EyeOTmonitor's Latest Release: Auto-Discovery, Camera Image Analytics, and New Integrations

The physical security and infrastructure world is changing fast. Cameras are no longer just recording devices. Switches and gateways aren’t just network nodes. Every connected endpoint is now part of a living, breathing ecosystem that depends on visibility, context, and automation.

That’s where EyeOTmonitor’s newest release comes in. Over the past few months, our team has pushed forward a wave of updates that expand how integrators, service providers, and operators can see, understand, and manage their environments - no matter how diverse their devices or vendors may be.

This update introduces several major capabilities:

ImageAssure – AI-based camera image analytics that ensure every feed is clear and usable

AI Auto Discovery – smarter, faster, and context-aware device discovery

Hanwha Vision VMS integration – complete visibility into camera health and retention

Mobile Asset Tracking – map field resources to site issues in real time

NOC Services and Managed Operations – EyeOTmonitor-powered support for 24/7 uptime

Auto Topology Mapping and Command AI – a preview of the future of network intelligence

Let’s unpack what’s new and why it matters.

Seeing What Cameras See: ImageAssure

For years, integrators and operators have had to settle for basic “up or down” camera monitoring. A camera might show as online, but that doesn’t mean it’s actually capturing usable footage. A spider web, a blurry lens, or a storm-drenched dome can render video useless - and you might not know until after an incident.

ImageAssure changes that.

By analyzing the camera feed directly through its RTSP stream, EyeOTmonitor takes twice-daily image captures and evaluates them for clarity, brightness, and obstruction. The system can detect if a view is blurred, overexposed, dark, or covered — automatically triggering alerts or events based on severity thresholds you define.

Because it’s cloud-processed, ImageAssure adds almost zero overhead to local agents. It also works with virtually any camera vendor, giving integrators one unified way to monitor image quality across mixed environments - whether that’s Axis, Hanwha, Avigilon, or something else entirely.

Think of it as camera assurance for the real world — not just network connectivity.

Integrating with Hanwha Vision VMS

The update also introduces a native integration with Hanwha Vision VMS, letting EyeOTmonitor connect directly to one of the industry’s most widely deployed video management platforms.

Once connected, EyeOTmonitor can identify whether each camera is online, actively recording, or experiencing retention gaps - for instance, if a system meant to retain 30 days of video is only holding 28.

The integration also synchronizes all camera metadata automatically. That means new Hanwha cameras appear instantly in EyeOTmonitor’s device list, ready for monitoring and ImageAssure analysis without manual setup.

Next on the roadmap: Milestone VMS, extending the same seamless experience across even more ecosystems.

Smarter Auto Discovery with AI

Most network monitoring platforms discover devices by scanning ports. It’s a brute-force method that’s slow, incomplete, and noisy - sometimes even triggering intrusion detection systems in sensitive environments.

EyeOTmonitor’s new AI Auto Discovery takes a completely different approach.

Instead of probing every port, EyeOTmonitor reads the data that devices are already sharing through SNMP. It collects object identifiers, model strings, and system descriptions, then sends them through an AI model trained to recognize and classify device types and vendors with high accuracy.

This means EyeOTmonitor doesn’t just find “a camera.” It identifies a Hanwha XNV-6080R fixed dome. It doesn’t just see “a switch.” It knows it’s a Cisco Catalyst 3560 Layer 3.

The process is fast, lightweight, and precise. Devices are added to a device candidates window, where teams can choose which ones to onboard for monitoring. That control matters - it prevents noise, keeps licenses clean, and ensures you’re only watching what truly matters to your operation.

Once approved, devices are fully mapped, monitored, and ready to feed real-time data into your dashboards, maps, and alerts.

Tracking People, Not Just Devices

Visibility isn’t just about hardware. Integrators and operators with distributed teams - think smart cities, airports, utilities, or mining operations - need to know where their field staff are relative to active issues.

The new mobile tracking capability adds human context to your operational map.

With user permission, the EyeOTmonitor mobile app can track technicians and overlay their positions alongside site alerts or outages. This enables faster response coordination, trend analysis, and time-on-site metrics.

In other words, it helps you understand not just what’s wrong, but who’s closest to fix it.

From Monitoring to Managed Services: EyeOTmonitor-Powered NOC

Many integrators excel at deploying systems but struggle to scale proactive monitoring and response. Their customers - cities, campuses, enterprises - increasingly expect managed uptime, SLAs, and 24/7 coverage.

That’s where EyeOTmonitor’s NOC Services come in.

Our team can act as an extension of the integrator, providing Tier 1 response, ticket creation, and alert triage using the same EyeOTmonitor platform that powers your customers’ sites.

Each NOC engagement follows the integrator’s runbook:

- Priority 1 alerts trigger middle-of-the-night notifications

- Standard events can be batched for morning review

- Tickets can automatically open in systems like ServiceNow or Teams

We can provide 9-to-5 coverage or full 24/7 monitoring - helping partners scale managed services revenue without the overhead of staffing a round-the-clock operations center.

It’s a powerful bridge between integration and recurring service delivery.

Coming Soon: Auto Topology Mapping

The next evolution of Auto Discovery is visualization.

We’re developing Auto Topology Maps that use LLDP, CDP, and vendor-specific data to draw real-time relationships between devices. Integrators will be able to right-click a switch and instantly reveal all connected cameras, radios, and edge devices — organized visually by layer or hierarchy.

No more manual diagramming. No more guessing what’s connected where. Just live, data-driven topology that grows as your network does.

Command AI: Operational Intelligence for the Modern NOC

The final piece in the roadmap is Command - an AI operations engine designed to cut through alert noise and surface what matters most.

Command aggregates every alert, event, and severity change across time and applies contextual AI to understand relationships. It learns that Camera A connects to Switch B, which connects to Router C. It sees that Switch B has 17 severity changes in 24 hours - and tells you that’s where your attention should go first.

The goal is simple: when you start your day, you shouldn’t have to dig through 200 alerts to figure out what’s critical. Command will do that analysis for you.

EyeOTmonitor remains your investigative platform. Command becomes your operational intelligence layer - a place to ask questions like “Which sites had the most incidents this week?” or “Which devices have the highest failure correlation?”

A Platform That Grows with You

This release represents a major step toward what EyeOTmonitor is becoming - a unified platform for AI-powered operational intelligence across cameras, networks, and IoT infrastructure.

It’s built for integrators who want to move from project-based installs to recurring services. For service providers who manage thousands of distributed assets. And for operators who simply need to know that everything — from cameras to sensors to gateways - is performing as intended.

As Kirill Sokolinsky summed it up during the release session:

“We’re not just monitoring devices anymore. We’re understanding them — how they connect, behave, and impact the world around them.”