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Solution Introduction

Intelligent Customer Service Solution

The Langboat Intelligent Customer Service Solution harnesses the industry-leading Mengzi large-scale model, focusing on a comprehensive upgrade of intelligent customer service scenarios. It seamlessly integrates with vast textual knowledge from enterprises, utilizing the large model to accurately generate answers, swiftly learn enterprise knowledge, and enhance vertical domain understanding. By leveraging the large model to enhance complex semantic comprehension, it delivers precise responses to users, bringing the interactive experience closer to that of human interaction. It can be flexibly applied to both customer service assistants and robots, improving construction efficiency and significantly enhancing user experience.

Business Scenarios and Pain Points

Pain Points

01

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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

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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

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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.

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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.

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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.

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Business Value

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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.

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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.

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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

The online customer service of a certain enterprise group

Providing intelligent customer service LLM (Large Language Model) technology services for a major enterprise, in collaboration to conduct LLM technology validation for scenarios involving intelligent online chatbots/digital virtual assistants, aiming to achieve more efficient and intelligent customer service. Intending to improve intent recognition accuracy by 5% and increase problem resolution rates by 14%.

Solution Experts Can Demonstrate Products Remotely or on Site

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.

Langboat Smart Knowledge Base

Provide intelligent AI search, AI-assisted writing, and other functions to help enterprises rapidly build their own secure and reliable knowledge mid-platform.

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.

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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:

ewm

© 2023, Langboat Co., Limited. All rights reserved.

Large Model Registration Code:Beijing-MengZiGPT-20231205

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