Platform
Governed AI.
An AI layer built on the data platform, for environments where governance is non-negotiable.
Problem
The problem
AI in regulated environments is a governance question first. What data the model sees, what decisions it influences, what audit trail follows each output — those answers have to hold under scrutiny, not just produce a plausible demo. Most AI offerings start from the model and add governance as a wrapper. In regulated settings, that ordering does not survive contact with the first audit.
Delivery
What the practice delivers
The Cloudbuilder Governed AI layer is built on the Cloudbuilder Data Platform, not bolted on top of it. Governance is architected into the data surface the AI sees, the audit trail it produces, and the automation it enables in the reporting layer above. Outputs are traceable to their inputs — the data, the instructions, the model version — rather than reconstructed from inference after the fact. The same practice owns the platform, the governance model, and the AI surface — there is no handoff across vendors where accountability usually goes missing.
Track record
In practice
Governed AI is the mechanism that makes the other Cloudbuilder offerings production-credible in regulated environments. It is the spine that connects the data platform to the Power BI automation surface, without introducing a governance gap between them.
The governance model in detailRead moreHide
Governance is layered. The data layer enforces what the AI can see — row-level access, environment isolation, schema-level audit. The AI layer enforces how outputs are constructed — prompt provenance, model selection controls, output review where the regulator requires it. The reporting layer above enforces how outputs surface to the business — same lineage, same access model, same audit trail.
The three layers share an architecture rather than three separate governance regimes. A change in the data access model propagates to what the AI can see; a change in AI output handling propagates to how reports treat that output. One practice owns the design of all three.
How the audit trail holdsRead moreHide
Every AI output produced inside a regulated engagement is traceable to: the data the model saw, the prompt or instruction set that shaped the response, the model and version that produced it, and the downstream surface where it was used. The audit trail is not a separate logging concern bolted on after deployment — it is part of how outputs are constructed.
That structure means the question “why did the AI say this?” has a reconstructable answer rather than a generated explanation. In environments where the regulator may revisit a decision months later, that distinction is structural, not cosmetic.
Where engagements typically startRead moreHide
Regulated buyers come to a Governed AI conversation in one of three states: an existing data platform with no AI layer; a model in development without a governance discipline that survives audit; or a regulator-driven deadline that has made the question urgent. The first conversation surfaces which state applies and what it implies for the architecture. The first deliverable is a working governance model for the AI surface, written against the data the model will actually see.
What the buyer leaves with is the same architecture that runs in production — accountable from design through to handover. There is no handoff across vendors where accountability usually goes missing, and no separation between the practice that designed the governance and the practice that handed it over.