Pain Points
01
Enterprise knowledge is usually scattered and stored in various business systems, and mostly exists in different forms such as database, pdf, Word, etc., which cannot accurately capture the user's intention and accurately retrieve it.
Pain Points
02
Users need to read a lot of content to find the exact information, can not extract a concise summary or answer from a large number of documents, and the cost of tacit knowledge mining is high.
Pain Points
03
Based on the lack of deep understanding of natural language queries in traditional search methods, search results tend to lack accuracy and relevance. The query results are presented in the form of a list, and users need to click on the links one by one, which further increases the difficulty of finding information and affects the user experience.
Pain Points
04
Knowledge acquisition and management processes often require manual maintenance and updating processes that consume time and human resources, increasing management operational costs.
LLM+RAG technology is used to comprehensively improve the user's trust in search results to provide more accurate search results. At the same time, self-developed autonomous rejection and hallucination detection modules ensure that the answer information is true, accurate and based on actual facts. Through the fine sorting calculation of the retrieved results, more accurate answer output is realized.
Through interactive direct questions and answers in natural language, multiple rounds of dialogue can be conducted according to the contextual context, thus enabling in-depth exploration of complex issues. Have the ability to distill and summarize information in this process, and be able to trace the source of the answer.
The system supports users to upload documents with one click, and can complete knowledge construction automatically. Through the self-developed multi-grained hierarchy cutting technology, the system can intelligently and automatically divide the document. In addition, the system also provides document semantic structure analysis, including OCR support, to realize the intelligent processing of documents.
Through friendly natural language interaction, realize search, question and answer, knowledge induction, recommendation, etc., help users accurately obtain effective information, save a lot of document retrieval costs, improve work and learning efficiency.
Intelligently complete knowledge induction, refining and summary, promote the explicitness of tacit knowledge, give full play to the value of knowledge, and empower organizations to be more intelligent and efficient.
Loosely coupled enterprise knowledge base is built based on LLM+full stack retrieval enhancement system to realize optimal solution in terms of efficiency, accuracy and deployment cost.
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.
Ask any question about the uploaded documents with brand new and smoother office working experience.
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