Specialisation
Applied artificial intelligence, machine learning integration, and intelligent systems for product and enterprise.
Engagement with artificial intelligence and machine learning as a practitioner predates the technology becoming mainstream. The AI work here is grounded in engineering reality — understanding both what current AI systems can genuinely do and what they cannot, and applying that understanding to produce outcomes that deliver real business value.
Building AI-native products requires a different strategic and product mindset than traditional software. Advisory covers how to identify genuinely valuable AI product opportunities, design user experiences that leverage AI effectively, and build the data foundations that AI-driven products require.
Product strategy starts with the user problem and works backwards to the AI capability — not the reverse. Too many AI products begin with a capability looking for a use case; this approach ensures the product is solving a real problem in a way that AI makes meaningfully better.
Large language models represent a genuine capability step change for tasks involving language, reasoning, and knowledge synthesis. Advisory covers LLM selection, deployment architecture, prompt engineering, retrieval-augmented generation (RAG) system design, and the integration of language models into production workflows and user-facing products.
Practical experience building LLM-powered applications — including document intelligence systems, automated research tools, and conversational interfaces — ensures advisory is grounded in what actually works in production rather than benchmark performance.
Integrating machine learning into existing products and workflows requires careful engineering — managing model serving infrastructure, handling prediction latency, designing feedback loops for model improvement, and monitoring for model drift. ML systems have been architected end-to-end, from data preparation through model training and evaluation to production deployment and monitoring.
Both off-the-shelf models and custom-trained systems are considered, selecting the approach appropriate to the specific use case based on accuracy requirements, latency constraints, data availability, and cost profile.
The application of AI to investigative and intelligence work is one of the most significant capability shifts in the field. AI techniques — including entity extraction, relationship mapping, anomaly detection, and large-scale document analysis — are applied to investigative workflows, enabling analysis at a scale and speed that was previously impossible.
This intersection of AI capability with investigative methodology is a distinctive specialisation — and one that is increasingly in demand as the volume of digital information available to investigators continues to grow exponentially.
The deployment of AI systems carries risks that differ in character from traditional software risks — including bias, hallucination, adversarial manipulation, and the concentration of decision-making power. Advisory covers AI risk frameworks, governance structures, and compliance with emerging AI regulation, including the EU AI Act.
AI governance work draws on cybersecurity background — applying security thinking to AI systems, including model security, input validation, output monitoring, and red-teaming AI applications to understand their failure modes before deployment.
For larger organisations, AI adoption is as much an organisational challenge as a technical one. Advisory on AI transformation programmes helps leadership teams understand the genuine landscape of AI capability, identify priority use cases, build data and infrastructure foundations, develop internal AI literacy, and govern AI deployment responsibly.
The rigour of KPMG consulting background is brought to this work, ensuring AI transformation programmes are grounded in business reality, properly governed, and designed to deliver lasting competitive advantage rather than short-term technology theatre.