Engagements
Four tiers, from a diagnostic to governance we run.
Each tier spans data and AI as one continuum, and each can be the start of the next.
Land and expand
The value ladder.
- 01
Diagnostic
Establish exposure and a roadmap. Maturity assessment, data and AI risk exposure and an executive roadmap. Suits organizations beginning or unsure of where they stand.
- 02
Design
Architect the system. Operating model, control framework, risk taxonomy and policies across data and AI. Suits organizations with a clear picture of exposure, ready to define the target.
- 03
Transformation
Operationalize at scale. Implementation, lineage and lifecycle integration and assurance enablement. Suits organizations ready to build governance into live systems.
- 04
Managed governance
Run it continuously. Governance operations, assurance, regulatory tracking and reporting for data and AI. Suits organizations that want the function operated alongside their teams.
Scope is shaped by enterprise size, data and AI footprint and regulatory exposure, and is agreed in writing before work starts.
How scoping works
Scope is agreed before work starts.
Every engagement begins with a written scope that names the data and AI systems, business units and regulations in play, and the deliverables expected. The diagnostic is the usual way to set that scope for later tiers, because it replaces assumptions with a measured baseline.
Longer engagements state their milestones, deliverables and review points in the same document, so progress can be checked against something concrete.
Ideal fit
Who these engagements suit.
Large regulated and AI-scaling enterprises, typically with low-to-medium governance maturity and moderate-to-high regulatory exposure. Smaller or less exposed organizations can still start with a diagnostic to test whether the fit is right.
Sector context is described on the sectors pages, and the way each tier is delivered is described under method.
FAQ
Frequently asked questions
Do we have to move through the tiers in order?
No. Enter where your maturity and urgency suggest. Many organizations begin with a diagnostic because it sets scope and priorities for what follows.
What shapes the scope of an engagement?
Enterprise size, the footprint of data and AI systems in scope, and regulatory exposure. Scope is agreed in writing before work starts.
What does the diagnostic produce?
A current-state maturity view, a data and AI risk exposure assessment, and a prioritized roadmap. The request form describes what to include.
Contact
Make your data and AI provable.
Start with a diagnostic: current-state maturity, risk exposure and a prioritized roadmap. We aim to reply within two business days.