R&D in a startup is not a separate function. It’s an integral part of the product development process, especially in the early stages when requirements are uncertain, the solution is unclear, and the risk is high.
At Lumafield, I’ve worked on everything from early prototypes to complete product features like offset scanning and calibration systems. Over time, I’ve learned that effective R&D is not about having a perfect process. It is about thinking clearly, experimenting deliberately, and staying grounded in first principles.
Here’s how I approach the work.
Start with first principles
When tackling a new technical problem, I begin by reducing it to the fundamentals: physics, geometry, mechanical constraints. Early on, you often cannot rely on precedent. You need to understand what is objectively true and work forward from there.
This mindset helped us build scanning capabilities when we were still new to tomography. We didn’t import solutions from other companies. Instead, we reasoned through the challenges ourselves. This took more effort up front but allowed us to invent solutions that fit our specific needs and constraints.
Prototype to learn
In the early stages of product development, the purpose of a prototype is not to impress. It is to test a hypothesis.
A prototype should reduce uncertainty. Can we hit the resolution target? Will the calibration approach scale? Can we build this cost-effectively?
Sometimes the result is positive. Sometimes not. Either way, it moves the work forward. The key is to be clear about what you are trying to prove and avoid refining the details too early.
Use literature to build smarter and faster
We often begin with a literature review to understand the problem space. Research papers can clarify what has already been done, what techniques are available, and where current limitations exist. This helps frame our approach.
There is no need to reinvent the wheel. When researchers have already solved part of a problem or developed a method that fits into our pipeline, we use it. Applying proven components can accelerate development and avoid unnecessary work.
However, most published research is not optimized for product integration. Methods may assume ideal conditions, ignore cost constraints, or require infrastructure that is not practical in our environment. That is where adaptation comes in.
We borrow selectively, simplify when possible, and focus on what works reliably within our system. Understanding the principles behind a solution is more valuable than reproducing every detail. Our goal is not to replicate research but to apply it thoughtfully to build products that perform in the real world.
Align R&D with product outcomes
Our work is driven by product needs. Sometimes a request comes from the product team or a customer. Other times, we explore an internal idea that we believe could unlock value.
In both cases, we scope experiments tightly. We aim to answer key questions within a few days or weeks. This helps us avoid chasing technically interesting ideas that do not translate into real product improvements.
If a solution is imperfect but solves the problem in a useful way, that is often enough.
Focus on what customers actually need
In many cases, product requirements aren’t fixed. Customers may request specific performance targets, but in practice their needs are more flexible.
We’ve tested configurations that technically fall short of a stated requirement. Yet the results were good enough that the customer was satisfied, and even impressed.
Part of product development is figuring out where precision matters and where it doesn’t. That doesn’t mean lowering the bar. It means delivering meaningful outcomes based on real constraints.
Collaborate across functions
R&D is rarely isolated. We work closely with teams across the company—engineering, product, customer success—to connect our experiments to real problems.
Sometimes we initiate new product ideas. Other times, we help refine or extend features that already exist. In both cases, collaboration is critical. It ensures we’re not solving problems in a vacuum and helps us integrate what we build into the broader system.
At a startup, where responsibilities overlap, this cross-functional alignment becomes even more important.
Tie research to real impact
Good R&D demonstrates what’s possible, but great R&D leads to measurable improvements.
For example, I’ve spent the past two years developing a new calibration method. The goal was not theoretical accuracy. It was to improve every scanner in the field with a software-only update. This opened new use cases, improved customer outcomes, and increased the value of our existing products.
It’s easy to get lost in technical complexity. Staying focused on real product outcomes keeps the work grounded.
Tools help, but mindset matters more
We use a range of tools. Cursor has helped me write software more efficiently. Mechanical prototyping tools let us test physical ideas quickly. These tools matter. But they’re not the deciding factor.
What really drives R&D is the right mindset. Are you curious? Are you willing to test assumptions? Do you stay focused on solving the right problem?
A thoughtful, iterative mindset will take you further than any single piece of software or hardware.
Final thought
R&D in a startup requires balancing speed, rigor, and practicality. You’re working with uncertainty. You’re building things that may not work. But you’re also learning quickly and making decisions that shape the final product.
I have found that the most effective approach is to start from first principles, define clear questions, and work in tight feedback loops. You don’t need to be certain about everything. You just need to make consistent progress toward something real.
That mindset (not just technical ability) is what moves a product from concept to reality.