Modelling where you work

Modelling happens in one tool. The work happens in another.

Somebody opens a modelling tool, works out what a domain is called and what a metric counts, and writes it down. Everyone else goes back to the editor, the notebook and the query they actually work in, where none of it is visible.

So a definition is looked up once and remembered approximately after that. An engineer who needs to know what a table holds asks a colleague, reads the column names, or writes the version that looks right today.

What changes

Modelling stops being a separate job. Domains, models and definitions are drafted across your catalog rather than assembled by hand, and what comes back is a draft to review rather than a quarter of somebody's year.

What has been agreed is then reachable from the assistant already open in the editor, so an engineer asks what a table or a metric means where they are, rather than leaving to find out and settling for a guess.

Start with what you already have

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.