Data 
Intermediate AI Data Context: A data expert at every collaborator’s side
Our north star: a data expert at every collaborator’s side. A PM, a CSM, a GTM analyst, a marketer — anyone — should be able to get an analyst’s answer whenever they need one.
Two modes:
Reactive — ask in natural language, get the answer a senior analyst would have given, with the same caveats and context.
Proactive — the analyst shows up before you ask: the Monday read on last week’s launch, the “so what?” behind a metric move, the draft of your weekly update. This is less about answering faster and more about helping the whole team decide faster.
The model is the easy part. The hard part is the foundation underneath it: a governed semantic layer of data products and semantic views, exposed through a persona-aware harness that only lets the agent see what the user’s role is allowed to see, with skills that make it reason — and eventually act — like an analyst.
It works: on our first pilot use case, this foundation lifted end-to-end accuracy from ~40% to >90% on the eval set.