Expert analysis and practical recommendations for advanced manufacturing OEMs that need clarity before committing to action.
We provide engineering consultancy to advanced manufacturing OEMs, helping them diagnose engineering, operational, and cost challenges before significant investment decisions are made. We act as an advisory partner, not a delivery contractor. The output is clarity: a clear picture of where the problem lies and what your options are.
Our operations consultancy applies Operational Research methods to surface the hidden costs and inefficiencies that internal teams, too close to the process, often cannot see. And every engagement closes with Lessons Learned and Best Practices documentation that stays with your team, so the value doesn’t walk out the door when we do.
What sets our advice apart is that it comes from engineers. Our consultants combine engineering and operational expertise, so the recommendations you get are grounded in technical, as well as business reality, operational impact, and long-term cost, not just a framework applied from the outside. All engagements are delivered under EU GDPR and IP protection frameworks.
Our consultancy currently covers two areas. Both are advisory: we diagnose, analyze, and recommend, and you decide what to do with the conclusions.
Product development & engineering advisory
Drawing on our accumulated research and industry expertise across advanced manufacturing sectors, we provide expert knowledge and advice to help you address complex technological challenges and engineering problems. This is product development consulting for OEMs who need an external expert perspective on a specific challenge before deciding whether to build, buy, or partner. A typical advisory engagement includes an in-depth diagnostic assessment of the problem, structured analysis, and clear recommendations you can act on, whether you continue working with us afterward or not.
Cost optimization
We use Operational Research methods, process mapping, statistical analysis, mathematical modeling, and algorithmic optimization to identify the hidden costs that surface years later in a production facility. Our work covers cost-driven decision-making from early concept development through to scalable production, helping you minimize both development and operational costs before they are locked in. Every engagement closes with Lessons Learned sessions and Best Practices documentation, converting tribal knowledge into institutional knowledge that stays with your team long after we leave. This is engineering process improvement that compounds, because the next project starts from what the last one learned.
We work with advanced manufacturing OEMs facing complex engineering, operational, or cost challenges. Much of our experience comes from sectors such as semiconductors, aerospace and defense, medical technology, industrial machinery, and advanced materials, but the consultancy is defined by the challenge, not the industry. If the problem is technical, operational, or cost-driven and the next step is unclear, it is the kind of problem we are built to work on.
Clients typically engage us when:
This is decision support, not engineering delivery. If what you actually need is the work executed rather than advice, our other services are the better route, and the section below points you to the right one.
A consultancy engagement tends to end in one of three ways.
The analysis may confirm that your current direction is sound. The value is in that certainty: you proceed knowing the ground has been checked, and nothing further is needed from us.
It may show that the real constraint is internal capacity rather than direction. In that case, our Dedicated Industrial Engineering Teams can pick the work up and keep it moving, with engineers who already understand the context embedded alongside yours.
Or it may point to a specific design or development effort that needs to be owned end to end. That is where our Design Engineering service takes over, delivering the work from system architecture through to NPI.
In all three cases, the consultancy stands on its own. You are not obligated to continue with us, and our recommendations are not designed to push you toward further work. But because we already understand your context by the end of the engagement, picking up the next step with the same team typically saves weeks of onboarding. The team that diagnoses the problem is the same team that can build the solution, so there is no gap between knowing what to do and getting it done. The next step never starts from zero: no re-onboarding, no second discovery phase, no rebuilding the context you already paid to establish.
For a semiconductor OEM whose multi-module systems were losing availability to module faults on the fab floor, we developed deterministic diagnostic flows that trace each symptom back to a single root cause, so operators can both identify and fix the error. The approach cut system downtime at end-user sites sharply and was adopted by the client as their standard diagnostics method across new projects.
A lithography customer needed to detect contamination on reticles to sub-micron accuracy, as early in the process as possible. We built a three-dimensional reticle surface model from data the system already collects during calibration, added a new KPI to flag any divergence from a flat surface, and tuned it against contaminated samples to remove false readings. The model reached a success rate above 96% on a small data set, at negligible execution cost, and now runs inside the standard calibration sequence.
