Strategy for Applied AI where the wrong business outcome is expensive.

The decisions made before any code is written. What is worth building, what evaluation looks like, what the risk profile actually is.

This is where most AI investments are won or lost. I work with leadership teams to clarify what success looks like in operational terms, what the right evaluation metrics are for the actual risk surface, and where the proposed architecture has structural weaknesses that will not surface until production. The output is not a deck — it is a defensible decision.

A regulated-sector platform team has been told by a vendor that their LLM evaluation is "97% accurate." A two-week strategy engagement establishes that the test set excludes the highest-risk category of user query, that the accuracy metric is structurally insensitive to the harms the system actually creates, and that the organisation needs a different framework before scaling. The engagement ends with a written architecture review, a recommended evaluation framework, and a clear go / no-go on the deployment.

• Architectural & evaluation review
• Risk-aware evaluation framework
• Risk & governance assessment
• Build / buy / partner analysis
• Strategy memo & written recommendation
• Fractional Chief AI Officer engagement

Who this is for Boards, executive teams, and product leaders making capital decisions on AI investments — particularly in regulated or high-stakes environments where the cost of a wrong call is measured in compliance exposure, reputation, or harm.

The discipline of turning research and prototypes into systems that survive contact with real users — taught and applied.

A working prototype is not a product. The gap between the two is where most AI initiatives stall. I work directly with engineering and product teams on the mechanics — evaluation pipelines, release governance, observability, regression discipline — and I run programmes that build this capacity inside organisations rather than holding it outside them. Twenty years of implementing software in regulated environments inform this work; the editorial discipline is the same whether the product is a banking system or a clinical-decision tool.

A founder cohort enters a six-week productization sprint with prototypes that work in demos but fail under uneven inputs. The sprint establishes evaluation harnesses, regression testing, and release-readiness criteria specific to each prototype. The cohort exits with shipped, monitored, governed products — and a productization playbook each team continues to use.

• Embedded productization advisory
• Evaluation harness design & build
• Release governance & regression discipline
• Founder cohorts & productization sprints
• Executive education programmes
• Curriculum design & delivery

Who this is for Founders, engineering leaders, and learning-and-development teams responsible for moving AI work from demonstration to deployment, and for building the institutional capability to do it repeatedly.

For domains where the cost of being wrong is high, technical and policy questions are inseparable. I work on both.

Governance is not a compliance overlay. It is a structural property of the system — present in the choice of evaluation, in what gets logged and reviewed, in who is accountable when an output causes harm. My doctoral research at UMBC's KnACC Lab is directly on this terrain: how AI systems should evaluate content in environments where being wrong is expensive, and what proportionate, risk-aware evaluation looks like in practice. That work informs the advisory.

A platform faces regulatory pressure on its content-moderation pipeline. A governance engagement maps the system's structural blind spots — including where the evaluation framework systematically silences legitimate speech and misses genuine harm — and produces a revised governance architecture that is both more defensible to regulators and demonstrably less harmful to affected users.

• AI policy advisory & review
• Platform governance frameworks
• Health & financial AI evaluation
• Content moderation architecture review
• Expert review & written testimony
• Standards & regulator engagement support

Who this is for Platforms, regulators, foundations, and public-sector bodies whose AI systems sit on the policy boundary — and who need work that is technically rigorous and policy-fluent at once.