AutoBI is an all-encompassing SaaS platform crafted to enable small businesses to provide Business Intelligence analytics solutions while requiring very little human effort and domain skills. The platform automates essential tasks such as data cleaning, pattern identification, standardization, and data modeling using the dataset provided by the user. It then automatically generates dashboards that are aligned with the formulated data model without any user input.
Users can easily customize these dashboards with natural language description (instead of machine language) to meet their unique needs.
What distinguishes AutoBI is its innovative use of multi-agent AI architecture to autonomously generate BI dashboards.
These agents, capable of inter-agent communication, handle diverse tasks like data ingestion, cleansing, and dashboard development. After initial training, agents continually enhance their expertise, accumulating role-specific experience over time. This allows the system to increasingly predict user needs more accurately and efficiently, reducing the need for direct user intervention.
1. Users select the data sources to be inputted into the AUTOBI system. These sources can include Excel workbooks, structured data like database tables, and file formats such as CSV, TXT, and JSON. Selected raw data is then connected to the user's area.
2. Users have the option to choose a specific layout template and describe, using natural language, what to display in each section. If no layout and/or user inputs are provided, AUTOBI autonomously determines the most appropriate analytics and layout based on the input raw data content.
3. The dashboard auto-generation process begins with data cleansing, modeling, and profiling.
4. The AI Multi-Agent team then starts working on the new dashboard project:
a. The Business Intelligence AI Agent is the first to engage, analyzing the Data Profile and User Inputs to create software specifications.
b. The Coder AI Agent follows, using the specifications from the Business Intelligence AI Agent to develop the new dashboard.
c. The Tester Agent evaluates the developed code and presents the results.
d. If errors are detected, the Critics AI Agent offers suggestions for code correction. In this case, the Coder generates a revised version of the dashboard. This cycle of feedback and revision between the Coder, Tester, and Critic continues until the code functions correctly.
5. The dashboard is then ready for user review, after which it can be published to the production web environment.
6. Once in the production web environment, the dashboard is directly linked to the original data source and automatically updates whenever the data changes.
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