The evolution from traditional data models to ontological models represents a shift from fragmented data recording to holistic semantic cognition, leaping from describing the world to understanding and simulating it.
Build a universal business language system for enterprises, transforming fragmented standards into comprehensive and structured knowledge for AI.
Encode expert knowledge and process logic into an executable rule base for AI to make consistent decision-making.
Connect AI's decisions with system operations to form a closed loop across decision-making to execution.
Support multiple intelligent building approaches, including RDF import, requirement documents, database schemas, and application code.
Map ontology objects, logic, and actions into visualized networks for unified business knowledge accumulation.
Ontology + AI to enable quick simulation and reasoning across business scenarios.
Provide diverse interfaces for agents and business applications, such as API, OAG, MCP, and OSDK.