, speech, and LLM interaction stack that fits your product.

Build Smarter Interaction

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