If you're comparing occupancy platforms, the first question is what you're trying to decide. Reporting on whether buildings are being used is a different problem than forecasting how a floor will perform after a consolidation, modeling a lease exit, or pressure-testing an RTO policy before it ships.
That distinction shapes how different platforms capture data.
WiFi-based platforms estimate occupancy at the zone and floor level by tracking devices on existing infrastructure. Sensor-based platforms measure at the desk and room level and pick up passive occupancy that device-based signals miss. The granularity gap is what determines which decisions the data can confidently support.
Both solutions surface occupancy data, but they differ on how it's captured and what decisions that data can support.
This comparison covers what each platform delivers, where sensors and WiFi differ on accuracy, and which use cases each one fits.
Need space-level occupancy data, not just zone-level estimates?
VergeSense delivers desk and room-level intelligence, AI-powered forecasting, and scenario modeling backed by 200M+ sq ft of behavioral data across 200+ enterprises.
Both platforms help you understand space usage, but they optimize for different planning horizons and decision-making workflows. The right choice depends on whether your immediate priority is space-level intelligence for future planning decisions, or fast, hardware-free occupancy reporting that runs off your existing WiFi.
VergeSense's Occupancy Intelligence Platform leverages occupancy sensors, including the Infinity Area Sensor for privacy-safe detection, that capture real-time utilization across desks, neighborhoods, meeting rooms, and collaboration zones.
The platform unifies sensor data with booking systems, badge access, Wi-Fi telemetry, and building management inputs.
This foundation powers two capabilities that WiFi-based platforms can't replicate:
Basking.io is a WiFi-based occupancy analytics platform.
It connects to your existing WiFi infrastructure (Cisco DNA Spaces, Meraki, Aruba, Extreme Networks) via API and uses machine learning to estimate occupancy at the zone, floor, and building level.o hardware, no install, and typically live within a day.
The core product is occupancy reporting: Trend dashboards, heatmaps, peak/average utilization, and RTO tracking. Basking has expanded around that core with:
Basking is certified in the Cisco DNA Spaces ecosystem, which gives it distribution into enterprise accounts already on Cisco infrastructure.
The most fundamental difference between VergeSense and Basking.io is how each platform collects and processes workplace data. This distinction shapes everything from measurement accuracy to the types of decisions each platform can confidently support.
VergeSense deploys purpose-built wired or wireless sensors for occupancy detection. The Infinity Area Sensor uses computer vision to detect presence and count people in real time across desks, neighborhoods, and collaboration spaces, including passive occupancy detection.
The result is room-level and desk-level accuracy that proxy signals can't match. VergeSense sensors distinguish between a conference room at 40% capacity versus fully booked, and identify which neighborhoods hit peak demand at 10 a.m. versus 2 p.m.
Underneath, the Large Spatial Model contextualizes those patterns against cross-portfolio behavioral data — turning your occupancy signals into benchmarked intelligence rather than isolated readings.
Basking.io ingests information primarily from WiFi infrastructure, with badge systems and booking platforms as supplementary inputs, into a unified analytics dashboard. This model offers faster initial deployment since it doesn't require physical sensor installation.
Wi-Fi-based occupancy detection carries inherent limitations:
There's also an IT and InfoSec angle worth surfacing. Some enterprises, particularly in financial services and EU markets, have flagged WiFi-based device tracking as a non-starter for privacy review. The "no cameras" framing doesn't always translate into easier approval.
Basking.io's WiFi-based approach works well for directional, portfolio-level questions: which buildings are busy, which days see peak attendance, which leases look underutilized at a high level. It's less reliable when the question gets specific: which floors break at capacity, which space types are oversupplied, or whether a consolidation will actually work.
The feature gap between these platforms becomes clearest when you compare how each one helps you make space decisions. VergeSense centers on occupancy intelligence and predictive planning. Basking.io is a WiFi-based analytics platform that has expanded into adjacent areas like lease administration through LeaseOps.
VergeSense's tools help you answer forward-looking questions:
That's the difference between asking "is this floor busy?" and "will this floor still work after we consolidate?"
