Pain Points
01
Traditional investment research databases have completed the collection and sorting of unstructured data such as research reports, minutes, information and announcements, but lack of knowledge sorting and analysis based on these data to form professional depth in the investment research field.
Pain Points
02
The daily update of a large number of announcements and research reports, and the traditional search based on keyword matching make it difficult to grasp the true intention of users, effectively draw on external data, and accurately grasp the trend of investment research.
Pain Points
03
The desk documents are of various formats, the reference content is numerous and complex, and it takes a long time to organize and present, and the weekly and monthly reports, comments, investment research reports, etc. cannot be automatically generated, resulting in insufficient timely feedback of investment research information.
Pain Points
04
In the traditional Q&A interactive process of investment research, users' questions cannot be accurately understood, and questions about the depth of knowledge in the field of investment research cannot be filled or rewritten, resulting in low interaction efficiency and poor user experience.
Based on the Mengzi pre-training model technology system, through the study of a large number of professional basic corpus in the financial field, Q&A pairs with in-depth knowledge in the investment research field are automatically extracted for data such as research reports, announcements and public opinions, so as to enrich and expand the investment research information database.
Mchat combined with retrieval enhancement technology, through strong text parsing ability to build multi-document aggregation knowledge base; Self-developed embedded model and pre-trained ranking model to improve the accuracy and authenticity of Q&A.
Train on financial vertical data such as financial reports and research reports to further strengthen the vertical writing ability of Mengzi pre-trained models. Based on user-provided materials and custom writing templates, reduce illusions, fully follow the writing requirements, and align the writing specifications.
Through detailed analysis of investment research business concerns and business knowledge, the Bank gradually enhances the investment research knowledge accumulation of large models, and automatically generates questions with business depth to facilitate users to quickly analyze and ask questions and acquire investment research knowledge.
For closed-domain documents, it provides investment research analysts, researchers, product managers, etc. with always-on investment research analysis assistant, helping users quickly grasp the gist of the documents, achieve quick knowledge ingestion, and improve work efficiency.
Strengthen the understanding of knowledge in the investment research field, precipitate the standardized reporting system, quickly retrieve and integrate the fragmented information in the designated documents, and realize the rapid generation and processing of content oriented to business needs according to the customized report template.
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.
Providing various NLP capabilities with strong versatility in the financial industry through APIs.
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Large Model Registration Code:Beijing-MengZiGPT-20231205
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Floor 16, Fangzheng International Building, No. 52 Beisihuan West Road, Haidian District, Beijing, China.
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© 2023, Langboat Co., Limited. All rights reserved.
Large Model Registration Code:Beijing-MengZiGPT-20231205