From frontier technology to real‑world capability.
Lumen AI helps deep-tech ventures and organisations turn complex technology into clear commercial strategies, fundable propositions, and capability that gets adopted.
It's rarely the science that fails.
Complex technology rarely fails because the science is weak. It fails when the commercial story is unclear, the right use case has not been chosen, or nobody owns the path from idea to delivery.
I help leaders decide where AI can create value, turn technical work into a proposition people can understand and fund, and move from strategy to delivered capability.
Lumen AI connects those pieces.
Four ways to move from complex technology to useful outcomes.
Lumen AI works across the full journey: deciding what is worth doing, explaining why it matters, and helping the right people deliver it. Open any one below to see the detail and how the work runs.
AI strategy and roadmap From experimentation to a plan that ships.
Many organisations have run AI pilots. Fewer have a clear path from pilots to production value. I help you identify where AI can make a meaningful difference, assess readiness, prioritise opportunities by value and feasibility, and create a roadmap your team can execute, with the evaluation and governance standards that keep it safe as it scales.
- Opportunity assessment
- AI use-case prioritisation
- Data and capability assessment
- Operating-model design
- Evaluation and governance standards
- Roadmaps linked to commercial outcomes
Enterprise, government, and public-sector teams that need to move beyond disconnected experiments.
- DiscoverBusiness, data, goals
- AssessOpportunities & readiness
- PrioritiseBy value & feasibility
- RoadmapPlan + standards
Deep-tech commercialisation and investor narrative Make complex technology understandable, credible, and commercially relevant.
Deep-tech ventures often have extraordinary technical work but struggle to explain the market problem, customer value, or path to adoption. I help translate dense technical ideas into positioning, business cases, investor narratives, partnership propositions, and market-ready products. Having raised capital as a founder and built the investor narrative for a physics-based AI venture, I know what convinces technical reviewers and investors alike.
- Market positioning
- Customer and use-case definition
- Commercialisation strategy
- Investor decks and narratives
- Business cases
- Technical due-diligence support
- Partnership strategy
- Product and go-to-market thinking
Founders, research teams, and technology leaders commercialising frontier AI, physics-informed AI, scientific computing, or other complex technologies.
- DecodeUnderstand the science
- PositionDefine the moat
- BuildNarrative & investor deck
- ValidateReady to raise
Responsible and human-centred AI AI that stands up to scrutiny and works for real people.
AI used by customers, citizens, employees, or vulnerable communities needs more than technical performance. It needs to be understandable, testable, safe, and appropriate for its context. With foundations in cognitive science and a track record of designing AI for regulated and high-stakes settings, I help teams build responsible AI into the work from the beginning rather than treating it as a final compliance exercise.
- Risk and context assessment
- Bias and safety testing
- Evaluation design
- Explainability and human oversight
- Human-centred product design
- Responsible-AI standards
- Preparation for audit and scrutiny
Teams working in regulated, high-stakes, public-facing, or socially sensitive environments.
- FrameContext, users, risks
- EvaluateSafety, bias, performance
- DesignSafeguards & UX
- EmbedStandards, audit-ready
Fractional AI leadership Senior AI direction when you need it, with the practical work to make it happen.
Some organisations have strong data, science, or engineering capability but lack the senior direction needed to turn it into a coherent AI programme. As a fractional AI lead, I help decide which bet is worth making, define the work, source the right specialists, and keep delivery connected to commercial milestones.
- Setting AI direction and priorities
- Scoping and briefing technical work
- Defining standards and success measures
- Sourcing and managing specialist partners
- Connecting research, product, and commercial teams
- Advising leadership teams
- Keeping delivery focused and accountable
Organisations that need experienced AI leadership without immediately hiring a full-time executive.
- DirectSet strategy & the bet
- ScopeBrief the work
- SourceManage build partners
- DeliverRun delivery & advise
Technical depth. Commercial judgement. Human-centred responsibility.
I bring more than 20 years across AI research, government innovation, startup building, and enterprise delivery.
I call it Integrated Intelligence: understanding the underlying technology, shaping the product and commercial case, and holding responsibility to the people it touches, in one pair of hands rather than passed between three different advisors.
Few advisors hold all three at once. Tap each to see what it brings.
Tap or select any circle to see what it brings, or the centre for all three.
For teams moving beyond AI experimentation.
Deep-tech and physics-AI ventures
You have serious technical capability, physics-informed AI, scientific computing, frontier research, and need a sharper commercial position, an investor story, a customer proposition, and a route to market.
Questions I help answer- Which use case should we lead with, and which ones are distractions?
- How do we explain what this does to someone who isn't a deep tech expert?
- Is this solving a problem a customer will pay for, or one we find interesting?
- What does our investor narrative need to say that the technical deck doesn't?
- Who is the first customer, and what would make them move?
Enterprise and government research labs
You have R&D with commercial potential and the mandate to apply it, and need help connecting the science to customers, partners, funding, and adoption.
Questions I help answer- Which opportunity is worth the investment, and in what order
- Why pilots stall, and what has to change for one to reach production
- Whether the data, systems, and operating model can carry it
- What standards need to exist before scaling something public-facing
- How to connect research with commercial potential to customers, partners, and funding
I've spent my career moving technology from the lab into the real world.
I am an AI and deep-technology commercialisation leader with more than 20 years across research, government innovation, startup building, and enterprise delivery.
My PhD, from the University of Sheffield in the UK, was in natural language generation, an early foundation of today's generative AI. At A*STAR Singapore I worked across computing, cognitive science, and computational modelling. At Callaghan Innovation in New Zealand I moved closer to industry, working with startups and established organisations on product-market fit, product development, funding, and the commercialisation of emerging technologies.
I then founded Inclusys, taking technology from Callaghan into a funded startup, and built NLP-powered learning products for children with special needs, including Talk With Me and WeeWords. More recently I built and led Datacom's AI Solutions practice in New Zealand, delivering multi-million-dollar AI projects for major enterprise and government clients.
Work with me →
- PhD in natural language generation (University of Sheffield)
- 20+ years across AI and emerging technology
- Built and led an enterprise AI practice
- Founder of a funded education technology company
- Research and commercialisation for Singapore & New Zealand government organisations
- NLP products used by thousands of children
- 40+ peer-reviewed publications
Have a complex AI or deep-tech challenge?
Tell me what you are trying to commercialise, scale, fund, or deliver. A short note is enough. Please include where you are now, what is stuck, and what a useful outcome would look like.
Lumen AI works with a limited number of engagements at a time so the work stays focused and practical.