Case study: A governed data agent that verifies every number before release.

At a glance

!The challenge

Most AI analytics ask boards to trust the model — with no path back to the source.

⚙What we built

A governed agent that connects, measures and independently recomputes every figure before release.

★The result

Every claim links to its query and source; only aggregated results leave the environment.

BeforeTrust ussummaries with no path to source

→

AfterVerifiedevery figure recomputed

How it works

From input to outcome

⛁Connect

∑Measure

⌖Find the gap

✓Verify

⇪Release

Illustrative scene: AI advisory platform — Illustrative scene · AI advisory platform

The full story

Details for the curious

Open any section for the full detail — figures, design decisions and how it fits your business.

Agentic analyticsDeterministic computationVerification layerAudit trail

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The challenge

Most AI analytics ask you to trust the model. The client needed findings a board could audit: computed, not narrated, and verified before anyone saw them.

How it works

Connect & prepareRead-only access; records cleaned into one trusted view

Measure & diagnoseEach goal becomes a KPI; the gap is sized in cash and capacity

Verify & releaseAn independent check recomputes every figure; what doesn't reconcile is rejected

Why it is different

	Typical AI analytics	Our data agent
Where analysis happens	Data copied to a vendor cloud	Inside your environment
Who produces the numbers	A language model, checking itself	Deterministic calculation
How results are verified	"Trust us"	Independent recomputation
What it recommends	AI for everything	Says "no AI" when it doesn't fit