GBASE Database AI Integration Capabilities Analysis

Artificial intelligence is profoundly changing how enterprises use data. The explosion of large model technologies has not only spawned new application forms, but also driven fundamental changes in databases across three dimensions: a qualitative shift in data form from primarily structured to multimodal fusion, an innovation in interaction from SQL-driven to natural language-driven, and an evolution in operations from relying on human expertise to intelligent autonomy.

Database Paradigm Reinvented: AI Scenario-Driven Applications Powered by GBase's Intelligent Kernel

In response to these three transformations, GBASE General Data Technology proposes a new database paradigm for the AI era—a converged architecture with the intelligent kernel as the engine, capability services as the hub, and scenario implementation as the outlet. This paradigm no longer treats the database as a passive data "warehouse" but upgrades it into an "intelligent data foundation" with capabilities of perception, reasoning, interaction, and autonomy.

GBase's AI capability architecture is built on the GBase full-stack database system, with the core design philosophy of "Intelligent Kernel + Intelligent Ops + Intelligent Interaction." It can connect to multiple large models for inference and provides standardized interfaces to the external AI ecosystem, enabling deep integration of product capabilities with scenario-based applications.

GBase AI Capability Highlights

• Built-in, Not Bolted-on

  AI capabilities are not built as a standalone AI platform outside the database; instead, the vector engine, AI functions, in-database large models, and other capabilities are deeply embedded within the database kernel. This means AI computation can be performed "in place"—vector retrieval, model inference, and intelligent analysis can all be done without moving data out of the database, improving both efficiency and security.

• Open, Not Locked-in

  Through the MCP (Model Context Protocol) interface layer, GBase opens its database capabilities via standardized protocols to all mainstream large language models and agent frameworks. Enterprises can choose their preferred LLM (such as Qwen, DeepSeek, GPT, Claude, etc.) and seamlessly connect to GBase's data capabilities through MCP, without being locked into a specific model choice.

• Modular Approach for Incremental, Phased Implementation

  GBase AI services support on-demand configuration, allowing enterprises to start with the most pressing single scenario—for example, first using the vector engine to solve RAG retrieval issues, then leveraging GDA to improve data analyst efficiency, and finally deploying AIOps for full autonomy. Each layer of capability can be enabled independently and seamlessly combined when needed.

Intelligent Kernel

• Vector Engine

  The GBaseDataVec vector engine provides storage and retrieval capabilities for vector data types. When processing large-scale high-dimensional vector data, its efficient similarity search enables large models to accurately recall relevant private knowledge, thereby delivering lower latency and higher accuracy for enterprise-grade AI applications in scenarios such as Retrieval-Augmented Generation (RAG), recommendations, and semantic search.

• Storage-Compute Separation

  GBase supports object storage for reliable data storage and cost-effective scaling. By decoupling the compute and storage layers, it overcomes resource waste and scaling challenges of traditional distributed databases, delivering key capabilities such as auto-scaling, second-level branching, and virtually unlimited storage. This provides a technical foundation for eliminating operational complexity, building RAG and AI-driven applications, supporting large datasets, AI search, and burst traffic workloads. Instant Branching

• Instant Branching

  To address pain points in AI scenarios such as difficulty in tracking data versions, challenges in parallel development across multiple environments, inadequate isolation of experimental environments, and high rollback costs, we introduce a data agility workflow built on Instant Branching, deeply integrating the copy-on-write branching capability of the database with the full AI lifecycle. GBase's Instant Branching creates lightweight database copies from point-in-time snapshots, requiring no full data copies, and offers features such as second-level creation, read/write isolation between branches, reuse of historical checkpoints, and low-cost incremental storage.

• Built-in AI Functions

  At the kernel level, it integrates large model inference UDF/UDTF custom functions and built-in AI operators, enabling direct invocation of large models within the database to perform inference tasks such as text classification, sentiment analysis, content summarization, data labeling, and anomaly detection. This localizes data computation and model inference, reducing cross-node and cross-platform data transfer overhead, slashing inference latency from seconds to milliseconds, while safeguarding core business data privacy and security.

• In-Database Large Models

  Existing AI models (e.g., LLMs) primarily process unstructured data such as text and images and struggle to directly comprehend the intricate relationships among SQL tables. However, a vast amount of high-value data resides in relational databases (RDBs). GBase In-Database Large Models bridge this gap, enabling AI to natively process and analyze mainstream structured data. Featuring zero-shot prediction, ultra-long context windows, uncertainty quantification, and cost-effective efficiency, they can be widely applied to real-time prediction and planning in areas such as retail demand forecasting, financial asset price trends, data center resource workload, and weather station temperature changes.

Intelligent Interaction

The GBase AI Intelligent Interaction Platform is an enterprise-grade intelligent collaborative development platform for databases, offering two modes—GDS (GBase Data Studio) and GDA (GBase Data Agent)—each tailored to different user scenarios.

