Pain Points
01
The number of documents that the company needs to analyze and process is huge, and often involves various departments and multiple versions of historical data, and the format is quite different. Manual analysis is time-consuming and labor-intensive.
Pain Points
02
The information content in the document is complex, the data is messy, and there is a lack of unified structure and standards. The key information you want to extract is often scattered between the lines, requiring in-depth reading to understand the content.
Pain Points
03
The company's business is developing rapidly, and document analysis needs not only to process historical data, but also to respond to business changes in a timely manner. Smarter and automated solutions are needed.
Pain Points
04
Omissions are prone to occur when manually analyzing data, and errors are more likely to occur if the number of documents is huge. Some keyword matching can be done by using a relatively elementary rule algorithm, but it is difficult to achieve strong adaptability due to the inability to understand context semantics.
Based on Mengzi Financial Pre-training Model Technology
Langboat Technology uses a large amount of task data in the financial field to optimize the model for the financial field. It can better complete specific tasks in the financial field than pre-trained models in the general field, such as: identification of listed companies and bond-issuing entities, financial professional sentiment analysis, research report opinion extraction, financial document compliance review, etc.
Customizable Information Extraction Technology
It supports common information extraction tasks in the financial industry and can be highly customized according to customer needs.
Zero-shot and Few-shot Learning Technology Based on Large Models
By applying zero-shot learning techniques, benchmark performance can be quickly obtained without data labeling. For scenarios with higher precision requirements, only a small amount of labeled data is needed to obtain better analysis results. With the support of powerful Mengzi financial pre-training model technology, it can achieve strong scene generalization.
Through the pre-training model technology, it can provide full-process intelligent automation support for multiple document analysis scenarios. In special scenarios, it can also cooperate with manual operation to effectively improve work efficiency under the premise of perfectly guaranteeing the final result.
Effectively improve the omissions that are easy to occur in the analysis of a large amount of text content manually. Because it can understand the semantic environment of the context, it can also cover flexible language expression scenarios that cannot be well handled by keyword matching rule algorithms.
Based on the pre-training model technology and the special optimization of a large number of common task scenarios in the financial field, Langboat Technology's technical solutions have strong task generalization capabilities and can adapt to new content brought about by business changes to a certain extent. When the business content changes greatly, only small-scale maintenance is required to adapt to the new scene.
The Langboat Meeting Assistant can efficiently realize the functions of speech to text and multi-dimensional intelligent analysis of conference. Applicable to office meetings, teaching speeches, media interviews and other meeting scenarios,Provide deep analysis and value mining of meeting contents.
Providing various NLP capabilities with strong versatility in the financial industry through APIs.
Products
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