We plug the gap between simulation and infrastructure
Nobody owns it. Your modellers do not, and your infrastructure team does not either. That is the part we build: the conduit between the two. Not data engineering, not IT.
Our engineers have worked in semiconductor fabrication, oil and gas and public sector analysis. Our team includes PhD level mathematicians and physicists. We build so you can take the work back in-house whenever you want it.
Does this sound familiar?
Most teams find us when modelling has quietly turned from an advantage into a constraint.
Your analysis no longer scales past individual machines, or past one person’s laptop
Solver code, in C, Fortran, or any other older language, that has become risky to extend
Desktop or on-premise tools that now need secure cloud or HPC access
Parameter searches and bursty, high-volume runs your hardware cannot absorb
Results nobody can find again, because runs are not searchable across the team
If you recognise any of these challenges, more hardware will not save you. The shape of the system is the problem.
Why this is harder than it looks
Simulation is not a data science problem, so data science tooling does not fit it. It is not a general IT problem either, so infrastructure teams solve the wrong half of it and move on.
So the work lands nowhere. It gets picked up in the gaps between other people’s jobs, by whoever has time that month, and it gets rebuilt when that person moves on. Most teams pay for it twice before they call anyone.
What we have done
Semiconductor optics simulation
We refactored licensed legacy simulation code into modular, cloud-ready workflows, so teams could run high-volume bursty simulation. The client put the saving at over €60m in efficiencies.
Geophysics simulation
Subsurface simulation, modelling and visualisation.
Structural and civil telemetry
Structural health monitoring and cloud modelling.
Government statistics and analysis
Including work for the Office for National Statistics.
Examples of sectors we have delivered in: semiconductors, automotive, geophysics, energy, health and life sciences, civil and structural engineering, government statistics.
Multiple repeat clients, every engagement completed or ongoing.
Named references available on request.
How to start
A feasibility assessment
Short and bounded. Is this possible? What would it take? Where are the risks? Deliberately low cost and low risk, and the output is yours whether or not you go further.
Analysis scale-up
Architecture, implementation and deployment for scalable analysis, built on design patterns we have already proven, so you are not discovering them at your own expense.
Team extension
Our engineers embedded in your team, from sprint tickets to multi-year programmes, with pre-security-cleared staff available for sensitive government or commercial work.
Support for the open tools you depend on
Open source drives your research, and formal support for it barely exists. We cover that gap on a time-boxed basis, so your team stays on the science rather than the stack.
What you keep
This matters more than it sounds, so we will be explicit.
Your code stays yours. Client-owned code remains client-owned, with clear IP boundaries agreed at the start rather than argued about later.
No lock-in. We build systems you can search, inspect and eventually run without us. Not black boxes.
Knowledge transfer is part of delivery, not an afterthought and not an upsell.
Open source used strategically, not ideologically, and with due caution for commercial sensitivity. Where a proprietary tool is the right answer, we will tell you so.
What stops you simulating more?
A few sentences on where your analysis stops scaling is enough for us to tell you honestly whether this is something we can help with. If it is not, we will say so. If it is, the usual first step is a short feasibility assessment, and the output is yours either way.