Mastering ETL Challenges for Complex

Multimodal Data to Unleash Data Value

Enterprise-owned data is the optimal fuel for building GenAI applications, yet severe data fragmentation exists. MatrixOne Intelligence automatically integrates diverse heterogeneous data through multiple next-gen connectors, rapidly establishing high-quality data pipelines for GenAI.

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Multimodal Data

Building AI Applications

Starts with Data

Multi-Source Heterogeneous Connectors
Multi-Source Heterogeneous Connectors
Supports multiple data source types including databases, local file systems, object storage, HDFS, various cloud drives, and SaaS applications, covering both structured and unstructured data
End-to-End Pipeline Monitoring
End-to-End Pipeline Monitoring
Visual monitoring and alerting for complete data lifecycle management, ensuring observability and control of every data point to guarantee integrity and timeliness
Flexible Task Scheduling
Flexible Task Scheduling
Highly flexible and configurable data integration/processing task scheduling that enables easy workflow creation and adjustment per business needs, improving processing efficiency and business responsiveness

Rapidly Build

High-Quality Semantic Data Pipelines for GenAI

Rapidly Build
Break down enterprise data silos and activate dormant data assets

Break down enterprise data silos and activate dormant data assets

Eliminate barriers in traditional ETL for processing unstructured images, audio, and documents

Eliminate barriers in traditional ETL for processing unstructured images, audio, and documents

Establish solid data foundations for subsequent RAG, knowledge bases, and Agent applications

Establish solid data foundations for subsequent RAG, knowledge bases, and Agent applications

Use Case
AI Multimodal Corpus Management Platform
Shenzhen Smart City Group leveraged MatrixOne Intelligence to centrally manage multimodal data from diverse sources. Utilizing the platform's all-in-one integration, cleansing, processing, labeling, and enhancement capabilities, they improved data analysis and decision-making efficiency by 40%, accelerating intelligent automation for smart city applications.