Every occupancy platform can tell you a floor is half empty. Far fewer can tell you what to do about it.

That gap is the whole story when you compare Basking and VergeSense. Both measure how offices are used. The difference is what happens after the data appears on the dashboard.

You don’t pick between these on a feature checklist. You pick based on the decision you’re trying to make.

Two platforms, two starting points

Both sit in the same market. They solve the problem from opposite ends.

Basking: occupancy joined to the lease

Basking is a workplace occupancy and lease management platform for teams who need to know how space is used and what to do next.

The platform runs on four products that share one data layer: LeaseOps for lease administration and portfolio reporting, Occupancy for utilization and presence, DocsAI for AI lease abstraction, and Flow for tasks and approvals. Two sources of truth — what you lease and how it’s used — sitting in the same place.

Here’s why that matters. A utilization dashboard tells you a floor runs at 38%. It can’t tell you the lease expires in six months, that a renewal option is on the table, or that finance needs a commitment report before anyone signs. Basking puts those next to each other. A regional CRE lead can see the underused floor, the rent against it, the next notice date, and route the decision for approval — without stitching three systems together by hand.

There’s also a difference in what the occupancy data itself can tell you. Because Basking reads WiFi, it sees behavior, not just a headcount — duration of visits, how often teams return, and how people move between sites — all anonymized. That’s a richer read on utilization than a per-desk count, and it feeds the same portfolio decisions.

That’s the line we’d want a reader to remember: VergeSense helps you understand a space. Basking helps you make a portfolio decision.

The scale behind it is real. Basking analyzes 190M+ occupancy signals a month, has helped optimize 85K+ workspaces, and has accelerated 550K+ decisions from question to approval-ready. Teams at Allianz, HubSpot, Siemens, Uber, and Microsoft run on it. As Alexander Gebauer, CEO of Allianz Real Estate Western Europe, put it, Basking gave executives the missing piece — a real understanding of how their office properties are actually used.

VergeSense: sensors and scenario planning

VergeSense builds around dedicated sensors and predictive planning. Its Infinity Area Sensor uses computer vision to detect presence at the desk and room level, and its Meridian platform applies a Large Spatial Model — trained on a large cross-portfolio dataset of workplace behavior — to forecast demand and model scenarios like consolidations or RTO shifts.

When the decision is about physical space design, that’s a genuine strength. If you need to know whether a neighborhood will hit capacity after a policy change, or whether converting focus rooms to collaboration space will create a bottleneck, sensor-grade detail earns its keep.

How the data gets collected

The collection model drives cost, speed, privacy review, and how fast you can cover a portfolio.

Basking reads the infrastructure you already own. WiFi, badge, and sensor inputs fold into one signal, with reporting from portfolio down to zone level. For a company with offices across London, New York, Munich, and Singapore, that means a consistent portfolio-wide picture without treating every site as a hardware project. You start with the signals already in the building, then add depth where a specific question demands it.

VergeSense puts dedicated sensors at the center. That delivers desk- and room-level precision and catches passive occupancy — a bag and jacket claiming a desk while the person is in a meeting. The trade is the operating model: hardware to plan, place, install, and maintain. Worth it for a headquarters redesign. Often more than you need for a first-pass portfolio review.

Neither approach is “the accurate one.” They answer different questions at different resolutions.

About the “WiFi just counts devices” argument

VergeSense’s comparison page leans hard on two claims: WiFi counts devices instead of people, and WiFi accuracy drifts between buildings. Worth addressing head-on, because Basking already engineers for both.

On double-counting: Basking’s Device Clustering groups multiple devices belonging to one person, and Non-Human Device Identification filters out printers and other hardware that would otherwise inflate the count. On building-to-building drift: every site goes through a multi-week calibration against badge scans or manual counts, measured with Mean Absolute Error and variance rate, then monitored continuously with on-demand recalibration. You can pull an accuracy report per site whenever you want to check it.

So the honest version isn’t “WiFi is rough and sensors are precise.” It’s that WiFi answers portfolio and floor questions well when it’s calibrated properly, and Basking’s data work is built to keep it that way. Where you need desk-level certainty, Basking takes sensor input too.

On the privacy point specifically: device-based occupancy is often easier to clear than camera-based sensing, not harder. Basking is SOC 2 compliant and meets GDPR, CCPA, and similar privacy regulations. Built and run for a global customer base, it works from anonymized network signals rather than images — which is frequently what gets a workplace analytics tool through privacy and InfoSec review, from financial services to the public sector.

What happens after the data arrives

This is the part that matters most, and the part a feature list hides.

Occupancy data sitting in a dashboard has a ceiling. The value shows up when the data turns into a recommendation, a report finance trusts, an approval that moves.

Basking carries it that far. A site reads underused for three months. In a standalone occupancy tool, that starts a conversation. In Basking, the team connects the signal to the lease record, checks the next option date, sees the cost exposure, routes the decision through Flow, and hands finance a clean commitments report — in one environment. Instant Insights and AI Summary flag what changed so nobody rebuilds a report by hand.

VergeSense carries it somewhere different: into space planning. Predictive Planning models headcount changes and redesigns, and surfaces the breakpoint where a floor stops working before you commit capital. Strong work if the decision is about the physical space itself. The boundary is clear, though — VergeSense models how a space will perform. Basking ties that signal to the lease, the cost, and the approval that follows.

