Leadershift
Studio

AI Strategy & Integration
 

Most companies don't have an AI problem; they have a "where do we even start" problem. I help leadership teams cut through vendor noise, map AI opportunities against how work actually gets done, and decide what to build, buy, or leave alone. The starting point is always the real workflow followed by embedding AI where it earns trust and gets adopted, rather than where it looks good in a slide. 

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    Key Outcomes​​

• A prioritised AI roadmap tied to business objectives, not to hype

• Build / buy / partner decisions made on evidence

• Tools that people across the organisation actually use

• Honest expectations about what AI can and cannot do for you today

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Production AI & LLM Systems
 

Getting from a working prototype to a system that runs reliably every day is where most AI efforts stall. I have spent years productionising AI models - designing fault-tolerant, scalable inference systems and the infrastructure around them, and building practices that make them observable and maintainable. I bring that to teams who need AI to be dependable, not just impressive.

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    Key Outcomes​​

• AI features shipped to production and kept there

• Architectures that stay sustainable as usage and complexity grow

• Faster, more reliable delivery pipelines

• Reduced dependency on any single model, vendor or cloud

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AI Cost, Performance & Model Optimisation
 

AI bills grow quietly. My doctoral research was on automatically optimising cloud configurations for cost and performance. I have worked on projects that led to cutting production inference costs while improving throughput and reducing latency. I help companies choose the right models, infrastructure and trade-offs for their quality, latency and budget constraints - and set up a way to keep re-evaluating as the landscape shifts.

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    Key Outcomes​​

• Measurable reduction in AI and infrastructure spend

• Model and vendor choices matched to actual quality requirements

• Performance gains without over-provisioning

• A repeatable, empirical method for evaluating options

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AI & Engineering Team Leadership
 

Fractional or interim technical leadership for teams building with AI. I have led AI Ops, AI Productisation and Model Deployment teams, set technical direction, and worked day-to-day with Research, Product and customer-facing teams to keep them pulling in the same direction. I help founders put in place the practices - hiring, code review, onboarding, knowledge sharing, development plans - that let a small team ship quality work consistently.

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    Key Outcomes​​

• Senior technical leadership without a full-time executive hire

• AI, product and research teams aligned on priorities and trade-offs

• Engineering practices that scale with the team rather than against it

• Stronger hiring decisions and mentoring for engineering staff

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AI for Research & Knowledge Work
 

Organisations whose core work is thinking such as research groups, consultancies, think tanks, and R&D functions, have a specific integration challenge: the work is judgement-heavy and the cost of a confident wrong answer is high. I currently lead translational AI efforts at an academic research lab, building tools such as a multi-agent research-automation harness, an AI co-scientist, and a research recommendation system. I help knowledge-intensive organisations embed AI into how they discover, evaluate and produce work.

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    Key Outcomes​​

• AI assistants embedded in real research and knowledge workflows

• Human-in-the-loop by design, not as an afterthought

• Faster literature review, knowledge discovery and synthesis

• Agentic systems with memory and trustworthy outputs