China AI Restaurants Use Face, Tongue Scans to Personalize Meals

Restaurants in Hangzhou, eastern China, are deploying artificial intelligence-powered systems that scan customers’ faces and tongues to recommend personalized meals, while robots handle up to 60% of kitchen operations, according to local media reports. The model is already operational across multiple outlets and is being positioned as a cost-efficiency and standardization tool.

At least three AI-enabled restaurants have been operating for several months in Zhejiang province, with one of the most prominent examples being 24 Jieqi Robot Restaurant in the Xihu district. The outlet uses eight robots to manage key functions including ordering, cooking, serving, and cleaning, significantly reducing reliance on human labor during peak hours.

Robots Handle 60% of Kitchen Operations

According to operational data shared by the restaurant, robotic systems are currently responsible for approximately 60% of total kitchen workload. These machines are capable of preparing more than 100 dishes, covering a range of Chinese cuisine formats, including stir-fried, braised, and soup-based items.

Menu items produced by the robots include traditional dishes such as Three Cup Chicken, Crab Roe Tofu, Braised Pork Trotter, and Pian Er Chuan, a regional noodle soup specialty from Hangzhou. The automation is particularly suited to fast food formats, group meals, and high-volume cuisine styles such as Hunan and Sichuan dishes.

The robots operate using programmed cooking sequences that replicate human chef techniques, enabling consistent output across dishes. This allows kitchens to maintain throughput during high-demand periods while minimizing manual intervention.

Biometric Scanning Used for Menu Recommendations

A key feature of the system is its use of AI-driven customer analysis prior to ordering. The technology scans diners’ facial features and tongues and combines this data with responses from a short questionnaire. Based on this input, the system generates a profile that includes indicators related to lifestyle, emotional state, and digestive condition.

Using this profile, the AI recommends menu items aligned with perceived health needs and seasonal considerations. The approach reflects a growing intersection between food service and health-oriented personalization, where menu curation is guided by individual data rather than static offerings.

The implementation introduces a data-driven layer to dining, positioning the restaurant experience beyond traditional menu browsing toward algorithm-led decision-making.

Technology Replicates Chef Techniques

The robotic cooking systems have been developed to learn directly from human chefs. According to engineers involved in the project, the machines capture data on stove temperatures, timing, and movement patterns during food preparation.

This information is then used to replicate cooking actions such as stir-frying and pan handling with a high degree of consistency. The goal is to standardize dish quality regardless of volume, reducing variability that typically occurs in manual kitchen environments.

For kitchen staff, the introduction of robotics has shifted roles from direct cooking to supervision and system management. In some cases, chefs now oversee multiple machines simultaneously rather than preparing dishes individually.

Impact on Pricing and Labor Structure

The adoption of robotic systems has also had an impact on operational costs and pricing strategies. According to customer feedback, meals produced in these AI-driven environments are priced lower compared to traditional restaurant formats, suggesting cost savings from reduced labor requirements.

At community canteens using similar systems, chefs who previously prepared dozens of dishes during peak service now manage robotic units instead. This shift indicates a restructuring of kitchen labor, where fewer staff are required for production while technical oversight roles increase.

Customers have reported that the quality of food remains consistent with traditionally prepared meals, with some noting that dishes are balanced in taste and suitable for specific dietary preferences, including lower oil and salt content.

Public Response and Industry Context

The expansion of AI-driven restaurants in China has generated mixed reactions online. While some consumers and industry observers view the technology as a step toward efficiency and innovation, others have raised concerns about the reduced role of human chefs and the broader implications for employment in the food service sector.

The model aligns with wider automation trends in the hospitality industry, where operators are increasingly adopting technology to address labor shortages, control costs, and maintain consistency. China has emerged as a key market for such experimentation, particularly in high-density urban centers where scalability is critical.

As of now, AI restaurant formats remain concentrated in specific regions such as Hangzhou, but their continued adoption suggests potential for broader rollout. The integration of biometric analysis and robotic cooking indicates a shift toward data-centric dining models that combine food production with personalized customer insights.