VergeSense vs OfficeSpace: AI Space Planning Compared

An AI space planning tool is only as good as the data sitting underneath it. That single fact decides whether a neighborhood plan holds up six months after rollout, and if a lease decision survives the next board review.

If you are a real estate or workplace strategy leader, you may have watched this play out. A planner produces a stack plan that looks clean on paper. Six months in, the floor is uneven. One neighborhood is jammed, another sits half-empty, the team that "needed" 80 seats is showing up at 35% utilization.

You can avoid this scenario by grounding your plan in concrete data rather than assumptions. The tools you use to do this will play a key role in your success.

OfficeSpace acquired Dojo AI in October 2025 and launched AI Canvas in December 2025, putting it on the same shortlist as VergeSense for buyers evaluating their next space planning investment.

The two platforms approach the same problem from different directions, and the differences matter most where the cost of being wrong is highest: lease decisions, neighborhood right-sizing, and post-occupancy verification.

This piece compares VergeSense and OfficeSpace on:

• AI space planning
• How each engine works
• What data it's trained on
• Where each one is strongest
• Which buyer profile fits each tool

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At a Glance: VergeSense vs OfficeSpace

Both VergeSense and OfficeSpace target the same kind of user: enterprise workplace and real estate teams trying to make faster, more confident space planning decisions with less time spent on manual data analysis.

Both ship AI-powered planning capabilities, and both integrate with Microsoft,, badge systems, and room booking solutions.

OfficeSpace is a mature workplace management platform. It was founded in 2004, with $150M strategic investment from Vista Equity, 800+ customers including BCG, KPMG, Scotiabank, Capital One, OpenAI, AstraZeneca, and Airbnb. The acquisition of Dojo AI added patented stack planning to that footprint; AI Canvas wraps it in agentic workflows across planning, experience, insights, and facilities.

VergeSense grounds your right-sizing, design, and stack planning decisions in data from 250M+ square feet of measured occupancy data. Bring together all your occupancy data, from, badge, WiFi, sensors, and more into a single platform, then model the effects of proposed space, policy, or headcount changes on your space, employee experience, and bottom line.

Predictive Planning is powered by VergeSense’s Large Spatial Model, meaning you get planning recommendations grounded in how comparable portfolios have actually behaved.

Use the table below to understand how the two platforms compare on space planning.

Where VergeSense Wins

The biggest difference between VergeSense and OfficeSpace is the engine behind the recommendation. OfficeSpace organizes the space you already have. VergeSense predicts the space you'll actually need, then lets you pressure-test the decision before you commit a dollar to it.

Forecasting from the Large Spatial Model, Not Rules-Based Optimization

Predictive Planning forecasts how a change will play out before you make it. Draft a new layout, shift a policy, or model a headcount change, and the Large Spatial Model uses VergeSense's portfolio data to predict how teams will actually use the space: when they'll come in, how they'll cluster, and which areas they'll under-use. Whether you're right-sizing a portfolio, redesigning a floor, or restacking teams, you see the impact on capacity, experience, and cost while it's still a scenario, not after the buildout.

On the other hand, OfficeSpace's AI planning is rules-based optimization. Given the seats, departments, adjacencies, and historical utilization a planner inputs, it auto-generates a stack plan that satisfies those constraints. The output anticipates behavior based on the planner's assumptions.

Portfolio-Wide Coverage, Not Just Where Sensors are Installed

Planning recommendations break down at the seams of a portfolio: the buildings, floors, and spaces where the data goes quiet. VergeSense closes that gap with a layered approach:

• Predictive Planning helps you understand occupancy patterns for unmeasured spaces, using the LSM to extrapolate from comparable measured environments, or bring in inputs like badge or videoconferencing data
• WiFi data for floor and neighborhood-level occupancy counts.
• Infinity Area Sensors for the high-value individual spaces, like executive suites, conference rooms, focus rooms, where granular accuracy is key.

The result is portfolio-wide coverage you can plan against. You are not building a stack plan for the 30% of your portfolio that happens to be instrumented; you are planning across the whole footprint.

Passive Occupancy Detection Helps You See the Complete Picture

A bag on a chair, a jacket over a seat, a laptop left open while someone grabs coffee — the space is taken, but most sensors mark it empty the moment the person walks away. Badge and booking systems do the same: the moment the meeting ends on the calendar, the room reads as available, even if people are still in it. Forecasts built on that incomplete signal overstate how much space is really free.

The Infinity Area Sensor uses computer vision to detect signs of life, including people’s belongings, not just their bodies.

