Occupancy Data as a Starting Point or as Part of the Decision Process

Most teams comparing these two already have occupancy data. The real question is how long it takes to turn it into a decision someone will sign.

That’s the gap between Basking and Density. Density starts with hardware — a network of dedicated sensors is the first thing you buy, install, and maintain. It measures space beautifully once that’s in place: fine-grained people counting, an analytics layer that replays any hour of any day. If your problem is we can’t see how this floor is used, that’s a strong answer.

But a lot of CRE and workplace teams aren’t short on data. They already have badge logs, booking records, WiFi signals, maybe sensors in a few buildings. The bottleneck shows up in the meeting where the CFO asks whether to renew a lease — or whether to consolidate two floors, or hold a site for another year — and the data exists somewhere, but nobody can pull it into an answer before the meeting moves on. The cost isn’t missing data. It’s the days between having the data and being able to act on it.

Both platforms turn buildings into occupancy data. The difference is what happens with it next. Density gives you a clear, detailed record to analyze — you go in, read the dashboard, and pull out what matters. Basking works the other way around: it watches the occupancy data for you, surfaces what changed and what’s worth acting on, and gives the team a short path from that signal to a decision. One waits for you to come ask it questions. The other comes to you with the answer and the next step.

Two products, two jobs

Both platforms help you understand offices. They start from different ends of the job.

Basking: from occupancy data to action

Basking starts where most teams actually get stuck — not at collecting data, but at acting on it.

It pulls occupancy from the infrastructure a building already has — WiFi, badge, and existing sensor feeds unified into one signal — then gives teams the tools to act on what they see.

• Instant Insights and AI Summary surface what changed without an analyst rebuilding a report.
• Basking AI answers plain-language questions about utilization.
• Occupancy covers presence and trends across the portfolio.
• Flow carries decisions through approvals.
• Smart Cleaning turns usage into operations.
• For real estate decisions specifically, LeaseOps connects that usage to the lease behind it.

The thread tying those together is that the platform comes to you. A floor starts trending down, a site crosses a threshold, a pattern shifts — Basking surfaces it and points at the next step, instead of waiting for someone to go digging. That’s what shortens the gap between a signal and a decision someone signs.

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. Alexander Gebauer, CEO of Allianz Real Estate Western Europe, described Basking as the piece that finally gave executives a real read on how their offices are used.

Density: occupancy infrastructure

Density is, at its foundation, a sensor company. The catalog tells the story: Open Area for area-wide people counting, Entry for doorways, and Waffle, a self-installable radar sensor priced around $15 a unit per month. On top sits Atlas, the analytics layer that turns those counts into heatmaps and space-performance views, down to 15-minute windows, alongside Live Wayfinding and an Advisory service.

It’s well-built. Density’s own framing — Time Used and peak versus average count — is a genuinely clear way to read a single space, and Heatmaps replacing site visits is a nice touch for distributed teams.

Where Density concentrates is measurement: how is this physical space being used, in detail? Sensors, coverage, counting, wayfinding. To their credit, they’ve publicly pushed back on the “99% accurate” claim the whole category overuses, arguing accuracy depends on the type of space, not a flat number. That’s an honest position.

It’s a reactive model by design: the data sits in Atlas, clearly presented, and the team goes in to read it and decide what it means. That works well when someone owns that analysis as their job. It asks more when the question is “what should we be paying attention to this week” and nobody has time to go look.

Primary metrics and insights

This is where starting from WiFi instead of a single sensor changes what you can actually see.

A sensor is tied to one desk or one room. It counts how many people are in that spot and whether it’s occupied right now. Useful — but the moment someone walks out and another person walks in, the sensor can’t tell you whether that’s the same employee returning or a colleague from a different team. It sees the seat, not the pattern.

Basking reads occupancy from WiFi, which stays anonymized but reflects how people actually move through a workplace. That means the data goes past a headcount into behavior: how long people stay, how often they come in, and how they travel between sites.

With dedicated sensors, what you get is people count and presence — per desk, per room, or per space type.

