We build the operating model, the ownership, and the measurement that turn a governance policy into something your organisation actually runs — and, where it helps, the platform underneath it. Advisory and delivery from the same team.
Each of these can run standalone, or as a sequence — assess, then design, then measure, then sustain.
We design governance that fits the organisation you already have. That starts with understanding how your teams work today — who is already doing stewardship, which definitions matter most, and which regulatory obligations carry real weight.
From there we build the operating model: named owners, a clear decision path, and a small set of measures that show it is working. Practical enough that people use it, rigorous enough to stand up to an audit.
The deliverable is a running discipline. Where a platform helps, Clarix gives it a home — but the operating model comes first and stands on its own.
AI adoption moves quickly, and the organisations that get the most from it are the ones that can account for it — which models are running, on what data, with whose approval.
We build that picture with you: a live inventory, risk tiering so oversight is proportionate, and clear answers on processor choice and data residency that satisfy your own data-protection policy. New AI transparency requirements become something you are already prepared for.
A principle we hold to: observed, declared, and derived facts stay structurally separate, and human sign-off stays where the consequence sits.
Analytics earns its value on trust. When everyone agrees what a measure means and where it comes from, the meeting is about the decision rather than the numbers.
So we treat the metric definition as the primary artefact. Every measure gets a governed definition, an owner, and a stated source before anyone builds a chart on it. Then we build the models and reporting on top — sized to the decision being made, not to the size of the tool.
Where reporting has to satisfy a regulator as well as a board, definitions and lineage are traceable end to end: this number, from this source, under this rule, last verified on this date.
Quality works best when the standard is bound to the definition — so every governed term carries a measurable expectation from the moment it is created, and quality is assured at the point of definition.
We profile what you have, design rules across the six dimensions that matter, and set thresholds with the people who own the outcome. Then we close the loop: findings route to a named owner, and the remediation they deliver shows up as movement on the score.
That last part is what makes a quality programme sustain itself. When fixing something visibly moves a number, the work keeps its momentum.
Clarix is our own AI-native platform for integrated data and AI governance — a real-time scorecard for governance health, with processing in-house so your data stays in your environment. Engagements can use it or not; the advice doesn’t change either way.
Tell us where you want to get to — a governed set of definitions, measurable quality, or AI you can account for. First conversation is free.