Independent AI SDLC & engineering advisor
Turn AI coding adoption into reliable software delivery.
I help CTOs and engineering leaders redesign the SDLC around AI—so faster implementation does not create slower review, weaker quality, or unclear accountability.
Typical starting point
AI tools are moving faster than delivery.
- Generated code rises; review capacity tightens.
- Pilots multiply without a shared path to scale.
- Quality, security, and accountability stay unclear.
Two common starting points
AI is changing the work—or growth is exposing the operating model.
AI SDLC
Move beyond scattered AI coding use.
AI tools are spreading faster than team practices. Individual gains are hard to translate into predictable delivery, while review, testing, context, architecture, security, and accountability become the new constraints.
- Established IT and internal-product teams
- Mature or legacy SaaS products
- Brownfield engineering organisations
Engineering operating model
Make the organisation scale with the business.
Strategy exists, but decisions, ownership, architecture, and delivery flow no longer fit the company’s stage. AI may be part of the situation, but it is not the only constraint.
- Scaling product and engineering organisations
- Founder, CTO, CPTO, and VP Engineering transitions
- Targeted transformation or advisory-board questions
The leadership tension
More generated code does not automatically mean faster delivery.
AI compresses implementation unevenly. The bottleneck moves toward intent, context, architecture, review, testing, release, and accountable decisions. It also amplifies the delivery system already in place.
- Weak product intent becomes more output in the wrong direction.
- Poor architecture makes safe delegation harder.
- Limited verification capacity turns speed into risk.
- Unclear ownership makes AI-assisted results difficult to trust and operate.
The objective is not more generated work. It is better software delivery with evidence.
Ways to work together
Start with the constraint. Choose the smallest useful intervention.
AI SDLC Diagnostic
Assess current AI working modes, delivery constraints, context and platform readiness, verification, governance, roles, and measurement. Leave with a target profile and practical next steps.
Engineering Operating Model Diagnostic
Map how strategy becomes decisions and delivery, expose structural friction, and define a focused improvement path for the next stage of growth.
AI Enablement for Engineering Teams
Build shared practice around useful workflows, problem slicing, context, agent supervision, verification, and responsible adoption—not generic prompting.
Targeted Executive Advisory
Structured support for founders, CTOs, CPTOs, VPs Engineering, and selected boards where engineering strategy, architecture, operating model, or AI-enabled delivery is material to growth.
My role
Independent senior judgement across product, engineering, architecture, and change.
I have worked as a software engineer, architect, CTO, VP Engineering, Head of Engineering, CPTO, consultant, trainer, and facilitator. That breadth matters when a problem looks technical but is actually distributed across strategy, organisation, architecture, capability, and leadership.
I do not sell an AI platform, development capacity, or a standard transformation framework. The work begins with understanding the system and the decisions leaders need to make.
New workshop and self-check
Move from assisted coding to governed delivery.
New AI SDLC workshop
From Assisted Coding to Governed Delivery
A half-day working session for engineering, product, platform, architecture, and security leaders. Map current working modes, identify the next constraint, and leave with one risk-bounded pilot charter.
Visitor self-check
Where is your AI SDLC stuck?
Map your current working mode and six enabling capabilities. The result shows the most likely constraint and a practical next move, without reducing the organisation to one maturity score.
Start with a focused conversation
What is changing in your engineering organisation?
Share the trigger, the organisation’s context, and where progress feels blocked. The first conversation is to determine whether the real constraint is AI adoption, delivery flow, architecture, operating model, or leadership structure.