No expensive labeling or retraining — transfer cross-domain knowledge so any model gains professional domain capabilities fast
Task structures across industries are highly similar — that is the foundation of knowledge transfer
Spot tiny anomalies in images and assess region and severity
Identify manufacturing defects and map patterns to process causes
Infer failure risk from sensor anomalies and historical cases
Judge causality, liability boundaries, and risk from multi-source evidence
Not training from scratch — inject industry capability into existing models
High-quality labeled samples and reasoning chains across industries and tasks — the industry knowledge foundation
Knowledge Graph + neuro-symbolic retrieval to match similarity in task structure, reasoning chains, and decision logic
Fuse cross-domain samples and reasoning chains; inject into the target model via Input-level or Network-level modes
Let small models handle professional tasks on domestic compute — no dependency on costly large-model training
Boost industry performance of general LLMs without large labeled datasets or retraining
Replace costly labeling and retraining with knowledge transfer — enhance with plugins instead of building industry models from scratch