Organizations are no longer focused on which system spots a weapon first. The conversation has shifted to a bigger and more practical concern: how gun detection fits into a wider security environment.
Early AI gun detection tools were often added as overlays on top of existing camera systems. That approach worked for single buildings, but as deployments expanded, many security teams discovered operational gaps around visibility, response workflows, and long-term scalability.
Confronted with these challenges, the priority for IT, facilities, and security leaders changes from simple detection to finding a more integrated solution for multi-site deployments.
Detection alone wasn’t enough; businesses needed systems that could connect alerts to real actions across multiple locations. That shift is why buyers comparing Omnilert and ZeroEyes are also evaluating platform-level options like Coram.
- Omnilert focuses on AI gun detection paired with automated emergency notifications.
- ZeroEyes centers on strict human-verified weapon detection through a 24/7 security operations center.
- Coram takes a platform approach, where gun detection is built into a unified, cloud-native video and response system.
This article provides an in-depth comparison between these three systems, guiding you to the best choice for your security and operational needs.
What AI Gun Detection Systems are Designed to Do?
AI gun detection systems are visual surveillance technologies built to identify visible and brandished firearms in real-time through live video feeds, usually within seconds. They are designed to recognize weapons that appear on camera, not concealed firearms or a person’s intent.
Unlike traditional security solutions, AI gun detection systems use advanced computer vision models to analyze video frames. Before a shot is fired, the gun detection system triggers alerts upon detecting a weapon. When a suspected weapon appears:
- The AI model flags the event.
- The system sends the alert for verification.
- Security personnel or IT teams confirm the threat.
- Notifications or response protocols are triggered.
The goal is to shorten the time between a weapon appearing on camera and security teams taking action. Systems like ZeroEyes, for example, scan existing camera feeds around the clock and route suspected detections to a staffed operations center for verification before alerts are sent.
What “AI Gun Detection” Means in Practice?
While most vendors use similar terminology, “AI gun detection” can mean very different things operationally. In real deployments, these systems usually fall into two categories:
- Overlay-Based Detection Tools
These are standalone AI layers added on top of an existing camera or video management system. Typical characteristics include:
- Connect to existing cameras or VMS
- Detect visible firearms
- Send alerts to administrators or security teams
- Focus primarily on weapon detection
One advantage of this model is speed and cost efficiency. Since the system sits on top of the existing security stack, organizations can deploy AI detection quickly without replacing cameras or overhauling infrastructure.
This approach also preserves prior investments in video systems, reducing switching costs.
- Platform-Level Detection
In this approach, gun detection is just one feature inside a broader, integrated security system. Instead of bolting AI onto an existing stack, the platform:
- Manages cameras, video, and access
- Runs AI detection
- Handles alerts and security workflows
- Connects to access control and emergency response
Coram follows this platform-level model, where cameras, access control, emergency management, and AI alerts operate inside a single cloud-managed system.
Omnilert vs ZeroEyes vs Coram Platform Overview
Omnilert
Omnilert approaches visual gun detection with a strong emphasis on the quality of the data used to train its AI models. It doesn’t rely heavily on staged or simplified imagery; the company focuses on real-world surveillance footage that mirrors what cameras actually see in daily operations.
That includes challenging conditions like low lighting, motion blur, crowded scenes, partial obstructions, and unusual camera angles. By training its models on these realistic scenarios, Omnilert reduces ambiguity at the earliest stage of detection.
The idea is to build confidence directly into the AI layer, so the system can distinguish real threats from harmless objects before alerts are ever triggered.
This upstream focus helps keep alert volumes more manageable and reduces the workload placed on human reviewers, especially as deployments scale across multiple buildings or sites.
The tradeoff is that, as an overlay solution, Omnilert relies on integrations with existing video systems and response tools. In larger environments, this can require additional coordination across platforms to ensure alerts, workflows, and escalation paths remain aligned.
ZeroEyes
ZeroEyes takes a model-centric approach that incorporates data-augmentation techniques with real-life imagery, including staged or synthetic scenarios.
These controlled training environments can help the system learn how firearms appear under different conditions and speed up the development of detection models.
Operationally, ZeroEyes is built around centralized human verification. When the AI flags a potential weapon, the alert is sent to a staffed control center where trained personnel review the footage before escalating it.
In this structure, human judgment becomes the primary method for resolving uncertainty after the initial detection. Because of this design, the platform leans heavily on its review processes and monitoring teams to maintain accuracy and consistency.
As deployments grow, the effectiveness of the system is closely tied to the scale and efficiency of these centralized workflows.
