AI Agents: Driving Intelligent Decision-Making and Sustainable Business Growth through Data Analytics

Large models are revolutionizing the B2B sector, yet many traditional businesses remain unclear on how to effectively integrate these technologies into their operations. With their advanced capabilities in knowledge, intelligence, learning, and reasoning, large models combined with an agent framework empower businesses to cultivate memory, reflection, and continuous learning. This synergy enables organizations to leverage internal tools for ongoing improvement, unlocking substantial business value.

Research indicates that data analysis and decision-making are considered the most crucial applications of this technological combination. When evaluating data analysis agent products, businesses prioritize cost, data security, and measurable outcomes. The deployment of models containing hundreds of billions of parameters can be costly, often restricting access to larger companies. Additionally, data security is paramount, particularly in highly regulated sectors such as finance, where firms must ensure that investments in large models deliver real operational benefits.

To tackle these issues, Shushite Technology has introduced "SwiftAgent," a scalable, business-focused intelligent analysis assistant. This solution lowers costs and eases entry barriers while providing robust data security through a dedicated private data environment, supporting both private and localized deployment.

SwiftAgent 2.0 has recently launched, introducing transformative upgrades for enterprise data analysis and decision-making. The latest version includes five significant enhancements: a unified semantic layer for indicators and labels, multi-modal and multi-source heterogeneous data integration, user intervention mechanisms, sustainable learning capabilities, and a data acceleration engine. These advancements directly address challenges faced in enterprise data analytics.

The "Unified Semantic Layer" simplifies the complexities of business semantics by standardizing data interpretations across departments, preventing misunderstandings. The "Multi-Modal and Multi-Source Heterogeneous Data Linking" feature merges structured and unstructured data, enriching analytical insights. The "User Intervention Mechanism" allows users to engage actively in the data analysis process, offering feedback to refine the agent's capabilities. The "Data Acceleration Engine" facilitates real-time data processing, ensuring instantaneous queries and enhancing human-machine interaction.

At the core of the platform are high-performance data analysis engines like StarRocks and Doris, which provide view-based pre-computation and query optimization based on those pre-computed results. Additionally, data virtualization technology separates data definitions from physical datasets, enabling flexible indicator and label use and improving processing efficiency without hindering development timelines.

For example, in collaboration with a prominent tea beverage chain, SwiftAgent was implemented at the corporate level to establish a semantic layer and data analysis framework for store managers. This technology streamlines access to data and analytical insights through conversational queries, significantly boosting operational efficiency. By adopting this digital tool, the organization has transformed experience-based management into a data-driven operational model, substantially advancing business development and delivering tangible value.