NetEase Zhichi has disclosed that medical aesthetics consultants typically spend three hours compiling client communication materials. After integrating the “Emperor Crab” AI Agent platform, they can complete the task in roughly three minutes. We believe the real significance of these figures lies not in speed alone, but in enterprises beginning to use AI for fixed, verifiable workflows.
What This Is
This solution targets medical aesthetics consultations. The AI Agent—an AI system capable of autonomously invoking tools and completing tasks in stages—extracts information from skin-assessment reports stored on devices, CRM spending records and store treatment materials. After cross-checking the data, it asks follow-up questions about budget, needs and outstanding details, then generates a communication card covering treatment direction, combinations, sequencing, rationale, boundaries and alternatives.
The final card still has to be reviewed by staff and professionally verified, while doctors determine the specific diagnosis and treatment arrangements. AI handles retrieval, comparison, questioning and first-draft preparation rather than making decisions for clients or doctors.
Industry View
What is commendable about this workflow is that it does not aim to have the model deliver a treatment plan directly. Instead, it first checks whether the source materials are complete and builds “rationale” and “boundaries” into a standardized output. When enterprises deploy AI, generation capability is often not the scarce resource; traceable processes, reliable data and clear accountability for human review are.
The risks are equally clear: three minutes reflects only the document-preparation stage. It cannot be extrapolated to the duration of a complete consultation, much less prove the quality of the service. Incomplete skin-assessment descriptions, outdated store service catalogs or missing historical records can make a first draft look fast but unusable. In addition, medical and medical aesthetics data are highly sensitive, so customer authorization, data storage and platform integration methods require separate scrutiny.
Impact on Regular People
For enterprise IT:Before discussing automation rates, enterprises adopting AI should first clean up their data and define boundaries and review checkpoints. Data governance generally has a greater impact on the final outcome than model selection.
For careers:Jobs involving information retrieval, first-draft preparation and the execution of fixed workflows will shrink, while the ability to verify information, identify gaps and assume final responsibility will become more important.
For the consumer market:Consumers may receive structured consultation plans faster, but they will also pay closer attention to information sources, applicability limits and who reviewed the recommendation.