Skip to content
MarketScale
‹ Back to IndustriesEngineering & Construction

How to Take a Prototype to Production with Mike Wilkinson

Mike Wilkinson, the founder and CEO of Paragon Innovations, shares valuable insights into the process of transitioning from prototype to production. Customer feedback is crucial in refining the product, and alpha and beta testers play a vital role in ensuring it meets their specific needs and use cases. Designing for manufacturability (DFM) is essential to…

This story was produced through MarketScale. See how Engineering & Construction teams put it to work with Partner & Channel Enablement.

Share

Mike Wilkinson, the founder and CEO of Paragon Innovations, shares valuable insights into the process of transitioning from prototype to production. Customer feedback is crucial in refining the product, and alpha and beta testers play a vital role in ensuring it meets their specific needs and use cases. Designing for manufacturability (DFM) is essential to streamline production and create a product that can be efficiently manufactured at scale. Regulatory approvals, such as UL and FCC certifications, are necessary for market acceptance, particularly for IoT devices. Wilkinson’s expertise in understanding consumer behavior and navigating the complexities of product development positions him as a valuable resource for aspiring entrepreneurs seeking to bring their prototypes to successful market-ready products.

Video TranscriptExpand ↓

So Mike, what's the process for determining when a prototype is actually ready for production. Very good question and often misunderstood. So prototypes are just that. They are a first run look what your product is gonna be, but they can't be really your production product until several things have occurred. Number one, we've got to get it out in the market and have some alphan beta cuss who can review the product, use the product, and get some field time to make sure it works exactly the way the customer wants it to work, not just the way it was designed. So design might be correct, but it turns out that customers have a different use case for how they wanna than was original. Number two, it's got to be made to be manufacturable, so we call it DFM designed for manufactibility. So it should have been in the initial design we need to make sure you met all that. And third, we gotta get regulatory approval. So if this is a IoT device, for example, we're gonna need UL approval, FCC approval, cellular is gonna require PTCRB. We gotta get approval by the carrier. It's a lot of approvals to make a wireless product be safe and get into the marketplace and be accepted by the carriers. So many aspects have to be included. How long can that process take or does it vary depending on the product? It varies up among the product and varies about how many things are being pushed through. So you can imagine if there's a whole bunch of new IoT devices being pushed through AT and T or Verizon for example. They can't test them all at once, and so they you may get put in line. It could be six months waiting. Right now, it's not too bad. Paragon's lucky enough to have be able to do self certification now on AT and T and Verizon in our office. And so that's a big plus, so we can do it real time in our office for cat m one radios. That's fantastic. I think one of the things that you mentioned that really stood out to me was just that idea that sometimes consumers use products differently than maybe they were intended to be used but are just looking for something that satisfies and and meets that need perhaps. And so I think that's a pretty crucial element to this. It is. I mean, think about Android phone and an iPhone, both do the same thing. Fundamentally, both do email, text, video, etcetera. But one has a different user interface, a different feel, Some users are more attracted to the iPhone, some are more attracted to the Android. It's not a right or wrong answer, but it's if you're trying to make a product you need to identify your market, and then have a product that meets that with the right use case.

Engineering & Construction: are you visible to AI?

Before they reach out, Engineering & Construction buyers ask AI engines which vendors to trust. See how AI describes your company today, and where competitors show up instead.

Free workspace

You just read one expert. Imagine publishing your whole team.

This article was produced through MarketScale. Create a free workspace and turn your own team's expertise into articles, video, and social posts. No credit card, no demo required.

NPS +73 · 1,000+ creators · 38+ countries

What you get, free

Your own MarketScale Studio workspace
One video edit a month, on us
AI writing, editing, and publishing tools
In-platform coaching to learn the system

More Engineering & Construction Insights

AI moves from back office to job site in construction's next build-out

AI moves from back office to job site in construction's next build-out

McCarthy Building Companies has entered a multimillion-dollar agreement with Palantir to enhance AI adoption. However, RICS experts highlight that data readiness and organizational culture pose significant challenges. This development signals a shift in integrating AI within construction sectors.

  • 01McCarthy Building Cos. signs a major deal with Palantir.
  • 02Data readiness is a critical hurdle for AI integration.
  • 03Organizational culture impacts AI adoption in construction.

Jul 11, 2026

South Korea commits $7.5 billion to AI-autonomous manufacturing as smart factory count hits 30,000

South Korea commits $7.5 billion to AI-autonomous manufacturing as smart factory count hits 30,000

South Korea is investing $7.5 billion in advancing AI-autonomous manufacturing, with a significant increase in smart factories, now totaling 30,000. The initiative also targets the development of 100 AI manufacturing zones throughout the country.

  • 01South Korea invests $7.5 billion in AI-autonomous manufacturing.
  • 02There are currently 30,000 smart factories in South Korea.
  • 03The government aims to develop 100 AI manufacturing zones.

Jul 11, 2026

Construction's productivity crisis: why ML cost forecasting and off-site methods are converging

Construction's productivity crisis: why ML cost forecasting and off-site methods are converging

U.S. construction productivity has decreased since 1968. Machine learning models and off-site construction methods are becoming pivotal in bridging this productivity gap by providing accurate cost forecasting and efficient building practices.

  • 01U.S. construction productivity has been declining since 1968.
  • 02Machine learning models offer enhanced cost forecasting capabilities.
  • 03Off-site construction methods contribute to improved project efficiency.

Jul 10, 2026

Explore More Engineering & Construction Insights

Read more expert perspectives from across Engineering & Construction.

Browse Engineering & Construction Hub

For B2B teams

Your experts could be publishing here

Stories like this one run on content MarketScale captures from real practitioners. See how your team's expertise becomes coverage in Engineering & Construction and beyond.

Book a 15-minute demo

Or call us. No forms required. We pick up. 214-945-2512