Grasp customer preferences through multiple rounds of dialogue and contextual understanding to recommend products in a targeted manner. Automatically note customer information, freeing customer service from repetitive work. Determine the real cause of after-sales problems through multiple conditions and automatically create tickets.

The bot automatically records contextual information during the chat process. When the user asks another question related to the information, it automatically completes the recorded information and gives a more accurate answer, making the bot interaction more natural and smooth.

Abandon the traditional grammar model, implement automatic learning from chat records, generate knowledge base, reduce the cost of manual preparation of knowledge points. Through intelligent cleaning, abnormal questions and similar questions in the knowledge base are automatically detected and processed to improve the accuracy of the bot.

After using a variety of pre-trained models for transfer learning, and setting different answers for a knowledge point, the bot will give relevant answers and explanations according to the specific circumstances of the user's query.

Large groups can create different bots according to departmental and business requirements to serve users in different channels and scenarios. Bots share the basic knowledge base, and departments can share the work to maintain the knowledge base.

By drawing business logic in the system, it can independently customize multiple rounds of sessions to achieve smooth interaction effects, supporting complex logic such as interfacing with business interfaces, entering multiple pieces of information at once, interrupting, modifying, etc.