Inside InnerSpace: How Our Platform Actually Measures Human Behavior

Most companies measure performance. Some measure engagement. Almost no one measures what actually drives both: human behavior.

Workplace and CRE leaders rely on badge wipes, booking systems, occupancy sensors, headcount reports and employee surveys to measure space use, but these tools rarely show how people actually move through the workplace, collaborate in real time or adapt under pressure. They capture isolated data points, not the full picture of behavior behind them.

At InnerSpace, we measure real, observable patterns of workplace behavior - how people use space, move and collaborate in context - turning behavioral data into actionable insight that transforms how organizations understand performance.

Most workplace leaders believe they already have the data they need. They can see occupancy. They can see badge swipes. They know how many people came into the building and how full it was on Tuesday.

But attendance is not behavior.

If 200 people enter the office, what does that actually tell you? It tells you they showed up. It does not tell you how long they stayed. It does not tell you whether they worked alone or collaborated. It does not tell you whether teams gathered together or sat scattered across floors. It does not tell you whether meeting rooms were used effectively or misused as overflow desks.

Occupancy data answers the question “Is someone there?” It does not answer “What are they doing, how are they using space, and what does that mean for design?”

That distinction is critical.

At InnerSpace, when we talk about measuring human behavior, we mean understanding how people actually use the workplace once they are inside it. We measure patterns such as how often people come in, how long they stay, which zones they gravitate toward, how they move across the floor, and how different teams cluster or disperse over time.

For example, a meeting room that shows as occupied could mean very different things. It could be one person on a video call. It could be two colleagues having a quick check-in. It could be a cross-functional team solving a complex problem. Occupancy sensors treat those scenarios as identical. Behaviorally, they are completely different.

When you can see patterns across time, you begin to understand whether large meeting rooms are consistently used by small groups, whether certain teams repeatedly book rooms on opposite sides of the floor, or whether collaboration spaces are actually supporting collaboration. That insight allows you to resize rooms, redistribute them, or colocate teams more effectively instead of simply adding more space.

Behavior also shows up in movement. If two teams frequently move between the same zones and consistently interact, that is a signal that adjacency matters. If a team travels across floors multiple times a day to meet another group, that friction is measurable - something attendance data would never surface. Movement patterns do.

Hybrid work adds another layer

Many organizations see peak days and assume they have a space shortage. But behavioral data might show that congestion is concentrated in specific zones, while other areas remain underused. It might reveal that employees come in primarily for meetings and leave early, changing the type of space that should be prioritized. Without understanding dwell time, visit frequency, and zone preference, leaders are making hybrid decisions in the dark.

Traditional tools cannot capture this level of clarity. Surveys measure perception. Time studies capture short snapshots. Badge systems show entry. Basic sensors show presence. None of these tools really connect patterns across time, teams, and space types to show how the workplace is actually functioning.

InnerSpace was built to provide that missing layer.

Inside the platform, organizations can see how often employees come onsite, how long they stay, where they spend time, how space usage compares to capacity, and how different teams behave within the environment. We identify behavioral cohorts such as full day users, quick visit users, and highly mobile employees. We surface real utilization trends rather than isolated data points.

Importantly, this is not surveillance. Our proprietary pHLF technology enables accurate measurement without wearables or intrusive tracking systems. The data is anonymous and aggregated, focused on patterns rather than individuals.

The value of this approach is practical. Leaders can redesign floorplans based on real movement flows. They can adjust meeting room ratios based on actual usage patterns rather than complaints. They can colocate teams that naturally collaborate and reduce friction where unnecessary movement occurs. They can evaluate whether hybrid policies are supporting or disrupting collaboration.

Most importantly, they move beyond the belief that occupancy equals insight.

Seeing that a space is full does not explain why. Understanding behavior does.

Why InnerSpace?

• Not surveillance
• Accurate technology - pHLF
• No sensors huge from a costs and maintenance perspective, private and anonymous, deeper insights beyond simple occupancy
• No sensors means lowers costs and no maintenance burden - while delivering privacy-first, anonymous data and deeper insights beyond just simple occupancy.

Understanding performance without understanding behavior is like looking at the scoreboard without watching the game. You see the outcome, but not the patterns that created it.

Behavioral intelligence changes that.

When organizations can see how decisions are made, how collaboration actually unfolds, and how teams respond under pressure, they gain something far more powerful than surface metrics. They gain clarity. With clear insight into how people work, organizations can design spaces that truly support their teams - enabling stronger collaboration, better leadership, and a culture that performs at its best.

At InnerSpace, we believe behavior is the missing layer of organizational insight. As the future of work becomes more complex, dynamic, and team-driven, understanding how humans actually operate will no longer be optional. It will be foundational.

That’s the layer we’re building.