A semantic layer is a translation layer that maps raw tables and columns into consistent business terms, such as “revenue”, “active user”, or “churn”, so that everyone querying the data gets the same definition no matter the underlying schema.
It sits between the warehouse and the tools that consume data: BI, dashboards, and increasingly AI agents. Without it, the same metric gets computed five different ways across five teams, and nobody can tell which number is right.
For AI, a semantic layer matters because an LLM or an agent querying enterprise data needs stable meaning, not just rows. When a column is renamed or a metric is redefined upstream, the model quietly starts answering a different question.
A semantic layer keeps definitions consistent. It does not guarantee a data state that an AI run can trace and reproduce. CUBIG builds the platform for AI-ready execution on top of consistent semantics, not instead of them, so the data an AI uses is usable, traceable, and reproducible.