Building a Digital Intelligence Foundation: GoldenDB’s Core Foundations and AI‑Driven Integration

Core Foundations: Security, Reliability, and Commercial Leadership

GoldenDB, founded in November 2021 with over 6.257 billion CNY in registered capital, is the largest domestic database R&D enterprise by capital. By 2024, strategic investors from China Mobile, CITIC Bank, China Construction Bank and Bank of China have invested, reflecting industry confidence.

With more than 20 years of technology accumulation and over a decade of industry experience, GoldenDB has run stably for more than six years in the core systems of several state‑owned and joint‑stock banks, handling over 200 billion daily core transactions and a total transaction amount exceeding 10 trillion CNY. It serves more than 1 500 global users and has filed over 1 000 patents. Its product portfolio now covers distributed‑transaction, centralized‑transaction, and distributed‑analytics models, with four flagship products that have passed national authority certifications and achieved six industry milestones.

Intelligent Integration: Multi‑Modal Unified Architecture

To meet the diverse load requirements of digital transformation, GoldenDB adopts a same‑source, integrated architecture that enables AI‑coordinated multi‑modal interoperability. For domestic substitution, it provides a "one mature solution + three technical paths + six core capabilities" engineering system, employing full‑process control, end‑to‑end support, CAC migration assessment, Sloth migration sync, and simulation replay tools to deliver a turnkey, lifecycle‑spanning substitution capability.

The platform emphasizes three core capabilities: multi‑modal fusion, collaborative computing, and open compatibility. Its cloud‑native design offers extreme elasticity and full integration with the AI ecosystem.

DB for AI: Native support for multimodal data processing and an Agent‑native data service layer, positioning GoldenDB as AI infrastructure.

AI for DB: AI techniques reshape the core—AI‑driven query cost optimization, intelligent index recommendation, parameter tuning, and full‑link observability—enabling self‑awareness, self‑diagnosis, and self‑evolution.

Key enterprise AI application pain points—semantic understanding, multimodal data management, separation of TP and AP data, branch isolation, and Agent state management—are addressed through four scenarios (transaction analysis, fused retrieval, multimodal AI‑SQL, Agent‑native analysis) and five technical breakthroughs: native multimodal storage and hybrid retrieval, LTAP (row‑column conversion, load isolation, consistency, freshness), ontology semantic modeling, data branching (environment isolation, cost‑free cloning, change traceability), and memory management (extraction, precise recall, dynamic evolution).

Autonomous Operations Architecture

Traditional DB operations involve complex decision chains across resource sizing, schema design, indexing, performance tuning, and change management, often hindered by human‑centric CLI and GUI tools that AI struggles to parse. With Agents becoming primary interaction entities, GoldenDB proposes a paradigm shift from "human‑managed" to "intelligent‑governed" operations.

The autonomous operation framework consists of four closed‑loop layers:

Interaction Layer: Conversational interface allowing DBAs to issue natural‑language commands and conduct multi‑turn dialogues, secured by role‑based access control.

Scheduling Layer: Master scheduler handling sub‑task planning, execution graph orchestration, dependency management, and task status tracking.

Perception & Knowledge Layer: Multi‑source collectors (metrics, logs, deadlock graphs, alerts), an operation ontology database (entity metrics, fault graphs, causal analysis, semantic reasoning), and an RAG vector knowledge base (historical faults, case studies, best practices, manuals).

Data & Resource Layer: Core data instances, assets, runtime environments, and configuration.

These layers enable a "perceive‑judge‑decide‑execute‑feedback" intelligent loop. When a fault occurs, the Agent automatically infers intent, gathers multi‑modal information, performs root‑cause reasoning, decides remediation, generates a repair script, executes it in a gray‑scale rollout, and validates the outcome—transforming DB operations from a manual workshop to a scalable, automated "intelligent factory".

Future Outlook

The author envisions databases evolving from mere "record‑keeping containers" to "cognitive infrastructure" that records, understands semantics, and supports reasoning and decision‑making. GoldenDB aims to become a long‑term memory repository and continuous learning engine, leveraging ontology modeling, memory management, data branching, and sandbox mechanisms to support iterative AI agent development.