Science-led companies rarely stall because the technology fails.
They stall in the gap between invention and market.

Hardware carries an asymmetry that software does not. An unvalidated iteration in software costs a sprint. In hardware it costs a tooling run, a supplier qualification, most of a year and a visible share of the round, and you find out whether you were right at the end rather than in the middle. Validated learning before commitment matters more in deep tech than in software, not less, which is the reverse of how most early hardware teams are resourced.Capital is usually not the binding constraint. Well-funded companies with real science stall anyway, and four failure modes account for most of it:

A technology readiness level is treated as a roadmap. TRL 6 tells you the device works in a relevant environment. It tells you nothing about which customer, which use case, or at what price.Technical validation is mistaken for market validation. The demo impresses other physicists. Nobody has yet asked a buyer what job they are trying to get done, or what they do instead today.The prototype does not survive contact with manufacturing. Tolerances that were fine when a postdoc built three units by hand become a yield problem at three hundred, and the cost model built at prototype stage was never real.There is no agreed way of deciding when to stop investigating and start committing. The decision defaults to whoever is most confident in the room, or to the fact that the build has already started.

None of these are science problems. They are product problems, and they are cheap to fix early and expensive to fix late.

Commercialisation Readiness Review

A five to eight day assessment ending in a written report your board can act on. I look at where your technology readiness and your market readiness have diverged, name the three things most likely to kill your next product introduction, and map the stage gates you have skipped. Fixed scope, fixed fee, no retainer.Best for seed and Series A hardware companies about to commit real money to a build.

Development Process and Governance

A gate structure your team will actually use: what has to be true before money is committed, who decides, and what counts as evidence. Built around your stage rather than lifted from a textbook, with the jobs-to-be-done research, business model work and delivery tracking that feed the gates.I have built this twice, in organisations with very different definitions of a good investment, which is where you find out which parts adapt and which are load-bearing.

Fractional CPO / CTO

Senior product and technology leadership on a part-time basis for teams who need the function before they can justify the headcount. Roadmap ownership, R&D prioritisation, engineering leadership, and the governance to make decisions stick. Typically one to two days a week over six to twelve months.

Defined Projects

Scoped work with a clear end point: an NPI cycle, a design-for-manufacture and cost review before you commit to tooling, supplier qualification, measurement and test system design, or the technical narrative behind an investment round.

The full arc, without a handoff

I joined an RF hardware startup as founding CTO and built the engineering function from nothing, including a $1.1m wireless test laboratory I specified, costed, commissioned and then ran. I filed the patents underpinning the core technology and drove full NPI cycles into US nationwide retail. After acquisition I took the CPO role and owned every product line across a $20m business, managing contract manufacturers in Asia and taking $615k a year out of manufacturing cost. As Chief Scientist I ran a product line P&L, drove a 4x performance improvement through materials science, and built a B2B licensing programme that reached a quarter of the line's revenue.Nine US patents. I have taken a product through US federal regulatory approval end to end, and acted as senior technical representative to the FCC on RF exposure standards. What transfers is treating approval as a costed, scheduled gate with an unpredictable lead time, rather than as paperwork at the end. That assumption is one of the more expensive ones available to a hardware company.

Why this transfers, whatever your technology is

I am not selling domain expertise in your science. You already have that, and it is usually the reason the company exists. What generalises is the machine you have to build around it: measurement you can defend, tolerances that survive a hundredfold increase in volume, a supply chain that will still quote you in eighteen months, a regulatory route costed before it becomes the critical path, and gate decisions made on evidence rather than on how far along the build already is.That machine looks broadly the same whether you are building a medical device, a health or diagnostic product, a cleantech system, a scientific instrument, or something that does not have a category yet. I have built it once, from nothing.

The Science is Not Decoration

My first degree was an MPhys in Physics with Technological Physics at Manchester, which is physics aimed deliberately at the point where it meets engineering and application. I stayed on for a PhD in laser physics.My postdoctoral work at the Beckman Laser Institute was femtosecond laser ablation for surgical applications, published as first author in the Journal of Biomedical Optics.From there I moved to a private research lab, building the diagnostic optical instrumentation for an experimental superconducting plasma centrifuge.Then fourteen years of RF hardware, where what carried over was not the physics but the habit: measurement before opinion. Building the test laboratory came first, because you cannot improve what you cannot measure.It is the same question in a product review as at the bench: what is the hypothesis, what is the cheapest test that could disprove it, and what result would make us stop?

Independent Standing

I work as a UKAS Technical Assessor, assessing laboratory competence against ISO/IEC 17025: measurement systems, method validation, traceability, data integrity, and whether the science supports the claim.

Photonics and laser systems · Biomedical applications · RF and wireless · Precision instrumentation · Materials science

Fit

Companies with credible technology and a product function that has not been built yet. University spinouts moving from grant funding toward commercial revenue, seed and Series A hardware startups approaching first volume production, and engineering SMEs whose development process has stopped scaling with them. Post-funding, pre-revenue to early commercial is where I am most useful.