Before Javier Cuello was building sensors smaller than a fingernail or tracing millions of clinical samples, the founder of H+Trace was capturing live audio for global icons like Madonna and AC/DC. With a background in electronic engineering, sound design, and public innovation, his path to transforming healthcare logistics has been anything but straightforward.

Today, he is the driving force behind a platform that is addressing one of the most overlooked failure points in diagnostics by combining real-time tracking, automation, and artificial intelligence.

In this Q&A, he shares how lessons from live events and drone technology shaped his approach, why owning the full hardware and software stack was essential, and what it takes to bring clinical-grade precision to one of healthcare’s most chaotic frontlines.

You started your career capturing live audio from Madonna and AC/DC. What did the live music industry teach you about precision and timing that now informs your work in healthcare diagnostics?

I learned that the only way to maintain high quality in complex, unpredictable environments is through fully streamlined, automated processes powered by technology. While human input remains essential, true reliability, especially in large-scale operations prone to surprises, whether it’s a live event with tens of thousands of attendees or a logistics system handling thousands of blood samples daily, comes from proven, tech-driven systems.

Given your background in electronic engineering and sound engineering, how have those cross-disciplinary skills influenced your approach to hardware/software integration in logistics tracking?

Beyond my academic background, I’ve always been passionate about data. What excites me most about engineering, whether in electronics or acoustics, is that it’s a discipline grounded in reliable data.

Years of working in sound engineering helped me understand human emotional behavior, but that alone doesn’t improve a process. It’s about designing a solution specifically to capture precise, accurate data that can enhance operations, completely independent of emotional subjectivity. We have to recognize that people are part of the process, we have emotions, good days and bad days, and that variability affects the quality of our work. That’s exactly when a process built to withstand human variation becomes essential.

You have a background in software, drones, and public sector innovation. How have those experiences shaped your approach to solving entrenched problems in healthcare logistics?

I like to think of drones as, essentially, data-capturing machines, they’re basically a package of sensors with flight capabilities. In the end, they’re collecting visual, thermographic, and environmental data that only becomes valuable after processing. That’s exactly what H+Trace does in the logistics process, it’s like having a mini drone inside every box, tracking critical health-related items in real time.

My journey through public innovation has always been driven by the search for impact. Back in 2014, I set out to make a difference by launching free programming education initiatives for underserved communities, groups that often had no idea how deeply software shapes our lives. If you want to build solutions that truly make an impact, software isn’t optional, it’s essential.

H+Trace is tackling what you call “the most overlooked failure point in healthcare.” What made you zero in on the preanalytical phase of lab diagnostics, and why do you think others have missed it for so long?

A friend of mine was misdiagnosed with diabetes during her pregnancy, at a top-tier hospital. My first reaction was anger, but then I focused on understanding how such a critical error could happen in something as fundamental as a blood glucose test. To my surprise, I discovered that the sample had experienced a temperature excursion during transport, and the analyzing lab hadn’t caught it because their transport traceability systems were outdated. That’s when I realized that what seemed like a major problem was actually a huge opportunity.

Two-thirds of lab errors happen before samples are even analyzed. How does your Contactless MultiSense platform bring automation and reliability to such a chaotic part of the system?

It comes down to two key aspects: on one hand, the technology needs to be cutting-edge, providing reliable, AI-ready data. On the other hand, the solution must be extremely user-friendly and easy to implement. This second part is often underestimated by tech companies, but for us, it’s critical. You can build the best technology in the world, but if it’s a hassle to implement or requires extra work and training for the team, it simply won’t scale. Our solution fully automates the process of generating, collecting, and sending data, so users are actually happy to use it, it takes routine tasks off their plate.

You’re developing both the hardware and software in-house. Why was it important to own every part of the stack, and what advantages has that created?

The truth is, we didn’t really have another option, what was available on the market simply didn’t work for this kind of use. So we had no choice but to build it ourselves from the ground up. Two added benefits of taking this path are, first, that we can easily adapt to customer specific needs, and second, that we have a very efficient cost structure since we don’t rely on third parties for any part of the product.

Predictive analytics and AI are central to your product. How do your algorithms detect and prevent cargo damage in real time? What kind of data inputs are you analyzing?

The vast majority of errors follow very clear data patterns, much clearer than we often realize. It’s important to remember that the first step in artificial intelligence is training an algorithm with high-quality data, and that’s where our strength lies: we start the process by generating the highest-quality data in the market. This makes prediction much easier, because the measurements are standardized, allowing for fast and reliable identification of patterns that fall outside the norm.

Some of the key data points we track include inertial temperature, unauthorized package openings, impacts, tipping, and overall mishandling. We’re able to capture all of this thanks to a set of sensors embedded in a piece of electronics no bigger than a fingernail, completely wireless and fully automatable.

You’ve traced over 11 million samples through your platform so far. What clinical or operational insights have emerged from that data, and how have those insights shaped the evolution of your product?

Our clients have uncovered all kinds of insights, often related to tasks being done out of habit or tradition, blindly following protocols where continuous measurement simply wasn’t possible. The reality is that people and environmental conditions are constantly changing and, if you’re not measuring, you won’t catch issues until it’s too late. Some common examples include improper thermal conditioning (too cold or not cold enough), downtime during logistics, hemolysis caused by excessive shaking, and samples being compromised during transport, among others.

H+Trace’s Agentic AI is a bold step into autonomous lab intelligence. Can you walk us through what that actually looks like in practice?

The LIS (Laboratory Information System), the software used to manage labs, is generally a very basic and outdated tool. What we’re doing is adding the power of autonomous decision-making, which brings greater efficiency and trust to complex lab operations. This includes things like proactive error detection, risk- or urgency-based prioritization of analyses, automated recommendations for follow-up tests, prediction of operational saturation and turnaround times, automated conversion between different standards and formats, and detection of suspicious data patterns.

You’re now based in Miami, prepping for U.S. expansion. How are you approaching market entry differently this time compared to your launches in Latin America?

In the U.S., our primary focus will be on at-home blood draws. We’ve developed a product specifically for this use case, the first smart packaging that passively self-cools or freezes. This enables the highest level of sample monitoring while simultaneously starting the cooling or freezing process (as needed) from the moment the sample is collected at home. The result is not only higher quality and greater reliability, but also a reduction of at least 24 hours in turnaround time.

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