Physical AI

Artificial Intelligence. Real-World Impact.

Deploy cutting-edge computer vision and machine learning models to analyze physical environments, optimize operations, and predict threats.

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Overview

ZEI deploys physical AI — computer vision, behavioral analytics, and machine learning — that turns existing camera infrastructure into intelligent sensors. Edge or cloud-based analytics detect objects, classify behavior, and surface anomalies in real time, with privacy-aware design and human-in-the-loop verification.

Technology Partners

  • NVIDIA AI computing platform
  • Vaidio AI video analytics
  • Avigilon video surveillance by Motorola
  • Axis Communications network cameras
  • Verkada cloud-based security cameras
  • NVIDIA AI computing platform
  • Vaidio AI video analytics
  • Avigilon video surveillance by Motorola
  • Axis Communications network cameras
  • Verkada cloud-based security cameras
Representative real-world environment where ZEI deploys physical ai
In the field

A representative environment where ZEI engineers, deploys, and supports physical ai.

Problems We Solve

Where current systems fall short.

  • Cameras producing footage no one watches until something goes wrong
  • Operators overwhelmed by motion alerts that are mostly weather, animals, and shadows
  • Slow forensic searches through hours of timeline to find a specific person or vehicle
  • Loss prevention, PPE compliance, and queue analytics handled manually
  • No early warning for slips, falls, loitering, or aggression
Core Capabilities

Beyond Basic Security

Turn your existing camera infrastructure into a network of intelligent sensors.

Behavior Analysis

Predictive intelligence.

Detect loitering, aggressive behavior, or slip-and-fall incidents before they are reported.

Object Detection

Track what matters.

Identify specific vehicles, PPE compliance (hard hats/vests), or unattended baggage in crowded spaces.

Heatmapping & Flow

Operational insights.

Analyze foot traffic patterns, dwell times, and queue lengths to optimize retail layouts or staffing.

Anomaly Detection

Learn the baseline.

AI models learn the normal patterns of your facility and alert you only when something truly unusual occurs.

Typical Components

What gets installed.

A real deployment is more than the headline product. These are the components we typically specify and integrate.

AI-capable cameras

Cameras with onboard NPUs (Axis, Avigilon, Hanwha, Verkada, Bosch) that classify objects in real-time at the edge.

Edge analytics appliances

Local GPU appliances (NVIDIA-based) that run analytics on existing camera streams without replacing cameras.

Cloud analytics platform

Cloud platforms (Verkada, Genetec ClearID, BriefCam) for cross-site search, summarization, and forensic review.

Custom models

Bespoke models for niche use cases — specific object detection, custom behavior, regulated PPE — when off-the-shelf isn't enough.

Operator console

Workflows that surface verified events to operators with one-click case creation and clip export.

Data pipeline

Event metadata flowing into BI tools, ITSM, or custom dashboards for operations and loss-prevention teams.

Integrations

Connects to what you already run.

Our physical ai integrate with your existing identity, communication, and security systems for unified operations.

Active Directory integration
Active Directory
Slack / Teams integration
Slack / Teams
HR Platforms integration
HR Platforms
VMS Systems integration
VMS Systems
Alarm Panels integration
Alarm Panels
Fire Systems integration
Fire Systems
Building Mgmt integration
Building Mgmt
Custom API integration
Custom API
Deployment Process

How it gets built.

Every project follows the same engineering-led sequence — designed, documented, and delivered with no surprises.

  1. 01

    Use-case definition

    Pick 1–3 high-value use cases (loitering after hours, PPE compliance, queue length) before choosing models. Vague goals produce vague systems.

  2. 02

    Camera readiness audit

    Audit existing cameras for resolution, lens, frame rate, and lighting; identify which can run analytics and which need replacement.

  3. 03

    Architecture design

    Edge vs cloud decision, network capacity check, storage and retention design, and operator workflow.

  4. 04

    Pilot

    4–8 cameras running analytics in a real environment, measured against ground truth, tuned for false-positive rate.

  5. 05

    Rollout

    Phased rollout across the rest of the fleet with consistent calibration and operator training at each phase.

  6. 06

    Continuous tuning

    Monthly review of detection accuracy, false positives, and operator feedback; tuning per scene as conditions change.

Engineering Considerations

What our engineers look for.

  • Privacy is designed in: face blurring, role-based access to identifiable footage, clear retention policy, and signage where required by law.
  • Edge-first architecture keeps personally identifiable data local; only metadata leaves the building unless investigation requires the clip.
  • Models are evaluated on your scenes, not vendor demo reels — false positive rate is the metric that matters operationally.
  • Human-in-the-loop verification is the default for high-consequence alerts (police dispatch, emergency response).
  • Drift is monitored: model performance is measured monthly against ground-truth labeled events.
Maintenance & Support

After the install.

Service tiers built around what your facility actually needs — not a one-size-fits-all SLA.

Essentials

Annual model review, firmware updates, and tuning during business hours.

Pro

Quarterly model performance review, false-positive tuning, and 24/7 incident response.

Managed

Fully managed analytics operations: weekly review, custom model maintenance, and operator workflow optimization.

Reference Architecture

How the system fits together.

Reference architecture diagram for physical ai
Physical AI — reference architecture
Architecture Options

AI Processing

Where the intelligence lives.

Edge Processing

AI models run directly on the camera or local appliance.

  • Ultra-low latency
  • Minimal bandwidth required
  • Privacy-first architecture
  • Works offline

Cloud Processing

AI models run in the cloud on aggregated video streams.

  • Access to massive compute power
  • Continuous model improvements
  • Infinite scalability
  • Cross-site analytics
FAQs

Frequently asked.

Does AI surveillance create privacy risk?

It can — and it doesn't have to. We design analytics for privacy-by-default: face blurring at capture, identifiable data kept local, role-based access to clips, and clear retention rules. The brief explicitly excludes surveillance abuse use cases; we won't deploy systems that harass employees or visitors.

Edge or cloud analytics — which is better?

Edge is better when latency and privacy matter, when you have many cameras and limited WAN bandwidth, and when you want analytics to keep working during a cloud outage. Cloud is better when you need cross-site search, when you want to apply new models to existing footage, and when GPU cost would otherwise be high. Most enterprise deployments mix both.

Can existing cameras run analytics?

Modern cameras with onboard NPUs from Axis, Avigilon, Hanwha, Verkada, and Bosch run analytics natively. Older cameras can feed an edge appliance (NVIDIA-based) that runs analytics on their streams. We audit your fleet and tell you what's achievable without replacement.

How do you avoid false positives?

Three layers: (1) modern analytics only fire on classified objects, not raw motion; (2) we tune per scene during the pilot to suppress false triggers; (3) high-consequence alerts go through human verification before action. The right metric is "alerts that result in real action," not raw alert volume.

What about ALPR (license plate) and facial recognition specifically?

ALPR is a mature, useful technology for parking, gates, and watchlists. Facial recognition is more sensitive — we deploy it only where it's lawful and consensual (e.g. badge-photo verification at access control), with strict retention and access controls. We do not deploy public-space facial recognition.

Let's build your system.

Tell us about your facility. Our engineering team will design a system tailored to your security, connectivity, and automation requirements.

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