Pain Points
01
In traditional data analysis, business report developers are often required to write SQL or rely on specialized data analysis tools. However, the skill threshold in this process becomes a barrier, especially for non-technical roles such as corporate decision makers and business people.
Pain Points
02
The efficiency of querying data indicators is limited by the fixed query fields and combination methods provided by traditional methods, which lack flexibility, especially when responding to temporary or urgent data analysis needs, and cannot achieve rapid response.
Pain Points
03
Traditional data methods are usually passive analysis, which cannot effectively explore and analyze actively, and cannot meet the deeper exploration of data by business personnel.
Pain Points
04
Enterprise metrics/data are often scattered across multiple business databases, resulting in fragmented information and inefficient query analysis. At the same time, the lack of suitable tools for team collaboration and knowledge sharing hinders the effective precipitation of enterprise knowledge.
The user can use natural language input, the product can understand the user's instructions and the meaning behind them, and according to the context, give interpretable answers, which are automatically presented in visual reports.
Users can explore and use data of different dimensions or that they cannot pay attention to more autonomously. Support multi-round Q&A, four-rule operation, aggregate query, sorting, multi-indicator, multi-dimensional mixed query, etc.
Support custom scene kanban, one-click report generation. At the same time, based on the user's search behavior to precipitate and share, different user knowledge to achieve crossover and learning.
Zero code based on LLM, no drag and drop, subverting the traditional way of interacting with data analysis. 'Generate reports in one sentence'.
Taking advantage of the platform's centralized search traffic, the search behavior of system users can be precipitated and shared, making the system smarter and smarter.
Because of the lower barriers to use of the platform, users explore and use data in different dimensions and make decisions about things in their own hands.
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.
Provide intelligent AI search, AI-assisted writing, and other functions to help enterprises rapidly build their own secure and reliable knowledge mid-platform.
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.
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