Jin Chen serves as the Chief Technology Officer (CTO) for NeARabl Inc., a New York–based computer vision AI company transforming how construction and engineering professionals capture building data through the use of 3D imaging and augmented reality.

Chen began her career as a standout data science and engineering student at The City College of New York, where she joined a campus research lab focused on indoor navigational mapping. Funded by the National Science Foundation, the Air Force Research Lab, and Homeland Security, the project pushed the boundaries of spatial AI. Her contributions helped the project advance its technology into a more agile 3D mapping and AR solution with real commercial potential.

We achieved a great milestone in our spatial accuracy: improving building scans to just under 30mm reconstruction accuracy. This is a level of precision that rivals laser scanners and far exceeds traditional mobile-based capture tools.

After graduation, Chen became part of NeARabl’s core leadership team, rising from a college student to CTO practically overnight. Today, she singlehandedly leads the company’s research and development efforts, building an intuitive Infrastructure AI tool that helps users visualize design ideas and infrastructure data with precision, accuracy and ease.

NeARabl’s technology was developed in a research lab at CUNY. Can you share how the technology came to be, and how has it has evolved since those lab days?

NeARabl is the result of more than five years of focused research and development, backed by the National Science Foundation, Air Force Research Labs, Homeland Security, and Bentley Systems Inc. The original technology began as a mobile wayfinding and navigation tool, initially designed as an assistive solution for people with low vision. Our technology allowed users to seamlessly explore indoor maps and points of interest directly from their phones.

During testing, however, its broader potential became clear—spanning industries such as construction, architecture, and facility management where spatial imaging and measurement was truly critical. With some additional funding, our team spent the past year advancing the platform with enhanced 3D visualization and significantly improved spatial accuracy, transforming it into a scalable, field-ready commercial product. What once was an academic lab project has now become a modern jobsite tool.

Can you tell us what interested you about this technology and why you chose to be a part of the research and development?

My interest in mathematics was sparked at an early age in elementary school, where I developed a lasting curiosity about how things work and a habit of asking “why” behind every concept. I found joy in connecting problems with solutions, which naturally led me to study mathematics and computer science at The City College of New York. There, I discovered an equal passion for programming and gained hands-on experience through a software development internship at WebMD.

Following that internship, I took a leap of faith and joined a grant-funded research project with NeARabl—a decision that allowed me to merge my love of problem-solving with real-world applications. What drew me in was how the technology not only delivered practical solutions but also empowered users to work smarter and more effectively in the field.

We all see AI tools growing in popularity, why is this kind of technology important for the construction and engineering industries?

When it comes down to it, artificial intelligence is no longer a futuristic concept—it’s here and actively transforming how construction projects are planned, executed, and maintained. Tools like NeARabl’s Infrastructure AI are a clear example—turning mobile devices into highly precise mapping tools that can capture spaces within centimeters of accuracy. That kind of innovation reduces costly rework, improves collaboration between architects, engineers, and trades, and accelerates timelines without sacrificing quality. AI’s influence isn’t just in efficiency gains; it’s fundamentally reshaping the project lifecycle, from design to delivery.

Can you share any specific pain point you’re solving with the capabilities of the tool and what the customer market looks like?

The solution is truly built to adapt — serving a wide range of industries where indoor intelligence, visualization, and navigation are critical. There’s no expensive hardware needed, making the deployment fast and scalable as well. Right now, we’re seeing the technology is particularly of interest to architects, construction teams, mechanical engineers, electricians and plumbing professionals who want to use quick yet accurate building scans to enhance their strategic planning efforts, expedite infrastructure development, and minimize the risk of costly mistakes.

We can help reduce the risks of human error by seamlessly integrating AI, augmented reality and 3D capabilities for an accurate visualization what should be built before ever breaking ground. That’s an incredible improvement in the planning stage of infrastructure and construction projects.

We hear you had a recent R&D breakthrough; can you tell us about it?

Our R&D team is well-versed in advanced robotics, computer vision and mixed reality so we’re continually improving the solution to better met market needs. Just recently, we achieved a great milestone in our spatial accuracy: improving building scans to just under 30mm reconstruction accuracy. This is a level of precision that rivals laser scanners and far exceeds traditional mobile-based capture tools.

When you look at current mobile hardware, like an iPhone, there’s a fundamental limitation in the depth sensors, IMUs, and cameras and they were never designed for engineering-grade measurement. Thankfully, we’ve developed proprietary methods that allow us to push beyond those constraints in ways that most didn’t think possible.

You’ve navigated leadership quite well at a young age, and what advice do you have for others looking to break into STEM and research fields?

One mantra I try to live by is: “Every problem holds a solution waiting to be found, and failure is the path that leads us there.” This has guided me on my path through the STEM world, and all the discomfort that may come with it. Setbacks will come, but they carry the most valuable lessons, and progress comes from trying, adjusting, and trying again. Look at a challenge as a step forward, and not as a step back. I’m excited to be part of the research and development team at NeARabl and look optimistically at what the future holds for AI innovations.

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