I help CTOs and engineering leaders introduce AI-assisted development and make it reliable in everyday work, from requirements and implementation to testing, review and release.
Start with one team, one workflow and clear acceptance checks. Paid scope and hour cap agreed before work begins.
AI workflows depend on the platform underneath. Identity, CI/CD, security and cost controls make a workflow maintainable. I connect AI adoption with the engineering systems your team already runs.
Examples of work we can scope together, depending on your priorities and environment.
Start with a team exploring AI-assisted development or improving its current use. Choose an approved tool, configure repository instructions and agree review expectations using the team's own tasks.
A first scope: one team, reusable guidance and an agreed way to review changes and track adoption.
Choose a repeatable engineering task. Define the context, permissions and acceptance checks, then configure a workflow with bounded retries and human escalation.
A first scope: one repository, one workflow and recorded results to decide what to improve or extend.
Explore the workflow →Bring an AWS architecture, cloud cost, CI/CD or platform migration problem. Review the design, agree a change and work with your engineers to implement and verify it. Cloud and platform work can be a standalone engagement.
A first scope: one documented problem, an agreed change and checks against the required behaviour.
We agree scope, access, paid hours and acceptance criteria before starting. You can hire me directly for this work without a prior assessment.
Work with me on a defined project or bring me in for regular hands-on support. We agree the scope, hours and working arrangements before starting.
Work directly with me on AI tooling, developer enablement, cloud architecture or delivery improvements. We agree priorities, a limit on paid hours, meeting time and working windows before starting.
Hourly · agreed availability Regular hands-on supportRate and hours agreed for your scopePut a defined change into practice: a governed agent workflow, Copilot adoption, an engineering platform improvement or an architecture change. Agree deliverables, access and acceptance criteria up front.
Defined project · clear deliverables A focused delivery scopeTimeline and commercial terms agreed together Explore an AI engineering pilot →For a wider decision before a larger investment. Reviews AI adoption, delivery bottlenecks, governance and measurement. You leave with a pilot design and a costed roadmap your team can use.
Optional · 4 weeks · fixed scope From £18,000Not required before hiring me View scope and deliverables →Illustrative example. This fictional scenario shows the format and decision logic. It is not a client document or evidence of a delivered result.
Two parts of my enterprise experience: a completed developer-platform migration and the organisational capabilities built around AI-assisted delivery.
Azure DevOps was the starting platform. The migration moved more than 14,000 users to GitHub, establishing the platform foundation for the subsequent AI-SDLC work.
My enterprise platform work included this migration. It sits alongside my experience in cloud architecture, DevSecOps and developer enablement: moving a platform and helping engineering teams work with it.
I lead the AI-SDLC transformation, connecting Copilot adoption, governance, developer enablement, custom agents and measurement.
Adoption gained named owners and an AI Champions community. Engineering groups gained tailored sessions, reusable instructions and a controlled path for distributing agents.
Project summaries focus on my contribution and delivery outcomes. Further details or references are subject to client permission.
Practical methods for a new engagement, illustrated separately from the delivery experience above.
Outcomes are measured against your own delivery data rather than vendor benchmarks.
The problem, success criteria and review boundaries, agreed with a named owner in your team.
A focused change on one team or one workflow, with planning and cycle time baselined before the work starts.
Delivery time, review effort and quality tracked before deciding what to expand. Autonomy is granted one workflow at a time, behind human approval gates.
Three self-serve assessments and frameworks drawn from the same work. No call required.
A 40-point self-assessment informed by NIST AI RMF 1.0, OWASP GenAI LLM Top 10 2026, ICO guidance, and practical delivery controls.
Run the assessment →Model activation, usage, and retained workflow value against the AI-SDLC measurement families, including the review-cost drag most ROI cases leave out.
Open the model →How to close the AI production gap: eliminating the ambiguity tax, handling context rot, and specifying work so agentic delivery stays reviewable.
Read the framework →Who this is for: CTOs, engineering leaders and platform owners with a concrete challenge, from a single team to an enterprise programme, with a named owner and agreed outcomes.
Consulting is delivered directly by me; larger programmes also need capacity from your own engineering team.
Yes. We can choose a first task and an approved tool, or improve a workflow using tools you already have. I work with your engineers on useful instructions, acceptance checks and review. An assessment is optional when priorities need a wider review.
Delivery metrics show outcomes. The AI value system measures licence activation, surface adoption, user-initiated versus agentic work, acceptance, planning or cycle-time change, quality and security, human intervention, and value per workflow.
It depends on the work. Architecture advice or an assessment can often start with delivery metadata, documentation and interviews. Implementation usually needs access to the relevant repository and tools. We agree the minimum access needed before starting.
You can review my professional background on LinkedIn. Further project details or references may be available, subject to client permission and confidentiality obligations.
Email me with the problem, your team and your preferred timing. We agree fit, scope, rate, a limit on hours, paid meeting time and availability before work starts. For a defined project, we also agree deliverables and acceptance criteria. If you use the optional diagnostic, you can separately request the measurement framework by email.
By not trying to reach every engineer personally. The transformation is principal-led and delivered through your own engineering organisation: I design the operating model, lead the first governed pilots, set the agent and review patterns, and train the team leads and champions who carry it into their teams. What scales is the system and the people you already employ, not consultant headcount. That is also why there is no delivery team to hand you off to.
No. You can start directly with part-time consulting or a scoped implementation. The four-week Transformation Assessment is a separate option for organisations that need an independent baseline and roadmap before a larger investment.
This is direct specialist consulting. If you need a fully outsourced delivery team or continuous operational cover, that requires a different staffing arrangement. We should agree the ownership and capacity your team can provide before taking on the work.
Tell me about the problem, your team and your preferred timing. We can discuss regular consulting, a focused implementation or an assessment when you need a wider roadmap.
[email protected] · Scope and availability agreed before starting