With Basking, you also get the trends behind the count:

• Duration of visits — how long people actually stay, not just whether a seat was taken
• Frequency of visits — how often individuals and teams return across a week or month
• Inter-site visits — how employees move between offices across the portfolio
• Department and business-unit patterns — how different groups use space, not just how a room performed

For a team trying to understand behavior across a portfolio, that’s a different class of insight than a per-room count — and it comes from data the building already produces.

How the data gets in

This is where the speed difference starts, because it decides whether you wait on hardware before you see anything.

Density starts with dedicated sensors. They get specified, ordered, mounted, and managed. Waffle is genuinely easier than most — self-installable, no ladders — and a floor can go live quickly once units arrive. For a single high-priority floor that’s a clean approach. For thirty offices across a portfolio, it’s a procurement and installation project before the first chart loads.

Basking can combine WiFi, badge, and sensor data, starting with the infrastructure a building already has. Those inputs fold into one occupancy signal, reported from portfolio level down to zone. A company with offices in London, New York, Munich, and Singapore can get a consistent portfolio-wide picture without treating every site as a hardware rollout — then add sensor depth where a specific question calls for it.

The two aren’t mutually exclusive on sensors; the difference is the starting point. Density’s sensors capture things device signals don’t — desk-level presence, passive occupancy, a person sitting still in a room — and where that detail is the decision, they earn their place. When the goal is to compare attendance and utilization across a portfolio and act on it, Basking gets there without waiting on an install.

On accuracy

Accuracy in occupancy data depends on what you’re measuring and how the system is calibrated — a flat percentage on its own doesn’t mean much.

Basking refines WiFi-based counts through Device Clustering, which groups the multiple devices a single person carries, and Non-Human Device Identification, which filters out printers and other hardware. Each site is calibrated against badge scans or manual counts, tracked with Mean Absolute Error and variance rate, and monitored continuously with recalibration on demand. You can pull a per-site accuracy report whenever you want to check it.

That keeps the data reliable at portfolio, building, and floor level — the altitude where most portfolio decisions actually get made. Where a decision turns on desk-level certainty, Basking can fold in dedicated sensor data too.

Deployment and cost

Deployment isn’t just IT effort. It’s how soon you can answer a real question — and how much you spend to get there.

Because Basking is software-based, there’s no hardware to buy, wire, or maintain. It connects to the WiFi and systems a building already runs on, so the cost curve looks nothing like a sensor rollout — and the data starts flowing fast. In practice, Basking can be deployed across hundreds of sites within a couple of hours, because turning on an existing network as a data source doesn’t require anyone on-site. You can compare attendance across 30 offices before deciding which sites deserve deeper measurement. First step is portfolio visibility, not a purchase order for sensors.

Density’s timeline and cost depend on hardware. Waffle lowers the friction and a single floor can go live fast, but portfolio-wide coverage still means specifying, shipping, installing, and managing sensors across every site you want to see — and paying for each one. That depth is worth it for a focused study; it’s slower and heavier when you need wide coverage soon.

Sensors go deeper in selected spaces. Basking goes wider, sooner — and gets you to the decision without the install.

From occupancy to action

Understanding a space is the start. The value lands when a team does something with what they learn — and that’s where the two platforms separate.

Take a floor reading 40% over a quarter. On its own, that’s an observation.

Basking is built to carry it into whatever comes next — reassigning a neighborhood, adjusting cleaning frequency to match real use through Smart Cleaning, folding it into a portfolio review, or routing a recommendation through Flow for sign-off — rather than leaving it in a chart for someone to act on later, elsewhere.

One of those next steps is a lease decision, and it’s a good example of why acting in one place matters. That same 40% floor means different things depending on the contract — a lease ending in six months leaves room to move, a long remaining term might point to subleasing instead. Through LeaseOps, the usage signal and the lease sit together, so the answer doesn’t wait on someone reconciling two systems by hand.

Density carries the data to a different stopping point: space performance. Atlas shows how a floor was used, where people lingered, which rooms sat empty — strong material for a workplace-design or occupancy-planning decision. Acting on it across leases, costs, and approvals happens in other tools. That handoff is where days tend to go.

