At the 2026 China International Big Data Industry Expo, YuanGuang Software unveiled its answer to the data infrastructure challenges of the AI era: the AI-Native Data Foundation. This new offering directly confronts a persistent corporate dilemma: why, despite mountains of accumulated data, does AI still fail to grasp the business context?

Over the past decade, enterprises have invested heavily in data middle platforms and data lakes. However, these systems were fundamentally designed for human use, prioritizing dashboard displays and post-hoc analysis, with data processing relying heavily on manual effort. Their critical shortcoming is that the data lacks embedded business semantics, leaving AI unable to understand, autonomously access, or reason over it.

The traditional "software plus AI" external plug-in model is like trying to build new floors on an old foundation, unable to adapt to complex and rapidly changing business environments. YuanGuang Software has chosen to rebuild the foundation itself, designing a brand-new enterprise-wide data infrastructure from the ground up, native to AI requirements.

Five Core Capabilities: Upgrading Data from "Queryable" to "Reasoning"

Built around the goals of making data "AI-comprehensible, autonomously accessible, and continuously improvable," the YuanGuang AI-Native Data Foundation is structured around five key capabilities: Data Fabric, Data Governance, Ontology-Driven Intelligence, Intelligent Analytics, and Intelligent Querying. These capabilities unify the management of multimodal enterprise data—including structured, time-series, document, image, and vector formats—into an integrated whole where the bottom layer connects, the middle layer governs, and the upper layer empowers applications.

At the foundational level, the Data Fabric technology serves as the core support. The self-developed Data Fabric Engine seamlessly connects heterogeneous data sources from multiple systems, enabling real-time cross-source queries without requiring large-scale physical data migration. By breaking down data silos between finance, HR, and supply chain systems, it allows cross-database queries using a OneSQL approach, making multiple databases feel like a single one. This thoroughly resolves the challenges of federated querying across disparate systems.

In terms of Data Governance, AI large models intelligently generate quality rules, automating a closed-loop process that covers issue detection, supervision, and rectification. This upgrades data quality management from a manual, rule-by-rule review process to an intelligent, automated system.

More groundbreaking is the Ontology-Driven Intelligence capability. By leveraging ontology theory to build a unified enterprise semantic layer, it maps real-world business operations into six key elements: entities, attributes, relationships, rules, behaviors, and events. These are fused with algorithmic models to create reasoning capabilities. In practical terms, a "supplier" in the system is no longer just a data field but a dynamic object carrying its qualifications, performance history, and risk relationship network. AI can thereby understand the business meaning behind every metric, much like a seasoned domain expert.

This means data is no longer a collection of cold, disparate fields, but a knowledge graph with business cognition, capable of supporting multi-dimensional simulations like causal analysis and scenario planning. Every reasoning path is transparent and auditable, ensuring that key decisions are explainable and traceable.

Intelligent Analytics integrates ontology semantics with multi-scenario algorithm models, streamlining the entire workflow from exploratory analysis and intelligent attribution to trend forecasting, automatic application generation, and proactive decision support. It covers the full spectrum of analysis scenarios—diagnosis, prediction, and standardization—effectively tackling common pain points like inefficient analysis, limited exploration, cumbersome setup, and disconnected decision-making. This drives a shift from "people searching for data" to "data informing people."

Intelligent Querying, powered by large language models and intelligent agent technology, enables data retrieval, attribution analysis, predictive analytics, and report generation through natural language interaction. Business users can simply converse to access the data they need, while backend AI automatically handles data cleaning, validation, and metric calculations—truly realizing the vision of "autonomous, data-driven decision-making."

Seven Key Features for an AI-Era Data Infrastructure

Based on the design principle of "built for large models and AI agents," the YuanGuang AI-Native Data Foundation boasts seven core characteristics.

First, it is designed for intelligent agents, offering a unified foundation that supports two application modes: one for human analysis and one for automated AI invocation. Second, it provides multimodal enterprise-wide support, enabling integrated storage and governance of structured data, documents, images, and vectors under a single framework. Third, its Data Fabric ensures interconnectivity without requiring full data migration; data is woven and linked on demand, preserving data sovereignty while dismantling silos. Fourth, its semantic, ontology-driven approach injects business context into data, enabling AI to understand and reason about it. Fifth, the AI Autonomy Pipeline delegates repetitive tasks such as data cleaning, validation, and anomaly detection to automated agents, freeing up human resources. Sixth, the model-data symbiosis loop establishes a two-way channel between model training and data provisioning, fostering continuous evolution through a "supply-analyze-iterate" cycle. Finally, enterprise-wide trust and control mechanisms guarantee end-to-end lineage tracing, tiered desensitization, and permission auditing, fully aligning with the security and compliance requirements of state-owned enterprises.

Unlocking Data's Value: A Threefold Impact

For enterprises, the value delivered by the YuanGuang AI-Native Data Foundation is multi-dimensional. At the infrastructure level, it acts as a vital "blood supply system" for AI implementation. By addressing the gap of having data without business understanding, it standardizes metric definitions through a universal semantic layer and supports large models with enterprise-wide data supply for autonomous comprehension and logical reasoning. This paves the way for intelligent querying and for AI agents to autonomously access and utilize data.

At the business scenario level, it functions as a "decision-making advisor" for operations and management. Relying on a trustworthy and traceable data foundation, intelligent querying allows managers to obtain business answers with a simple question. Risk assessment can shift from post-hoc analysis to pre-emptive warnings, compliance audits can move from sampling to comprehensive full-volume checks, and regulatory analysis can better support oversight requirements in an era of transparent supervision. This embeds AI capabilities directly into frontline business operations, ensuring decisions keep pace with the business rhythm.

At the strategic level, it serves as a "transformation engine" for turning data into a factor of production. During the critical phase of digital transformation for state-owned enterprises and the push towards building world-class companies, it converts "dormant data" scattered across various business systems into a production factor that can be inventoried, governed, and enhanced in value. This brings the "Data Element ×" initiative and the drive for new quality productive forces directly to the management frontline. For enterprises, it represents a practical path to transforming years of accumulated management experience into a competitive advantage in the intelligent era.

The launch of the YuanGuang AI-Native Data Foundation is more than a product upgrade; it is a pragmatic response to the defining question of our time: how can data truly empower AI? By enabling AI to understand data, autonomously utilize it, and continuously refine it, the final mile towards intelligent enterprise decision-making has now been traveled.