On a large-scale forensic microscopy program, we developed the algorithms that identify and classify trace evidence across six categories, from hair and fibers to glass and dirt. By combining multi- and hyperspectral, fluorescence, and polarization imaging with geometrical and optical properties, the algorithms discriminate traces with high accuracy and return similarity indexes that tell a lab whether a sample matches a known reference. The output is editable by lab operators and portable between facilities.
An OEM with long, complex development cycles could not see how design decisions would affect manufacturing and service costs, because that impact only surfaced years after implementation, well outside the view of its NPI teams. Working across development, logistics, and suppliers, we ran cross-sector workshops to pin down the main cost drivers, identified overlapping and contradicting design requirements to merge or eliminate, and built an in-house stochastic model that uses design parameters to calculate the cost of existing tools and forecast the cost of new designs. The model now drives target setting, cost engineering, and inventory optimization, giving design teams a cost-driven view at the point of decision rather than years later.
For a semiconductor OEM whose multi-module systems were losing availability to module faults on the fab floor, we developed deterministic diagnostic flows that trace each symptom back to a single root cause, so operators can both identify and fix the error. The approach cut system downtime at end-user sites sharply and was adopted by the client as their standard diagnostics method across new projects.
A lithography customer needed to detect contamination on reticles to sub-micron accuracy, as early in the process as possible. We built a three-dimensional reticle surface model from data the system already collects during calibration, added a new KPI to flag any divergence from a flat surface, and tuned it against contaminated samples to remove false readings. The model reached a success rate above 96% on a small data set, at negligible execution cost, and now runs inside the standard calibration sequence.
On a large-scale forensic microscopy program, we developed the algorithms that identify and classify trace evidence across six categories, from hair and fibers to glass and dirt. By combining multi- and hyperspectral, fluorescence, and polarization imaging with geometrical and optical properties, the algorithms discriminate traces with high accuracy and return similarity indexes that tell a lab whether a sample matches a known reference. The output is editable by lab operators and portable between facilities.
An OEM with long, complex development cycles could not see how design decisions would affect manufacturing and service costs, because that impact only surfaced years after implementation, well outside the view of its NPI teams. Working across development, logistics, and suppliers, we ran cross-sector workshops to pin down the main cost drivers, identified overlapping and contradicting design requirements to merge or eliminate, and built an in-house stochastic model that uses design parameters to calculate the cost of existing tools and forecast the cost of new designs. The model now drives target setting, cost engineering, and inventory optimization, giving design teams a cost-driven view at the point of decision rather than years later.
If you need engineering work executed rather than advised on, these are the services to look at:
Most engagements start with a 2–4 week diagnostic phase: scoping, data review, and stakeholder conversations. From there, we present findings and recommendations, then either close the engagement or move into a deeper analytical phase if the scope expands. The deliverable is a written analysis with findings, recommendations, and, if you want one, an implementation roadmap.
We are engineers first. Our consultants have hands-on experience designing, building, and running complex systems in semiconductor, aerospace, medical, and other advanced manufacturing sectors. Large management consultancies often bring frameworks and analysts; we bring people who could implement the recommendations themselves if you asked them to.
Yes. Many engagements start with a focused diagnostic: a single process, a specific cost question, or a defined product development decision. The diagnostic phase is scoped and priced separately, so you can evaluate the working relationship and the quality of the analysis before committing to anything larger.
Diagnostic engagements typically run 2–6 weeks. Deeper analytical or improvement engagements can extend to 3–6 months depending on scope. We scope each engagement with clear milestones and decision points along the way, so timelines are agreed upon upfront rather than left open-ended.
You do. All analysis, models, recommendations, and documentation produced during the engagement are your intellectual property. As an EU-based engineering partner under full EU IP protection frameworks, contractual enforcement is the same as with any other European company.
Tell us what you are trying to solve. In a 45-minute call, our engineering and operations leads will review your challenge and come back with a clear picture of where the problem lies and what options exist.