Basking.io's WiFi aggregation surfaces portfolio-level trends and offers Basking AI as a conversational interface to query those trends. But Basking AI sits on top of WiFi data, so it inherits the same zone-level limits. There's no equivalent to the Large Spatial Model, no Monte Carlo scenario simulation, and no behavioral breakpoint analysis.
Basking.io's LeaseOps module adds AI-driven lease abstraction, document search, and renewal recommendations on top of its core occupancy reporting. For teams that need help managing lease documents and critical dates, that's a useful admin layer.
It's worth separating two different questions, though. Lease administration is about managing the document. Lease decision intelligence is about answering "should we exit, consolidate, or renew?" That second question doesn't get answered from a lease abstract. It gets answered by understanding how a space will actually be used under different scenarios.
Rather, VergeSense provides the decision support layer that informs those decisions:
VergeSense unifies data from sensors, booking systems, badge access, Wi-Fi, and building systems into a single occupancy intelligence layer. It can also expose that unified view through APIs and pre-built connectors to tools like Archibus and ServiceNow.
The Large Spatial Model API enables advanced use cases when integrated with other workplace technology tools:
For teams building occupancy intelligence into daily operations, VergeSense's API architecture delivers the real-time data access that portfolio management platforms weren't designed to support.
Implementation complexity and enterprise readiness often determine which platform delivers value quickly and which will scale with your portfolio over time.
VergeSense is built for rapid deployment across distributed portfolios. Infinity sensors install in seconds each, and a full floor can be live in hours. Predictive Planning can also start adding value without occupancy measurement, using just a floor plan and behavioral benchmarks from the Large Spatial Model. Predictive Planning delivers actionable insights within hours of activation.
Basking.io's API connection to existing WiFi is genuinely fast for initial rollout. For organizations that need directional occupancy data quickly, this is a real advantage. Beyond the core occupancy reporting, layered modules like LeaseOps require additional configuration, which extends time-to-value if your team plans to use them.
Scalability at enterprise scale:
Your choice between VergeSense and Basking.io depends on where occupancy intelligence fits within your broader real estate strategy and how deeply you need to integrate space utilization data into planning decisions.
Choose VergeSense if you need:
Choose Basking.io if you need:
As you can see, Basking is a good fit for organizations that need a quick, low-friction way to determine whether buildings are in use.
VergeSense is built for the higher-stakes decisions WiFi data can't answer, including identifying your true capacity breakpoints, passive occupancy detection, scenario modeling for lease exits and consolidations, and space-type-level intelligence at desk and room granularity.
Is your WiFi data enough to recommend a million-dollar call?
VergeSense's occupancy intelligence and AI-powered scenario modeling tell you where your spaces are busy, why, and what to do about it.
VergeSense uses dedicated sensor infrastructure for passive occupancy detection and unifies data from badge systems, Wi-Fi, and booking platforms in a single solution. The result is real-time desk and room-level accuracy across your portfolio. Predictive Planning can also start without sensors, using just a floor plan and behavioral benchmarks from the Large Spatial Model.
Predictive Planning forecasts future demand, models space scenarios, and identifies breakpoints before they impact operations. You get recommendations on right-sizing neighborhoods and optimizing space mix. It turns historical occupancy data into forward-looking decisions that reduce costs and improve employee experience.
It depends on what decisions the data needs to support. WiFi-based platforms like Basking.io give you fast, low-cost directional data at the zone and floor level. That's useful for portfolio-level questions like "are these buildings being used?" Sensor-based platforms like VergeSense give you space-level intelligence (desk, room, neighborhood) that supports higher-stakes decisions like lease exits, floor consolidations, and behavioral breakpoint analysis. The question isn't sensors vs WiFi. It's whether your data needs to be directional or decision-grade.
Yes. VergeSense supports 200+ global organizations managing over 200 million square feet, with enterprise-grade scalability, security, and API access. VergeSense delivers the occupancy intelligence and predictive planning depth that enterprise portfolios need to optimize space performance across distributed locations.