• GDS

  GDS (GBase Data Studio) is an intelligent development platform designed for database developers, offering an immersive AI-assisted coding experience with features such as SQL syntax prompts, optimization suggestions, SQL quality monitoring, metadata recognition assistance, and real-time performance risk alerts. Additionally, GDS includes a built-in "Query Agent" (NL2SQL, natural language to SQL), which leverages business knowledge bases to understand field semantics, enabling users to generate and execute SQL directly from natural language input.

• GDA

  GDA (GBase Data Agent) is the core intelligent component of GBase databases for the AI-native era, delivering a one-stop offering that includes an Agent development framework, built-in intelligent agents, and an extensible agent ecosystem, empowering enterprises to easily build database-driven intelligent applications. Through a standardized Agent development SDK and a plug-in architecture, it supports custom skill modules and workflow orchestration for Agents. Developers can leverage the GDA framework to encapsulate database operations, API calls, and business logic into reusable skill components, rapidly constructing specialized agents for specific scenarios.

Intelligent Operations

GBase AIOps is an intelligent operations management platform for the full database lifecycle, built on three core pillars: an operations knowledge graph, intelligent algorithms, and digitized expert experience. It delivers an end-to-end intelligent perception and analysis operations system, integrating six key capabilities: intelligent baselining, health profiling, SQL lifecycle management, capacity model forecasting, fault model early warning, and automated deep inspection. This transforms operations from reactive fault response to proactive risk prevention, and from human-experience-driven to intelligent data-driven models. It is compatible with both IT Application Innovation (Xinchuang) and cloud-native deployment environments, providing a stable, efficient, and intelligent database operations foundation for enterprise AI digital transformation.

MCP Interface

At the large model standard protocol level, the GBase MCP Server provides standardized capability encapsulation for the GBase database family. This includes offering database access and operation interfaces for AI Agents; supporting context awareness to transform a single query into a sustained multi-turn conversation; and enabling multimodal data processing to understand and process text, images, SQL data, and other cross-modal data.

Vector-Based AI Search

Vector search powered by GBase is widely applied in typical scenarios across industries, such as:

• Text search: Q&A-style search integrated with large language models, typically including online customer service systems, chatbots, and more.​

• Image recognition: Used for security surveillance, identity verification, and other scenarios by analyzing facial features in images.

• Vehicle retrieval: Capturing vehicle images via cameras for license plate recognition and vehicle feature analysis.

• Real-time trajectory tracking: In logistics, obtaining transportation trajectories through real-time tracking to improve efficiency and safety.

• Recommendation systems: Recommending relevant products based on user browsing behavior and purchase history to enhance satisfaction.

• Voiceprint matching: In finance, security, and other fields, using voiceprint recognition for identity verification to ensure transaction and operation safety.

• Genetic screening: In drug research and development, searching for specific gene sequences to identify potential drug targets, accelerating new drug discovery.

Autonomous Database Operations

With GBase's native support for vector data, users can freely embed vector retrieval capabilities into their business systems, building intelligent search, analysis, and decision-making applications on demand.

GBase has been widely deployed in finance, telecommunications, government, energy, transportation, and other industries, serving over 1,000 customers. Autonomous Database Operations is GBase's full-stack self-managing capability designed for enterprise-grade database maintenance scenarios. Leveraging an intelligent kernel and AIOps foundation, it enables databases to evolve from manual oversight to a fully intelligent lifecycle encompassing self-monitoring, self-diagnosis, self-optimization, self-scaling, and self-healing.

Business Forecasting and Planning

In many business areas—whether supply chain, financial risk control, energy dispatch, IT capacity planning, or predictive equipment maintenance—forecasting and planning capabilities are core to operational efficiency and cost control. Moving from early experience-based judgment to statistical models, GBase's in-database large model technology now allows business analysts to complete the entire pipeline from data processing to predictive output using standard SQL, without relying on advanced statisticians or complex Python environments. This dramatically lowers the barrier to intelligent forecasting.

Enterprise AI Agents

Driven by both enterprise digital transformation and AI-powered upgrades, the deep integration of GBase with large models breaks down traditional data silos, creating intelligent applications tailored to business scenarios. This enables intelligent business processes, self-service decision analytics, and natural human-machine interaction, allowing data intelligence to directly empower business outcomes. Enterprise AI Agents will progressively replace existing application models, with AI Agents understanding business logic and shaping a new trend of "Agent + Database" business applications.

In the AI era, a company's competitive advantage no longer depends solely on the volume of data it possesses, but on its ability to transform that data into intelligent decisions efficiently, securely, and cost-effectively. The core value of GBase AI capabilities lies precisely in helping businesses bridge the gap from "having data" to "having intelligence."

• Efficiency Leap: Data insights in minutes, not days.

• Cost Optimization: From redundant builds to reusable capabilities. 

• Security Baseline: From external data movement to in-database intelligence.

• Lowering Barriers: From expert-only to accessible for everyone.

At the dawn of the AI era, databases are undergoing a paradigm evolution from "data management systems" to "intelligent data foundations." GBase AI will continue to advance in deeper embedded intelligence, broader ecosystem connectivity, and stronger autonomous capabilities.