GenBI for ASUS | Going Cloud Use Case | Going Cloud

About

ASUS is a global technology leader focused on the R&D and innovation of computer hardware and smart devices. Its product portfolio includes laptops, motherboards, graphics cards, servers, and AIoT solutions. Renowned for superior quality and innovative design, ASUS is committed to driving digital transformation and sustainable development while maintaining a strong brand and global customer base.

Challenge

As ASUS expanded its global footprint, the demand for real-time sales insights surged. However, the company faced a critical bottleneck: a heavy reliance on specialized data teams, manually processing large volumes of data, consolidating reports, and producing charts. A key goal of their digital transformation efforts was to enable various departments to query sales data by product and region independently. The existing manual model was no longer efficient or sustainable in such a fast-paced market.

Key pain points included:

Decision Latency: Business teams faced long wait times for data extraction, and back and forth communication loops that delayed critical market moves.

Resource Bottlenecks: A small pool of analysts was overwhelmed by a massive volume of ad-hoc queries, stalling high-level strategic projects.

Technical Barriers: The lack of intuitive query tools prevented non-technical staff from independently obtaining real-time sales insights by product or region.

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Solution

To resolve the bottlenecks inherent in the traditional BI process, we introduced an AI-driven architecture that automates the transition from data analysis to visualization.

Built on the AWS Well-Architected Framework, this solution utilizes Amazon Bedrock to create an autonomous AI Agent with Generative BI capabilities, deeply integrated into ASUS's existing data workflows.

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Feature Highlights

Users can input questions in natural language; the system automatically parses these to generate corresponding SQL queries.

The system generates charts based on data table content to intuitively present relationships and trends, while also writing analytical text to provide precise insight reports.
Supports chart downloads for easy integration into reports or presentations, boosting workflow efficiency.
Maintains personalized historical conversation records, allowing users to track and continue previous interactions.

Supports personalized settings including toggles for frontend processes, language switching (Chinese/English), and switching between different business domain modules.

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Feature

Solution Architecture

Result

Created a self-service data analysis environment that allows business and product teams to obtain real-time sales insights.
Automated traditional report generation, shortening the data acquisition and decision-making cycles.
Established a scalable data application architecture, laying the foundation for future AI application scenarios.