Planning recommendations built on that signal stay accurate through the gaps where badge data, booking systems, and motion-only sensors go quiet.

Where OfficeSpace Is Strong

Stack planning and moves-adds-changes workflows.

Block-and-stack planning, MAC workflows, and scenario layouts are well-built, and the Space Planning Agent adds natural-language prompting on top. Teams running frequent reorganizations or floor-by-floor stack changes will find the day-to-day planning UX deeper here than in a platform built around portfolio-level decision intelligence.

Workplace experience management.

Wayfinding, desk booking, room booking, visitor management, facility requests, and asset management all sit inside one platform. For workplace experience teams whose mandate is the in-office employee day, that breadth removes the need to integrate four or five-point tools.

Buyer Fit: When to Choose VergeSense vs When to Choose OfficeSpace

Choose VergeSense if your next decision is a lease renewal, a portfolio consolidation, or a neighborhood right-sizing, and the question in the room is whether the data behind the recommendation will hold up to a CFO or board review.

The platform is built for real estate and workplace strategy leaders making portfolio-level calls on observed behavior.

Choose OfficeSpace if your mandate is the in-office employee day, such as desk and room booking, visitor management, wayfinding, frequent moves-adds-changes, stack planning at the floor level, and you want that breadth in a single platform. The Space Planning Agent adds AI on top of workflows your team already runs.

Weighing AI space planning tools and need to know which one fits your portfolio strategy?
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4,100 Hours of Ghosted Meetings Eliminated: Consulting Firm Avoids a Floor Lease at Hudson Yards

A management consulting firm was receiving complaints about a lack of conference rooms at its Hudson Yards office, so the workplace team modeled another floor lease to add meeting capacity.

The space planning recommendation, on paper, was clear: add space.

VergeSense, combined with the firm's space booking data, told a different story: recurring meeting blocks were the source of the issue: 40% of 10,400 hours of conference room bookings were being ghosted, so rooms sat empty while colleagues looked for somewhere to meet.

Instead of leasing another floor, the firm integrated VergeSense space booking automation with its booking platform to automatically release ghosted rooms when attendees did not show.

The outcome: 4,100 hours of ghosted meetings eliminated per month, and an estimated $50K per month in avoided leasing costs.

The decision was integration-first. The firm did not replace its booking system or workplace experience tools: it layered VergeSense's occupancy intelligence into the workflows employees were already using, and let the data correct a planning recommendation that would otherwise have been wrong.

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FAQs about VergeSense vs OfficeSpace

How does VergeSense's AI space planning differ from OfficeSpace's AI Canvas?

VergeSense's Predictive Planning forecasts how teams will actually use a proposed plan, before you commit to it. It draws on the Large Spatial Model, trained on 250M+ sq ft of measured workplace behavior across 200+ enterprises, to predict when people will come in, how they'll cluster, and which spaces they'll under-use, so you can right-size, redesign, or restack with confidence.

OfficeSpace's Space Planning Agent, built on Dojo AI's patented technology inside AI Canvas, runs rules-based optimization, auto-generating stack plans that satisfy the constraints and adjacencies a planner enters.

Do I need to rip out OfficeSpace to use VergeSense?

No. VergeSense is a listed native integration on OfficeSpace's integrations page, and some customers run both — OfficeSpace for desk and room booking, visitor management, wayfinding, and day-to-day operations, and VergeSense for occupancy intelligence and Predictive Planning underneath. Because OfficeSpace is sensor-agnostic, layering VergeSense's passive occupancy data and behavioral simulation on top of an existing OfficeSpace deployment is a common pattern.

Which platform is the better fit for a real estate leader focused on lease and portfolio decisions?

When the next decision is a lease renewal, a consolidation, or a footprint right-sizing, you need to know how much space you'll actually need before you sign, not after. Predictive Planning forecasts demand across the whole portfolio on the Large Spatial Model, so you can model the scenarios and walk into the decision with evidence the CFO will accept. OfficeSpace is built to manage the space you already have, not to tell you how much of it you should keep.

What data does each platform use to inform space planning?

VergeSense unifies the full range of occupancy signals into one measurement layer: Infinity Area Sensors, WiFi counts, and integrated booking and badge data, with room for inputs like videoconferencing. That combined picture feeds the Large Spatial Model, trained on 250M+ sq ft across 200+ enterprises, so plans reflect how space is actually used, not one data stream's partial view.

OfficeSpace uses each customer's own floor plans, headcount inputs, badge and booking history, and ingested sensor data from third-party vendors (including VergeSense and SenzoLive), processed by Dojo AI's algorithms inside AI Canvas.