Space Management Software for CRE: Optimize Occupancy with Butlr's AI Sensors

Butlr’s approach: AI sensors that are anonymous and heat-based

Butlr combines thermal (heat) sensors with AI analytics to deliver precise, privacy-preserving occupancy insights. The sensors detect heat signatures and movement patterns, then feed anonymized occupancy data into a cloud-based analytics platform that translates raw counts into detailed space usage metrics.

Why this matters

• Privacy-first: Sensors do not capture images or personally identifiable information, reducing privacy concerns and simplifying compliance.
• Reliable counts: Thermal sensing is robust in varied lighting and can be less intrusive than camera-based systems.
• Flexible deployment: Available as wireless or wired sensor options to suit different building infrastructures.

Core features of space management software powered by Butlr

Butlr’s solution, integrated with space management platforms, typically delivers a set of features that turn sensor data into actionable space strategy.

• Real-time occupancy dashboards showing current and historical use.
• Zone-level analytics for rooms, desks, corridors, and common areas.
• AI-driven people counting and dwell-time analysis.
• Scheduling and desk hoteling optimization based on actual demand.
• Alerts and automation triggers for cleaning, HVAC, or security.
• Integration capabilities with building management systems (BMS), workplace apps, and IWMS (Integrated Workplace Management Systems).

Common CRE use cases and benefits

Butlr-enabled space management supports a wide range of CRE objectives and delivers measurable benefits.

• Space right-sizing: Identify underused areas to reduce leased footprint or repurpose space for revenue-generating uses.
• Hybrid workplace management: Analyze in-office attendance patterns to tailor desk allocations and meeting room provisioning.
• Energy optimization: Use occupancy schedules to drive HVAC and lighting control, cutting utility bills.
• Operational efficiency: Schedule cleaning and maintenance when spaces are actually occupied, improving service quality and reducing waste.
• Tenant analytics: Provide tenant-facing dashboards or reports to show value and justify rent structures.

Benefits are measurable: reduced operating costs, increased utilization rates, and improved tenant satisfaction.

Implementation roadmap: from pilot to portfolio rollout

1. Assess and prioritize

• Map key spaces and define goals (e.g., reduce headcount per sq ft, lower energy use).
• Identify integration points (BMS, IWMS, CAFM).

2. Pilot deployment

• Choose a representative floor or building segment.
• Install a mix of wired and wireless sensors where appropriate.
• Validate sensor placement and calibration.

3. Data validation and model tuning

• Run the system for several weeks to collect baseline data.
• Use AI model adjustments to align counts with known occupancy events.

4. Action planning

• Translate insights into changes: desk reconfiguration, HVAC scheduling, or lease adjustments.
• Set KPIs and create reporting cadence.

5. Scale and integrate

• Roll out across additional floors/buildings.
• Integrate with desk-booking systems, facilities workflows, and energy controls.

6. Continuous improvement

• Periodically review sensor coverage, data quality, and business outcomes.
• Adjust thresholds and automation rules as patterns evolve.

Deployment checklist

• Clearly defined objectives and KPIs
• Site survey for sensor placement
• Connectivity (Wi-Fi, LoRa, or wired backhaul)
• Integration plan for downstream systems
• Privacy and communications plan for tenants/staff
• Training for facilities and workplace teams

Metrics and KPIs to track

Focus on metrics that drive decisions and ROI and link them to financial outcomes such as cost per square foot saved and payback period.

• Occupancy rate: average percentage of capacity occupied over time.
• Peak density: maximum people per zone at peak times—useful for safety and ventilation planning.
• Utilization rate: proportion of spaces used relative to availability (desks, rooms).
• Dwell time: average duration a person spends in a space.
• Turnover/churn: frequency of change in occupants for a space (helps schedule cleaning).
• Energy per occupied hour: energy used normalized to occupancy for targeted efficiency improvements.
• Space cost per user: real estate and operating cost divided by active occupants.

Link these metrics to financial outcomes (e.g., cost per square foot saved, payback period on sensor deployment).

Privacy, compliance, and tenant trust

Thermal sensing is inherently privacy-preserving because it measures heat rather than capturing images or identity information. Follow best practices to maintain trust and ensure compliance.

• Publish a clear privacy policy describing data types collected and retention schedules.
• Anonymize and aggregate data where possible before sharing with tenants.
• Limit access to raw sensor data; provide role-based dashboards.
• Follow local data protection regulations and consult legal counsel if needed.
• Communicate transparently to tenants and employees about what the sensors do and don’t collect.

These steps protect occupants and make adoption smoother across the portfolio.

Conclusion — actionable next steps

To start optimizing occupancy in your CRE portfolio with Butlr’s AI sensors, take focused, measurable actions and maintain tenant trust throughout deployment.

• Define one or two clear objectives (reduce energy costs, improve desk utilization, or justify lease renegotiation).
• Run a focused pilot in a representative area to validate assumptions.
• Track the core KPIs listed above and translate insights into operational changes.
• Scale gradually, maintaining tenant communication and privacy safeguards.

By combining space management software with anonymous, heat-based sensing, CRE teams can make faster, smarter decisions that reduce cost, increase utilization, and improve the tenant experience.