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BI Assistant for creating analytical dashboards and automated data analysis
The accuracy of the analysis increased
by
by
Report creation time reduced
30%
4,5
times
Customer
A major metrology company in Russia
CHALLENGES/FEATURES
High demands on the quality of input data when generating queries into the system;
Complexity of implementing the processing of specific queries taking into account the characteristics of the subject area;
Need to develop a user-friendly interface for users without deep technical knowledge;
Requirements for minimizing errors and increasing the speed of data processing;
Limited compatibility of existing BI tools with large language models (LLM)
Complexity of implementing the processing of specific queries taking into account the characteristics of the subject area;
Need to develop a user-friendly interface for users without deep technical knowledge;
Requirements for minimizing errors and increasing the speed of data processing;
Limited compatibility of existing BI tools with large language models (LLM)
Automate the process of creating analytical reports and dashboards to reduce reliance on manual labor and speed up the data analysis process
Task
Technical solution
1. An analysis of existing BI solutions using large language models (LLM) was conducted. A BI Assistant prototype was developed based on an open source project.
2. The system architecture was developed:
User interface: an intuitive interface for entering queries, defining relationships between data, and integrating data sources.
AI module: query processing using a vector database for contextual search and LLM direction for accurate execution of SQL queries.
Semantic engine: matches business terms with data sources, defines relationships, and includes predefined calculations.
2. The system architecture was developed:
User interface: an intuitive interface for entering queries, defining relationships between data, and integrating data sources.
AI module: query processing using a vector database for contextual search and LLM direction for accurate execution of SQL queries.
Semantic engine: matches business terms with data sources, defines relationships, and includes predefined calculations.
Business values
Reduced time spent creating reports
Reporting errors reduced by 30%
from 1.5 hours to 20 minutes, which is 4,5 times faster
Automation has freed up about 50% of analysts' time previously spent on routine tasks.
Saving labor costs
Increasing user satisfaction with the system
90% of respondents noted an improvement in user experience after implementing the BI Assistant
An intuitive interface and natural language support reduce the need for employee training by 80%
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Development of software
and Big Data solutions
and Big Data solutions
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