Proptech.AI — Securing Premium Commercial Spaces with Intelligent Access Control

Background

Proptech.AI is a leader in AI-driven property management — running edge AI solutions for access control and analytics across 5,000+ global users in commercial and residential portfolios. In 2023 the company partnered with Medro Hi-Tech Systems to scale facial recognition and occupancy analytics for urban trophy properties. The pilot site was 330 Madison Avenue, a 1.5M sq ft Class A office tower in Midtown Manhattan managed by JLL, where security, tenant experience and ESG goals converge.

• Pilot site: 330 Madison Avenue — 1.5M sq ft, multi-tenant
• Users impacted: building occupants, visitors, security ops teams
• Primary aim: reduce entry latency, cut tailgating, and provide occupancy insights for operational efficiencies

Challenge

The building relied on legacy keycard systems that struggled with post-pandemic access patterns and hybrid work surges. Specific challenges included:

• Peak-hour delays: average entry waits of 2–3 minutes per person during morning surges.
• Tailgating risk: ~15% unauthorized tailgating incidents identified via manual logs and anecdotal reports.
• Operational strain: a 20-person security team incurred ~$300K/year in overtime and audit handling costs.
• Siloed systems: BMS, access control and visitor management did not share real-time signals, limiting occupancy-driven optimisations felt in JLL’s 2024 report.
• Privacy & compliance: GDPR/CCPA concerns and multi-tenant data governance required robust privacy design.

Medro Hi-Tech Systems’ Solution

Acting as Proptech.AI’s exclusive technology partner, Medro designed a privacy-first, scalable integration that combined:

Implementation & Phased Rollout

The rollout followed a careful, low-risk phased approach over 6 months:

Phase 0 — Discovery & Compliance (Weeks 1–4)

Stakeholder workshops with JLL, legal review for privacy, and an asset survey to map camera, elevator and entry points.

Phase 1 — Pilot (Weeks 5–10)

Pilot deployment on two floors and one main entrance: edge nodes, model validation with anonymized datasets, and user opt-in flows tested.

Phase 2 — Core Entry & Elevator Integration (Weeks 11–18)

Retrofitted 12 elevators and 4 main entrances; integrated with BMS signals and Proptech.AI’s visitor management. RESTful APIs ensured continuous service and zero downtime.

Phase 3 — Optimization & Scale (Weeks 19–26)

Rollout predictive surge features, fast-path lanes, and tailgating automation; tune thresholds and operator workflows based on live data.

Phase 4 — Audit & Handover (Weeks 27–28)

Deliver compliance reports, training for security staff, and a runbook for model refresh and incident response.

Results & Impact

Operational & user outcomes

• Security efficiency: Manual oversight reduced — security team redeployed from constant monitoring to exception handling.
• Tenant experience: NPS rose 40%; 92% of surveyed tenants reported "seamless" access during hybrid surges.
• Sustainability: occupancy insights cut HVAC overcooling by 12%, supporting net-zero initiatives.
• Analytics-driven space use: 25% reallocation of underused areas for events and tenant amenities.

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Key Takeaways & Future Outlook

• Privacy-first edge AI works: moving recognition to edge nodes reduces privacy exposure while keeping latency low.
• Data monetization: anonymized occupancy insights opened new revenue streams — tenants bought anonymized, aggregated footfall reports.
• Operational shift: access control changed from a cost center to a proactive operational asset that supports ESG and space monetization.
• Scalability plan: Proptech.AI & Medro plan to expand to 10 JLL properties by Q2 2026, integrating generative-AI for predictive threat modeling and richer incident summarization.