Data Integration from Disparate Sources: The system gathers data from diverse sources, which could include databases, APIs, streaming data, or other repositories.

ML and AI Technologies: Machine learning and artificial intelligence are utilized for intelligent data curation and mapping. This suggests that the system is capable of automatically organizing, cleaning, and structuring data in a way that makes it more meaningful and useful.

Intelligent Data Curation and Mapping: This involves the use of ML and AI algorithms to identify patterns, relationships, and insights within the data, making it easier to understand and analyze.

Overlaying Industry Business Semantics: The system incorporates industry-specific business semantics, indicating that it understands the context and meaning of data within the particular industry it serves. This could involve domain-specific terminologies, regulations, or standards.

Capturing Real-World Meaning of Systems of Engagement: The platform aims to comprehend the real-world significance of the major systems used by an enterprise. Systems of engagement typically refer to the technologies and applications that directly interact with end-users.

Empowering Enterprise-Scale Applications: The end goal is to provide a robust foundation for the development and deployment of large-scale enterprise applications. This implies that the system is designed to support and enhance the functionality of various applications used within the enterprise.