Through the platform's multimodal data analysis abilities, the system can efficiently analyze and store key images and it's matching information. Combined with MatrixSearch's semantic retrieval and vector matching, sales teams simply need to upload the provided images to instantly match products and corresponding inventory details.

This technology reduces product search time by 60%, accelerates client response rates, and digitizes sales workflows for precision marketing and operational efficiency.

The platform unifies structured and unstructured data storage and processing, enabling petabyte-scale image and video analysis.

Through unified feature engineering and multimodal search increased cross-departmental feature reuse by 40% and reduced model development times by 30%.

Additionally, unified data organization and multimodal search allows quicker response to different business needs, increasing the enterprise's AI abilities and operational agility.

The platform handles both structured and unstructured data, while JSON and vector supports the complex storage and query workflows.

Simultaneously, MatrixOne's full-text and semantic search enhances search performance.

Cloud-native Kubernetes deployment reduced client delivery cycles from 2 months to 1 week, with data processing accelerated from hours to minutes.

For a top-tier energy manufacturer that struggled to process large bidding documents without error, which caused delayed market response and lower success rates, the platform enabled multi-source document integration with smart management. The implementation of the platform significantly boosts processing efficiency and data extraction accuracy, accelerating accurate decision-making, increasing bid success rates, using professional knowledge from different experts.

A retail industry leader struggled with scattered product materials, marketing content, user feedback, and other various data stored across seperate systems with inconsistent standards, effecting collaboration negatively. This directly impacted the R&D efficiency and service quality of its intelligent assistants and AI Agent applications. The MatrixOne Intelligence Platform leveraged its robust heterogeneous data integration capabilities, advanced multimodal data parsing technologies, and AI-oriented data governance and enhancement features to help build a unified, high-quality, and trustworthy intelligent data foundation. This not only provided precise and comprehensive information support for applications like the AM Copilot but also laid a solid data foundation for developing and iterating advanced autonomous AI Agents in the future.

The system efficiently processes massive financial transaction data and supports instant analysis of complex risk scenarios. This enables second-level detection and alerts for market fluctuations and transaction risks, drastically reducing risk exposure. It also establishes a robust foundation for refined operations, product innovation, and compliance with stringent regulatory requirements, enhancing decision-making foresight and business agility.