AI engineers
Your assistant infers what a table means. It gets it slightly wrong.
Semcore holds the agreed meaning of the words your business runs on and serves it to whatever asks, so the context your assistant works from is looked up rather than guessed.
Every consumer works it out again
A schema says a column is called status and holds four values. It does not say which of them counts as active, which was retired, or what the business means by a customer. Each assistant you build works that out for itself, from names and sample rows, and each one arrives somewhere slightly different.
So the meaning ends up in the prompt. It is written by hand, per tool, by whoever built that tool, and it is correct until somebody changes a definition and nothing tells you.
The failure is quiet, which is what makes it expensive. Nothing errors. An answer comes back confident and wrong, and the person who trusts it is the one furthest from the data.
What you can do
One entry in the catalog is written for this, and it puts the definitions where you already work.
Ask what a term means without leaving the editor
The semantic core is reachable from the assistant you already have open, so finding out what a table or a metric means is a question you ask in place rather than a tab you go and open.
Build on a definition somebody agreed
What your agent reads is the governed meaning rather than one inferred from a schema, so two tools built by two people answer the same question the same way.
Stop writing the estate into your prompts
The context that used to be typed into a prompt and maintained by hand is looked up, which means it is one thing to keep right instead of one per tool.
The catalog entry behind this, and what it lets you do.
An AI Readiness Review
A scoping conversation, and a look at your data estate as it stands: what your AI plans will need from it, where the gaps are, and which of them to close first. The findings are yours either way.