Ten years ago, I walked out of my previous job for the last time and into a tiny shared office space with a handful of desks and a big idea that I couldn’t stop thinking about. At the time, I didn’t know exactly what InnerSpace would become. I just knew there was a problem worth solving, and that the way people interacted with indoor spaces was archaic and out of step with the times.
My background is in computer science. Before InnerSpace, my work focused on the intersection between humans and the tools they use. During my graduate research at Queen’s and McGill, I studied how people physically and cognitively interact with technology, how tools shape behavior, and how environments influence the way people work.
What’s interesting, looking back, is that some of the earliest seeds of InnerSpace were already there long before the company existed. During my PhD research, I was studying software development teams and how they collaborated using digital tools. Another researcher I worked alongside came from an architecture background and was studying how those same teams used physical office space. We were observing the same people through two very different lenses. That was probably my earliest realization that workplace effectiveness isn’t just about software or technology, it’s also about the built environment people work within every day.
Still, InnerSpace didn’t truly begin until a trip to New York around 2014.
I was visiting the Metropolitan Museum of Art and using my phone constantly while navigating the city - finding restaurants, booking transportation, looking up attractions. Outdoors, digital experiences felt seamless. Then I walked into the Met and suddenly everything stopped working the way I expected. I was handed a paper map and a custom audio headset that only worked inside the museum. At one point, I was trying to move between two sections of an exhibit and kept having to stop museum staff to ask for directions.
What struck me wasn’t just the inconvenience. It was the realization that indoor digital experiences lagged dramatically behind outdoor ones. Outdoor navigation had already been solved at massive scale, yet indoors we were still relying on paper maps and bespoke systems. I remember thinking how strange it was that such a large and obvious technical problem still existed.
That experience stayed with me.
I started researching why indoor location technology remained unsolved. The answer became clear quickly. Existing technologies forced organizations to choose between accuracy and scalability. Bluetooth beacon systems (the leading solution at the time) could provide accuracy, but deploying hardware at scale across millions of square feet was operationally difficult and incredibly expensive. Wi-Fi-based approaches had broader reach but lacked precision.
Very early on, I realized that if this problem was ever going to be solved properly, the solution needed both. It needed to be accurate enough to matter and scalable enough to work across enormous real estate portfolios. Once I started looking at the sheer scale of commercial real estate globally, it became obvious that anything requiring heavy hardware deployment would eventually hit a wall.
Ironically, despite recognizing that challenge, our earliest years at InnerSpace were spent building sensors.
At the time, we believed we could solve the scale problem by building Wi-Fi-based sensors that required fewer deployments than traditional hardware systems. And we believed we could solve the fundamental accuracy problem that had plagued WiFi-based systems to date. We spent years refining custom hardware and developing the software needed to make WiFi-based indoor location intelligence actually work accurately at scale.
Those early years were difficult, but they were incredibly important. Building our own hardware forced us to deeply understand Wi-Fi positioning, location accuracy, and the technical limitations that existed within indoor environments. It also led us to develop one of the most important innovations in our company’s history, the pHLF algorithm.
pHLF was developed to address the core limitations with existing Wi-Fi location services. We knew that solving indoor positioning properly required a fundamentally different approach to interpreting and stabilizing location data. The patent for pHLF was submitted around 2019, well before we became fully sensor-free. At the time, the algorithm was still embedded inside our own hardware.
What changed everything was realizing that the intelligence itself was the real breakthrough, not the hardware surrounding it.
Around 2020, we began seriously exploring what we called “location as a service.” The idea was simple but transformative. What if we could extract the intelligence layer we had built into our sensors and apply it directly to existing enterprise Wi-Fi infrastructure?
At almost the exact same time, the pandemic hit.
I still remember the board meeting where we openly discussed whether the company could survive what was coming. Offices were shutting down globally. Workplace strategy was suddenly uncertain. We came out of that meeting and gathered around one of the lunch tables in our office talking about whether this new approach could actually work.
Then we landed a customer deployment that validated the idea.
For the first time, we successfully delivered our solution directly through existing infrastructure without requiring sensors. That moment fundamentally changed the trajectory of InnerSpace. Sensors became optional rather than required.
The shift was later reinforced through our work with Microsoft.
Initially, Microsoft (our client) planned to deploy our sensors across their East Campus (one of their primary campuses) redevelopment project. But as the scope of deployment grew, even an organization with Microsoft’s (our client’s) resources recognized the operational complexity of scaling physical hardware across massive global portfolios. Eventually, they asked us directly whether the sensor-free approach could work instead.
That became a major confirmation point for us. If deploying sensors at enterprise scale felt burdensome to one of the most sophisticated technology companies in the world, then hardware was never going to be the long-term answer for this market.
Today, InnerSpace is very different from the company we started ten years ago, but in many ways the core mission remains the same. What has evolved most is our understanding of what organizations actually need from workplace data.
Early in the industry, space utilization was mostly about counting people. How many employees came into the office? How many desks were occupied? How many people used a meeting room? Those metrics matter, but over time we realized they only answer part of the question.
The real value comes from understanding behavior.
How often do people come in? How long do they stay? Which teams collaborate together? Which spaces create friction? Where do employees naturally gravitate? How does movement change over time?
Workplace intelligence needs to be multidimensional because the workplace itself is multidimensional. That philosophy continues to shape how we build the company today.
I also think it’s important to recognize what we are and what we are not. We are not commercial real estate experts. Our customers are the experts in the realities of workplace operations, portfolio management, facilities, and employee experience. Our role is to provide them with better data, better visibility, and better tools to help them make informed decisions.
That mindset continues to guide us today.
One of the biggest lessons from building InnerSpace has been recognizing that solving long-standing problems often requires the willingness to challenge your own assumptions. Innovation is rarely about following a straight path. In many cases, it means realizing that the original approach, even one you’ve invested years into, may not ultimately be the right solution at scale. InnerSpace has continuously evolved because we stayed willing to challenge our own assumptions. We adapted from wayfinding to workplace intelligence. From hardware to sensor-free infrastructure. From occupancy metrics to behavioral analytics. And every one of those shifts came from listening closely to customers and understanding where existing solutions were falling short.
For us, that meant evolving from custom hardware and sensors to a completely sensor-free model built on existing Wi-Fi infrastructure. That shift wasn’t just a technical change, it was a recognition that true innovation comes from staying focused on the customer problem itself, not becoming attached to a specific technology or implementation. The workplace continues to evolve rapidly, and the organizations that succeed will be the ones willing to continuously adapt, rethink old models, and embrace new approaches as needs change.
As workplaces continue evolving, the need for meaningful workplace intelligence will only grow. Organizations are no longer managing static offices. They are managing dynamic environments shaped by hybrid work, changing employee expectations, operational efficiency pressures, and evolving collaboration patterns.
The companies that succeed will be the ones that understand not just how many people use their spaces, but how those spaces actually support the people inside them. That’s the problem we set out to solve ten years ago, and it’s still the problem we’re solving today for our clients and the wider CRE community.