Enterprise AI architecture, governance and risk.
We help organisations scope AI opportunities, design production-ready architectures, assess AI risk and establish governance aligned with emerging regulation and industry standards. From idea to controlled production — with the trade-offs, the costs and the residual risk written down.
Scope. Architect. Govern. Assure.
Years in enterprise data and AI
Practitioner experience
Defined engagements, scoped and priced on request
Services registry
Regulatory and standards frameworks mapped
EU AI Act · ISO/IEC 42001 · NIST AI RMF · UK · sector
Weeks to a decision-ready assessment
Shortest fixed-scope engagement
Most AI programmes do not fail technically. They fail before the build and after it.
They fail at the front, where nobody defined the problem, the evidence or the cost. And they fail at the back, where nothing governs what reaches production. We work both ends — an architecture practice and a governance practice in one firm, so the design and the controls are built from the same understanding of the system.
- 01
Scope
Define the problem, the decision it serves and what success would have to look like.
- 02
Architect
Design the system that meets it — with the trade-offs, costs and control points explicit.
- 03
Govern
Put the operating model, controls and stage gates in place so it can be run and evidenced.
- 04
Assure
Assess the risk, evidence the residual position and support an explicit decision.
Seven defined engagements. Three disciplines.
Turn an AI ambition into a design an engineering team can build and a board can fund — scope, feasibility, options, cost and target architecture.
Typically bought byCTO · Head of Architecture · Engineering leadership
AI Discovery & Scoping
Define the problem, requirements, feasibility, options, costs and delivery approach.
02AI Solution Design
Implementation-ready enterprise AI architecture and technical design.
03AI Architecture Review
Independent assessment of an existing AI architecture.
04Enterprise AI Platform Architecture
Organisation-wide target and reference architecture with a transition roadmap.
Make AI governable: an operating model your organisation can actually run, and a defensible view of the risk in each system before it reaches production.
Typically bought byCISO · CRO · DPO · AI Governance lead
Retained architecture authority — design decisions, review board attendance and governance continuity without a permanent hire.
Typically bought byCTO · CIO · Transformation leadership
Governance mapped against what actually applies to you.
An AI policy that cites every framework and lands on no obligations is decoration. We map your systems to the roles, classifications and duties that follow from them, then show exactly which controls exist, which do not, and what evidence you would be asked for.
EU AI Act
RegulationLikely role and classification per use case, the technical and organisational obligations that follow, existing controls mapped against them, and the evidence gaps that remain.
ISO/IEC 42001
Management system standardAn AI management system structure — policy, objectives, roles, lifecycle controls and internal audit hooks — shaped so certification is achievable rather than assumed.
NIST AI RMF
Risk frameworkGovern, Map, Measure and Manage translated into named owners, stage gates and measurable evaluation requirements rather than a reading exercise.
UK regulatory expectations
Principles-based regimeThe cross-sector principles and the expectations of the regulators that actually supervise you, reconciled with the controls you already operate.
Sector-specific requirements
Supervisory rulesFinancial services, insurance, health and public sector obligations — model risk management, operational resilience, safeguarding and procurement rules — folded into the same control catalogue.
We deliver readiness and alignment, not a legal opinion. Formal interpretation of any regulation remains with your legal counsel.
Every assessment ends in a decision, not a document.
A risk and impact assessment that concludes "there are some risks" is unusable. Ours walks one system from context to residual risk and closes on an explicit, recorded position that an architecture review board, a governance committee or an executive can sign.
How the assessment works →- 01 Proceed Residual risk is acceptable as designed.
- 02 Proceed with controls Acceptable once named controls are in place and owned.
- 03 Redesign The residual position cannot be reached from this architecture.
- 04 Escalate Specialist or legal assessment required before a decision can be taken.
- 05 Do not proceed The impact cannot be controlled to an acceptable level.
Two routes into the same problem.
A CTO buys architecture. A CISO, CRO, DPO or AI governance lead buys risk and governance. Inside an organisation those disciplines intersect immediately — which is exactly where most programmes come apart.
AI architecture and technical design support.
You need to build it properly.
Scoping, solution design, independent review and a platform architecture that stops every team rebuilding the same foundation.
Start with Solution Design →You need to be able to defend it.
A governance framework you can operate, a control catalogue mapped to regulation, and per-system assessments that produce decisions.
Start with Governance Readiness →The brand line
Architecting intelligence, engineering success.
From idea to controlled production. Architecture that survives contact with production, and governance that survives contact with a regulator.
Bring us the problem, the architecture or the regulatory question.
A scoping conversation costs nothing and ends with a written view of what the engagement would be, what it would produce and what it would cost.