Packing LLM capabilities into MCU / SoC-class hardware β a complete pipeline from training to edge real-time inference
Scenario data collection and labeling; dedicated model training
Pruning & INT8/INT4 quantization β tens of times smaller
Knowledge distillation preserves accuracy; small models approach LLM quality
Operator adaptation & memory optimization for fast cross-platform deployment
Millisecond response, offline-capable, privacy stays on-device
Covering every perception and decision need of companionship scenarios
Offline wake-up Β· ASR Β· TTS Β· voiceprint & voice cloning β the full audio chain on the edge
Face / gesture / pet recognition Β· object detection Β· SLAM navigation on local NPU
On-device intent understanding Β· distilled small LLMs Β· local command parsing
Motion control Β· posture balance Β· gait planning with millisecond response
Core barrier β phoneme fuzzy matching and fully self-developed differentiated small models: flagship LLM experience at edge hardware cost.