Master Complex Multi-Modal Data ETL Challenges

To Unlock Data Value

Enterprise proprietary data is the optimal fuel for GenAI application development, yet severe data fragmentation persists. MatrixOne Intelligence automatically integrates diverse heterogeneous data through multiple innovative connectors, rapidly establishing high-quality data pipelines for GenAI.

Multi-Modal Data ETL

Building AI Applications

Starts with Data

Multi-source Heterogeneous Data Connectors

Supports various data source types including databases, local file systems, object storage, HDFS, cloud drives, SaaS applications, etc., covering both structured and unstructured data

End-to-End Data Pipeline Monitoring

Full-lifecycle data visualization, monitoring, and alerting mechanism—ensuring every data point is observable and controllable, with guaranteed integrity and timeliness

Flexible Integration Task Scheduling

Highly flexible and configurable data integration and processing task scheduling, easily creating and adjusting workflows according to business needs, improving data processing efficiency and business response speed

Rapidly Build High-Quality Semantic

Data Pipelines for GenAI

Break down enterprise data silos, map dormant enterprise data

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

Lay a solid data foundation for subsequent RAG, knowledge base, and Agent applications

Use Case
AI Multi-Modal Corpus Management Platform
Using MatrixOne Intelligence, Shenzhen Smart City centralized multi-modal data from multiple sources and harnessed the platform’s all-in-one capabilities for integration, cleansing, processing, annotation, and enrichment. This approach accelerated data-driven decision-making, powered intelligent and automated smart city applications, and increased business responsiveness by 40%.