Intelligent Enterprise Knowledge Base Solution
Intelligent Customer Service Solution
Chat BI SolutionIntelligent Customer Service Solution
Business Scenarios and Pain Points
Pain Points
01
Traditional intelligent customer service lacks the ability to understand complex semantics and context
Intelligent customer service performs poorly when dealing with complex contexts and semantics, unable to accurately grasp user intentions and situations, leading to responses deviating from the actual questions, lacking specificity and depth. Due to the lack of comprehensive understanding of semantics, intelligent customer service fails to deliver high-quality interaction experiences, diminishing customer trust and satisfaction.
Pain Points
02
Traditional Intelligent Customer Service Provides Unintelligent and Stale Responses
Customers often complain that responses from intelligent customer service agents do not match their actual needs, appearing rigid and repetitive, lacking flexible semantic understanding and context adaptation. Due to the limited knowledge boundaries of intelligent customer service, it cannot answer questions outside the knowledge base, resulting in poor effectiveness during multi-turn interactions, thus impacting customer usage experience and satisfaction.
Pain Points
03
Lengthy and Lack of Self-Learning in Knowledge Organization and Training
Backend operators spend a considerable amount of time organizing and training the knowledge base, with existing systems lacking self-learning and optimization capabilities. Training in complex dialogue scenarios is difficult, increasing the workload of operators and making knowledge organization and training inefficient.
Intelligent Customer Service Solution
Based on the advanced semantic understanding capability of the MzGPT
Fully understanding human language and emotions to improve conversational effectiveness
By utilizing LLM (Large Language Model) technology to deeply understand human language and emotions, it significantly enhances the accuracy of understanding complex semantics. This enables a more human-like interactive experience with users, effectively improving the problem-solving rate of customer service and enhancing customer satisfaction.
RAG-based retrieval enhancement technology
Novel conversational retrieval, enhancing response efficiency
Integrating with vast enterprise knowledge, rapidly learning, and accurately delivering high-quality content, breaking free from the traditional FAQ-based rule-matching approach of chatbots. Instead, directly learning from enterprise document repositories and existing resources of search engines, retrieving precise answers from enterprise-grade knowledge bases, assisting chatbots in generating accurate results, enhancing the model's ability to recognize irrelevant search results, and providing precise answers, thereby resolving issues caused by excessive noise leading to incorrect responses.
Zero-shot and few-shot learning techniques based on LLM
Maximizing reduction in knowledge operation costs
Based on document-based question generation, easily construct FAQs, without the need to organize similar questions. Utilizing the Mengzi large-scale model, knowledge operation costs are significantly reduced.
Business Value
Improve Customer Service Dialogue Effectiveness
On the customer side, leveraging the advanced semantic understanding and human emotion recognition capabilities of the MzGPT, the robot reception has been significantly enhanced with a more personified approach, resulting in an increase of over 80% in solving complex issues.
Achieve Cost Reduction and Efficiency Enhancement for Enterprises
On the agent side, aiming to enhance the service quality of customer service agents, we have developed intelligent application scenarios by leveraging RAG retrieval enhancement technology in conjunction with enterprise knowledge bases. Through this innovative technology, high-quality Copilots are provided to customer service agents, empowering them to better handle customer inquiries and accumulate more conversational value.
Enhance Backend Knowledge Operations Efficiency
On the operations side, leveraging natural language text mining technology significantly improves knowledge production efficiency, resulting in a 70% reduction in dialogue construction costs.
Application Cases
Related Products
Mengzi Models
Langboat's in-house developed large language model, capable of handling multilingual, multimodal data, and supporting various text understanding and text generation tasks. It can rapidly meet the requirements of different domains and application scenarios.
AI Document Chat
Ask any question about the uploaded documents with brand new and smoother office working experience.
AI Search
Equipped with the advanced AI technology of Mengzi Large Language Model, it assists users in extracting knowledge from massive real-time information and discovering new realms of knowledge.
Business Cooperation Email
Address
Floor 16, Fangzheng International Building, No. 52 Beisihuan West Road, Haidian District, Beijing, China.
© 2023, Langboat Co., Limited. All rights reserved.
Large Model Registration Code：Beijing-MengZiGPT-20231205
Business Cooperation:
bd@langboat.com
Address:
Floor 16, Fangzheng International Building, No. 52 Beisihuan West Road, Haidian District, Beijing, China.
Official Accounts:
© 2023, Langboat Co., Limited. All rights reserved.
Large Model Registration Code：Beijing-MengZiGPT-20231205
