Yixin Huachen Releases Ruizhi OPT Multimodal Data Governance Platform, Adopting an AI-Native Architecture

On October 30, 2026, Yixin Huachen officially released its next-generation data governance product—the Ruizhi OPT multimodal data governance platform. The platform adopts an AI-native architecture, with more than 30 built-in data governance expert agents, over 50 governance skills and more than 100 knowledge bases, and unifies governance capabilities such as metadata, data standards, data quality, master data and data analysis into command-line interfaces (CLI), providing full-chain governance capabilities from collection and aggregation to scenario delivery for both structured and unstructured data.

At the launch event, Jin Zhengping, deputy general manager of Yixin Huachen, said that what often constrains the implementation of AI applications is not model capability, but fundamental issues such as inconsistent data standards, substandard quality and the difficulty of governing unstructured data; data governance tools are therefore shifting from the software era of "people looking for functions" to a native era in which AI delivers results.

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Jin Zhengping summarized the challenges facing data governance in the AI era into three aspects: general-purpose large models lack domain knowledge of data governance and suffer from hallucinations, making governance results difficult to directly trust; AI applications strongly depend on data governance, yet governance construction itself has a long cycle and a process that is difficult to standardize; traditional tools struggle to handle unstructured data such as documents, images, audio and video, while the high-quality datasets required for large model training strongly depend on multimodal governance capabilities.

Four-layer architecture: expert library, skill library and knowledge system form the "Data Governance Super Brain"

Ruizhi OPT adopts a four-layer architecture. The AI interaction layer provides multi-turn dialogue, streaming responses and task status management, allowing users to express requirements through conversation; the data governance super brain layer consists of an expert library, a skill library and a knowledge system, with experts covering roles such as requirement research, governance planning, data architecture, model design, data development, data standards, data quality, data assets, data security, data services and governance assessment, and the knowledge system covering DAMA-DMBOK2, DCMM 2.0, industry specifications, laws and regulations, and multi-industry project practices; the agent platform layer is responsible for large model scheduling, task orchestration, expert collaboration and tool invocation; and the governance function layer turns the original governance modules into CLIs, forming three types of command entrances—Ruizhi CLI, Ruima CLI and ABI CLI—supporting structured JSON output and unified authentication, making it easy for third-party platforms to integrate and invoke.

The platform provides three working modes: fully automatic execution, human-machine collaboration and manual revision. In fully automatic mode, after the user raises a requirement, the platform completes intent understanding, task decomposition and expert invocation; in human-machine collaboration mode, the platform proactively pauses and prompts for supplementation when key information is missing, with task progress and execution process visible in real time and abnormal tasks able to be terminated at any time; in manual revision mode, AI-generated governance results can be directly edited and saved by business personnel without re-issuing instructions.

Multimodal governance: covering six links, forming a "governance—dataset—application" closed loop

For unstructured data, the governance process of Ruizhi OPT covers six links: collection and aggregation, processing and fusion, multimodal governance, annotation and enhancement, asset operations and scenario-based delivery, supporting cross-modal field-level lineage tracing, multimodal standard compliance, image-text semantic consistency verification, and annotation capabilities such as image box selection and segmentation, document entity extraction, audio and video slicing, large model pre-annotation and synthetic sample expansion. Classification and grading, sensitive identification, desensitization processing and operation traces run through the entire governance process.

On this basis, the platform forms a closed loop of "multimodal data governance—high-quality datasets—AI applications—effect verification—governance optimization": governance outputs high-quality datasets, datasets support model training and inference, and application effects in turn verify dataset quality and governance level, thereby driving continuous iteration of datasets.

Scenario demonstration: four-step delivery of a supplier data governance project

At the launch event, a complete supplier data governance project was demonstrated on site. Based on the project background, cycle requirements and uploaded work statements and research reports input by the user, the platform generates a task list and project roadmap; it then completes data source access, metadata collection, data standard formulation and data modeling, during which it proactively pauses when detecting missing data source connection configuration information, guiding the user to fill it in and test the connection; it then associates the license image information in object storage with supplier master data to form a multimodal dataset and publish it as a data asset; finally, combining data analysis capabilities, it generates a supplier analysis report presenting supplier information completeness, type distribution and risk warnings such as licenses nearing expiry or expired.

"1×M×N": proposing an AI-native delivery model

At the launch event, Jin Zhengping proposed the "1×M×N" AI-native delivery model: 1 project leader is responsible for decision-making and accountable for results; M expert agents respectively take on roles such as requirement research, architecture design, data development, quality auditing, security compliance and asset operations; and N skill packages are plug-and-play and flexibly combined according to task needs.

According to the introduction, this change in the data governance delivery model will also affect the industry supply structure: delivery capability will no longer depend entirely on team size, but more on the degree to which expert experience and knowledge assets have been accumulated.