AI that works across the whole job

Both platforms now lead with AI. The difference is the data each one reasons over.

VergeSense’s AI lives in occupancy and planning – the Large Spatial Model simulates behavior, Predictive Planning tests scenarios, Workplace Assistant answers questions about usage trends. All anchored to space data.

Basking’s AI reaches into the part of real estate that eats the most hours: the lease. DocsAI turns any lease PDF into structured data — renewal dates, notice periods, rent schedules, escalations, options, covenants — pulled in a single pass. What makes it trustworthy is the AI-as-a-Judge approach: a panel of models scores confidence clause by clause, and only the low-confidence fields get routed to a human for a one-click confirm, edit, or reject.

The numbers are the argument here. Abstraction runs at 90–98% accuracy depending on document type, and review drops to about 30 minutes per lease from the 3–6 hours it takes by hand — roughly an 87% time reduction, around 5,250 hours saved on a 500-lease portfolio. That’s about two and a half person-years a team gets back. A wrong number in a utilization chart is an annoyance. A wrong notice date is a renewal you didn’t mean to trigger.

Where the comparison really turns: the lease

Plenty of platforms show whether an office is busy. Few show whether the business can act on it.

A floor at 40% means nothing on its own. If the lease ends in six months, you have room to move. If years remain, the smarter play might be subleasing, space-sharing, or cutting service costs — not exit. If the lease holds a favorable option, you might keep the location despite light attendance. Occupancy opens the question. The lease decides the answer.

Basking is built for exactly that join. Through AI-driven lease administration, teams review utilization, rent, upcoming events, and approvals in one decision context. VergeSense can inform the lead-up — its occupancy data and forecasts help you understand whether a space can absorb a change before you consolidate. What it doesn’t position itself to do is the lease administration that turns that insight into a committed decision. Its strength is space intelligence. Basking’s is connecting that intelligence to the contract.

Reporting that more than one team can use

Reporting is where workplace data either spreads across the business or stays trapped with the analytics team.

Real estate decisions cross departments. Workplace cares about usage, finance about commitments and cost, legal about obligations and source language, facilities about operations, leadership about a straight answer. Basking reports across utilization, presence, rent, commitments by month, lease events, and approval history — exportable to CSV or piped into your BI tool. The same data even drives operations: Smart Cleaning adjusts cleaning to actual usage instead of a fixed schedule.

VergeSense reporting centers on workplace performance — availability, shortage trends, utilization patterns, planning scenarios. A good fit when the question is “how is this space performing?” When the question becomes “what do we renew, renegotiate, or route for approval?”, Basking gives the surrounding context.

Deployment and time to value

Deployment isn’t just IT effort. It’s how fast you can start answering real questions.

Basking’s software-first model connects through existing infrastructure, so data starts flowing fast — no hardware to buy, wire, or maintain, and no on-site work. You can compare attendance across 30 offices before deciding which sites deserve deeper measurement. The first step isn’t a sensor rollout; it’s portfolio visibility, then detail where it pays off. Layered modules like LeaseOps add configuration time, which is the honest trade for what they do.

VergeSense needs more upfront planning where sensors are involved — placement, installation, management. Justified for a high-resolution study of a specific environment; heavier for fast portfolio-wide coverage. Sensors go deeper in selected spaces; Basking goes wider, sooner.

Which one fits you?

FAQ

Is Basking just an occupancy reporting tool?

No. Occupancy is one of four products. Basking also includes LeaseOps for lease administration, DocsAI for AI abstraction, and Flow for approvals — built so usage data connects directly to lease and portfolio decisions, not just a dashboard.

Does WiFi-based occupancy mean Basking only counts devices?

No. Device Clustering groups multiple devices per person and Non-Human Device Identification filters out hardware like printers, so the count reflects people. Each site is also calibrated against badge or manual counts and monitored continuously for accuracy.

Which platform is faster to deploy?

Usually Basking, because it works through existing WiFi, badge, and sensor data rather than a hardware install. VergeSense involves sensor planning and placement when dedicated sensors are part of the project.

Which is better for desk-level occupancy?

VergeSense, when desk- and room-level sensing with passive occupancy is the primary need. Basking can take sensor input too, but its center of gravity is portfolio and lease decisions.

Which is better for lease decisions?

Basking. It connects occupancy with LeaseOps, AI lease abstraction, lease events, approvals, and reporting — so a usage signal becomes an approved, finance-ready decision.

How accurate is Basking’s lease abstraction, and how much time does it save?

Abstraction runs at 90–98% accuracy depending on document type, with review at roughly 30 minutes per lease versus 3–6 hours manually — about an 87% time reduction, or around 5,250 hours on a 500-lease portfolio.

Can VergeSense support portfolio planning?

Yes, through Meridian and Predictive Planning — forecasting, breakpoints, scenario modeling. Its strongest fit is space planning rather than lease administration.

Is Basking secure and compliant for enterprise and EU use?

Yes. Basking is SOC 2 compliant, meets GDPR, CCPA, and similar privacy regulations, and supports IFRS 16 lease accounting. Because occupancy comes from anonymized network signals rather than cameras, it often clears privacy and InfoSec review more easily than image-based sensing..

Where should an enterprise CRE team start?

If the first priority is granular measurement inside specific spaces, start with VergeSense. If it’s connecting occupancy, leases, approvals, and reporting into one decision flow, start with Basking.