Implementation and Managed Intelligence

Discovery on a defined use case, so there is a specific question we are trying to make routine rather than a general aspiration. Connecting your sources. Building the semantic model: the tables, the grain, the join paths, what every column means and which ones are sensitive.

Then the part that decides whether any of it works — a validated starting library of business metrics and definitions. Each entry carries a metric name, a definition in plain business language, the calculation, the source system and fields it reads, the dimensions it can be broken out by, the filters that apply, the rules about what it may be compared with, an owner, a refresh cadence, who may see it, its validation status, example questions it answers and the limitations a careful analyst would state out loud.

The point of that library is not to have a list of metrics. It is to settle, once and in writing, the language and the arithmetic DeepGarden is allowed to use — so the argument about what revenue means happens in one meeting rather than in every meeting afterwards.

Then permission configuration, query validation against your scope, acceptance testing with the people who will actually use it, administrator training and launch support.

A monthly retainer sold as defined delivery capacity against a prioritized backlog — never as unlimited customization. New metrics and domain models, new Views, data-quality investigation, query and prompt tuning, and an analyst who knows your setup.

It exists because the alternative is worse for both of us. Without it, every piece of ongoing analytical work is either quietly absorbed into support until support stops working, or refused until the platform stops being useful. Naming it and pricing it means the answer to "can you also build us this" is a scope conversation rather than an argument.