AI: explore it yourself vs have it surface

Both lead with AI now. The split is who does the work of noticing.

Density‘s intelligence lives in observation. Atlas surfaces heatmaps and “Observations” that help a team notice how a space behaves — meeting rooms used by one person, neighborhoods that fill and empty on a rhythm. The insight is there; you go in and find it.

Basking‘s AI does the noticing for you. Instant Insights and AI Summary read the occupancy data and tell a team what changed — a floor trending down, a site that crossed a threshold, a pattern worth a second look — so the analysis shows up instead of waiting to be built. Basking AI answers plain-language questions about utilization and surfaces recommendations, so a workplace lead gets an answer without queuing behind the data team.

The same AI also handles the slowest paperwork when a decision needs it: DocsAI turns a lease PDF into structured data in one pass, with AI-as-a-Judge routing only low-confidence fields to a human — running at 90–98% accuracy and cutting review to about 30 minutes per lease from 3–6 hours by hand. It’s one more place the platform moves a team from noticing to doing.

Reporting more than one team can use

A decision rarely belongs to one department, so reporting either spreads across the business or stalls with the analytics team.

Basking reports across utilization, presence, rent, commitments by month, lease events, and approval history — exportable to CSV or piped into your BI tool. Workplace gets usage, finance gets commitments and cost, legal gets obligations, leadership gets a straight answer without waiting on five spreadsheets. The same data even runs operations: Smart Cleaning adjusts cleaning to real usage instead of a fixed schedule.

Density‘s reporting, through Atlas, centers on space performance — heatmaps, Time Used, peak and average count, exportable building and floor views. Good for the workplace-design conversation. Less suited to “what do we renew, renegotiate, or route for approval,” which is the conversation that decides the budget.

Which one fits you

Choose Density when the decision is about a specific space and you want precise, sensor-grade measurement of it — desk and room counting, passive occupancy, workplace design, real-time wayfinding.

Choose Basking when the decision is about the portfolio and how fast you can act on it — right-sizing, cost reviews, portfolio reviews, approvals, finance reporting, operations like cleaning, and lease decisions, all running on infrastructure you already have.

Shortest version: if the job is to measure a room precisely, look at Density. If the job is to get from occupancy data to an approved decision quickly, that’s Basking.

FAQ

What’s the main difference between Basking and Density?

Density gives you detailed occupancy data and dashboards to explore — you go in and analyze it. Basking is more proactive: it surfaces what changed and what’s worth acting on, then gives the team a short path from that signal to a decision. Density is strong for self-serve analysis of a space; Basking is built to move teams from data to action.

Is Basking just occupancy analytics, like Density?

No. Basking covers occupancy analytics, but it’s built to help teams act on what the data shows — Instant Insights and AI Summary to read it, Basking AI for questions, Flow for approvals, Smart Cleaning for operations, and LeaseOps for lease decisions. The focus is moving from a usage signal to an action, not just a dashboard.

Does Basking need sensors like Density?

No. Basking runs on infrastructure you already have — WiFi, badge, and existing sensor data unified into one signal. It can take dedicated sensor input where desk-level detail is needed, but it doesn’t require a hardware rollout to start.

Which is faster to deploy?

Basking, in most cases. There’s no sensor install, so data starts flowing through existing infrastructure. Density needs sensors specified and mounted before data flows, though its Waffle sensor is designed for quick self-installation.

Which is better for desk- and room-level measurement?

Density, when precise sensor-grade counting of a specific space — including passive occupancy — is the priority. Basking operates best at portfolio, building, floor, and zone level.

Which is better for lease and portfolio 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.

Is WiFi-based occupancy accurate enough to rely on?

For portfolio and floor-level decisions, yes, when it’s calibrated. Basking uses Device Clustering and Non-Human Device Identification to refine counts, then calibrates each site against badge or manual counts and monitors accuracy continuously.

How much time does Basking’s lease abstraction 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.

Where should an enterprise CRE team start?

If the first priority is precise measurement of specific spaces, look at Density. If it’s getting from occupancy data to an approved portfolio decision quickly, start with Basking.