Intelligent Extraction and Governance of Multimodal Data: For unstructured data such as PDFs, Word documents, scanned documents, images, audio, and video, DataSphere AI incorporates built-in capabilities such as OCR, speech transcription, and multimodal large language models. It can automatically extract text, tables, entities, speech content, and image metadata, and uniformly govern them as standardized assets that are structured, searchable, and traceable, helping enterprises incorporate “dark data”—which has long remained outside their governance systems—into unified asset management.

To address issues such as missing Chinese field names, unclear business definitions, and incomplete classifications, DataSphere AI leverages the contextual reasoning capabilities of large language models to automatically complete field names, business definitions, data classifications, and semantic information, thereby transforming the data catalog from “visible” to “understandable.” This approach can increase the completeness of the data catalog to over 85% and reduce the time data scientists spend searching for data by 60%.

DataSphere AI deeply integrates AI into the data development process, supporting SQL generation, scripting, logic explanation, validation checks, task troubleshooting, and code optimization. It helps development teams improve efficiency, shorten delivery cycles, reduce manual coding errors, and build a more efficient and stable data development system.

By combining a rules engine with AI-powered detection, DataSphere AI enables the automatic detection, early warning, root cause analysis, and automated remediation recommendations for data quality issues. This drives the evolution of quality governance from manual inspections to continuous, autonomous management, helping enterprises identify problems earlier, close the loop faster, and continuously improve data stability and business reliability.

DataSphere AI supports unified scanning of structured, semi-structured, and unstructured data, and can identify over 100 categories of sensitive information, including ID cards, bank cards, and medical records. The platform achieves an identification accuracy rate of over 95%, keeps the false positive rate below 5%, and improves the efficiency of data classification and categorization by more than 10 times, effectively supporting enterprise data security and compliance governance.

Based on table structures, historical documentation, field semantics, and business terminology, DataSphere AI can automatically generate data standards, metric definitions, and naming conventions. It also supports standard compliance checks, deviation detection, and consistency verification, helping enterprises reduce the cost of standard development and improve the efficiency of standard formulation, promotion, and implementation.

DataSphere AI uses AI to automatically analyze data lineage at the field level. When changes occur in upstream table structures or field logic, it can generate impact maps for downstream reports, applications, and models within seconds, helping enterprises quickly assess the scope of changes, identify potential risks, and reduce systemic impacts and operational costs.

For data service provision scenarios, DataSphere AI enables users to describe their requirements directly using natural language. The platform automatically handles intent parsing, asset matching, logic assembly, and parameter mapping, and publishes the result as a standard RESTful API with a single click. It also automatically applies governance policies such as authentication, rate limiting, and audit logging, significantly shortening the data service delivery cycle and improving the efficiency of data asset reuse.