Solutions Gallery — Model Citizen

The Data Concierge

A governed semantic model, curated and AI-ready, standing behind every ITSM report a global hospitality enterprise runs — plus an agent that explains why the number is what it is, and a Watchtower that watches the whole domain for the trouble forming before it lands.

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governed model

duplicated metric logic

AI-ready

and human-readable

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Data StorytellingGlobal Enterprise

Demand Views - 3:1

Three dashboards, three definitions of demand, three different answers to the same question. See how one blended signal — unifying sales, support, and web demand across dozens of markets — became the number leadership actually trusts.

68% faster reads

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Data ModelingFoundations

The Anatomy of a Semantic Layer

A foundational, interactive explainer: your data sources have a surface and your BI tools have a surface — the layer that makes them agree has none. Toggle the wiring to reveal the translator, and click its four parts to see what each one does.

3 layers · 1 translator

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Star SchemaFoundations

Some Numbers Are Pizza. Some Are Lasagna.

A foundational, interactive explainer on measure additivity: roll a measure up by a dimension and watch which ones add cleanly (pizza), which add across space but not time, and which are nonsense the moment you sum them (lasagna).

3 measure types

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Decision IntelligenceIT Service Management

MTTR V2.0

MTTR blames whichever team closes the ticket for time that was never theirs. See how a framework borrowed from marketing attribution separates who solved it from where the time actually went — so teams find drag instead of arguing about blame.

Friction, not blame

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Decision IntelligenceData Services

The Health Index

A data services team had a steady client base and a purely reactive support model — no view of which accounts were quietly at risk, or ready for an upsell. See how a health-scoring model, built from scratch and wired into their existing systems, gave them both for the first time.

+31% upsell

Have data that should be doing more?

Tell me about the pipeline that breaks, the metric nobody trusts, or the analysis stuck in a notebook. Let's operationalize it.

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