Build Next-Generation AI Data Foundation

Match AI Scale and Speed

MatrixOne Intelligence is designed specifically to meet the high-speed, large-scale data demands of the GenAI era. We provide a unified, high-performance, highly reliable solution, building a powerful data foundation that fully matches AI scale and speed, effectively supporting various data parsing and intelligent application scenarios.

Enterprise-Grade AI Data Foundation

Core Infrastructure for Buildings

Enterprise AI Capabilities

PB-Scale Data Analytics Capabilities

Lakehouse architecture that balances the flexibility of data lakes with the high-performance analytics of data warehouses, supporting high-speed storage, querying, and parsing of PB-scale data, effectively supporting various data parsing and intelligent application scenarios

Multi-Modal Data Management Capabilities

Break down data modality barriers, providing a unified platform to manage, process, and understand all types of data, simplifying data preparation workflows for AI applications

Flexible Scaling and Multi-Cloud Support

Cloud-native design enabling flexible deployment and operation across public, private, or hybrid cloud environments, meeting diverse enterprise needs for data sovereignty, security compliance, and infrastructure choices

Unlock Data Value and

Accelerate AI Deployment at Scale

Self-developed data engine, hyper-converged architecture, unified efficient computing

Achieve all AI data preparation tasks in one place, eliminating the need for multiple toolchains

Enterprise-Grade governance and security mechanisms, meeting compliance and sensitive data management requirements

Use Case
Building a Hyper-Converged AIGC Data Foundation Platform
By adopting MatrixOne Intelligence, TechAgent successfully simplified its previously complex multi-database architecture into a unified platform, significantly improving data processing efficiency and delivery speed. The cloud-native design of MatrixOne Intelligence enabled integrated application and database delivery, reducing customer delivery cycles from 2 months to 1 week and improving data processing efficiency from hours to minutes.