Seeds | Zhongke Xinzhi Closes Angel Round

Gasgoo Munich- Gasgoo Embodied Intelligence has learned that Beijing Zhongke Xinzhi Embodied Intelligent Technology Co., Ltd. has officially closed an angel round. The financing was led exclusively by Shanghai Xinzhiyan Enterprise Management Partnership (Limited Partnership), with Wanchuang Investment Bank serving as the sole financial advisor.

The company plans to channel the funds into three key areas: commercializing smart hardware, scaling computing power and training iterations for its multimodal emotion-sensing large model, and building infrastructure for standardized sensing modules and computing clusters.

Founded in February 2026, Zhongke Xinzhi is a Chinese tech firm focused on developing and industrializing embodied intelligence emotion-sensing large models. The company aims to build an "emotional and social cognitive infrastructure" for the next generation of embodied intelligence—enabling AI to go beyond task execution by understanding emotions, sensing states, and fostering long-term human-machine interactions. This mission drives the industrial deployment of emotional AI across healthcare, education, elderly care, smart wearables, and human-computer interaction.

Image Credit: Zhongke Xinzhi

To that end, Zhongke Xinzhi has developed a cross-modal emotion-sensing large model that fuses four dimensions: vision, voice, behavioral timing, and physiological signals. Leveraging Self-Attention cross-modal alignment and clinical knowledge graphs, the system outputs clinical-grade "empathy indices" and multi-dimensional mental state vectors—moving beyond simple "happy/sad" labels.

The company's model boasts an emotion recognition accuracy rate exceeding 92%, with early screening sensitivity for ADHD (Attention Deficit Hyperactivity Disorder) surpassing 89% and specificity over 91%. Underpinning this performance is K_med, a million-scale clinical emotion cognitive graph built through deep strategic research collaboration with the Shanghai Mental Health Center. Covering mental health data across all age groups and spectrums, it creates a data moat that pure-vision competitors find difficult to cross.

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