Combine user's constitution, health check results, and exercise habits to use algorithms to generate personalized dietary plans. Precisely calculate the daily required calories and nutrient ratios, recommend specific ingredients and recipes, and dynamically adjust nutrient intake, truly achieving "one person, one plan" scientific and precise supplementation.

Go beyond the scope of single diet and provide comprehensive health solutions covering sleep management, stress regulation, and scientific exercise. Based on behavioral data analysis, formulate executable daily micro-habit plans, guide users to reshape their life rhythm, and improve their physical functions and quality of life from all aspects.

Intelligently identify health check data and deeply interpret abnormal items such as blood routine and biochemical indicators, as well as their potential health risks. Convert obscure medical terms into plain language, correlate dietary habits to provide targeted warnings, allowing users to clearly understand their physical condition and no longer be confused and at a loss when facing the report.

Build a professional nutrition knowledge base and provide immediate answers to questions regarding macronutrients, trace elements, and dietary misconceptions. Use scientific evidence to dispel rumors and offer authoritative, easy-to-understand dietary guidance, helping users establish correct dietary concepts and easily grasp the core logic of healthy eating.

A well-known nutrition and health enterprise has collaborated with Chuangzhi to implement the Xiaoyizhi Ge AWS cloud deployment project, aiming to address the shortcomings of insufficient personalization in traditional nutrition guidance, lack of user expertise, and broken product conversion links. The project aims to create a full-chain AI nutritionist service platform covering nutrition consultation, solution recommendation, and product conversion, serving both C-end consumers and B-end channel partners.

The project is built based on the AWS cloud ecosystem, leveraging Amazon EC2 GPU/TPU instances to provide elastic computing power, and Auto Scaling to adapt to the peak demand of user consultations. Through Amazon SageMaker to host and fine-tune large models, combined with OpenSearch Service to achieve efficient retrieval of nutrition knowledge bases, the accuracy rate of question answering is ensured at 98%. Using Amazon S3 to encrypt store physical examination reports and nutrition knowledge documents, combined with VPC private networks to build a security system, the privacy of users' health data is protected. By leveraging Amazon RDS to manage user archives and product data, precise nutrition recommendations are achieved, with an adaptation rate of 95%.

After the project was implemented, the product conversion rate of the enterprise increased to 40%, the consumer churn rate was significantly reduced, and at the same time, through the accumulation of user data, it provided feedback to marketing and operation, achieving precise customer acquisition, becoming a typical cloud deployment practice of AI empowerment of commercial transformation in the nutrition and health industry.