Coram
Coram’s approach to AI gun detection is a cloud-first security platform, not a standalone feature. Coram Point analyzes every video frame locally and detects brandished firearms, including handguns and long rifles. The detection models run on the device, so initial analysis happens without relying on internet bandwidth.
When the system identifies a possible weapon, the event data is sent to the cloud for processing. Multiple AI models work together to confirm the detection, helping reduce false positives before any alert is issued.
Once the event is verified, notifications are delivered to the security team within seconds, based on response sequences they’ve already configured. These workflows can include actions like contacting emergency services.
Since video processing, AI detection, and alerting all operate from a single dashboard, the system doesn’t depend on third-party overlays or separate detection tools. Gun detection is simply one capability within this unified platform that manages cameras, monitoring, and response workflows from a single cloud-based interface.
This architecture allows organizations to act quickly, without the fragmentation that often comes with bolt-on security solutions.
Architectural Differences Explained
Although Omnilert, ZeroEyes, and Coram all support AI gun detection, they are built on very different architectural philosophies.
Those differences influence how alerts are generated, detection accuracy, response time, scalability, and long-term operating costs.
For buyers evaluating real deployments, architecture often determines how effective the system will be once it’s live.
Omnilert
Omnilert follows a data-centric model designed around early threat detection and fast, automated response. Its architecture typically depends on cloud processing, though it also supports on-premise deployments for organizations with stricter infrastructure requirements.
This flexibility allows the system to scale across large camera fleets. The detection models are trained primarily on real-world surveillance data, which helps the system recognize firearms in unpredictable environments.
Once a weapon is detected, Omnilert’s tight integration with its emergency notification system becomes the defining feature. This means the platform can trigger automated actions like lockdowns, mass notifications, or informing the police.
Verification can be handled by AI alone or through hybrid models involving internal staff, depending on the company’s risk tolerance.
ZeroEyes
ZeroEyes takes a different path, centering its architecture around human verification. The system is designed as a strict “man-in-the-loop” model, where the AI’s role is to identify potential firearms and route those clips to a 24/7 security operations center.
There, trained personnel review the footage before any alert is escalated. This downstream verification model prioritizes accuracy and consistency, especially in environments where false positives are unacceptable.
Since the platform focuses exclusively on firearm detection, its workflows are optimized around that single use case. Once a threat is confirmed, ZeroEyes provides actionable intelligence, such as the weapon type, suspect description, and location, directly to law enforcement.
Coram
Coram, on the other hand, is built around an edge-first, platform-native architecture. The platform uses its own edge device, called Coram Point, to analyze video locally. This means every frame is processed on-site, reducing latency and limiting the need to stream sensitive footage across the internet.
The system uses proprietary AI models optimized for real-time detection of both handguns and long rifles. When a potential threat is identified, the event is sent to the cloud for additional confirmation before alerts are issued.
Because Coram depends on cloud coordination for verification, alerting, and centralized management, businesses still need stable internet connectivity to get the full benefit of real-time notifications and multi-site visibility. When you’re offline, camera footage is processed to local hard drives.
Omnilert vs ZeroEyes vs Coram — Feature Comparison
Multi-Site and District-Wide Scalability
AI gun detection is moving beyond single buildings into district-wide and enterprise deployments. Scalability now becomes less about detection accuracy alone and more about governance, flexibility, and operational control.
Security leaders are asking how the system behaves across different locations, each with different policies, risk levels, and response protocols.
Omnilert
Omnilert is built with these varied environments in mind. Its architecture assumes that organizations don’t operate the same way at every site, so it allows security teams to customize response actions by location, camera group, or operational role.
A school campus, for example, can have different escalation paths than an administrative office or warehouse. The platform also integrates with existing camera systems and enterprise solutions.
This helps large organizations maintain centralized visibility while still giving local teams the control they need. This balance between standard governance and site-specific flexibility is useful for healthcare systems, multi-facility enterprises, and global organizations.
ZeroEyes
ZeroEyes’s architecture routes detections to a single, vendor-managed operations center, where trained personnel verify alerts and handle escalation. This model minimizes oversight for businesses that prefer a consistent, fully managed workflow.
Every alert follows the same path, regardless of location, which can make policy enforcement more predictable. However, this structure also means there are fewer native options for customizing workflows to the unique operational needs of different sites.
Coram
Coram approaches scalability from a platform perspective. It leverages current IP cameras for AI-enabled endpoints and manages them through a unified cloud dashboard. Security teams can monitor thousands of cameras across multiple campuses, stores, or facilities from a single interface.
The AI runs on local edge devices; each site processes video without requiring heavy or constant internet bandwidth. These edge units can be sized to fit different environments, from small offices to large campuses with dozens of camera feeds.
Deployment is also designed for speed. Companies can roll out the platform across entire districts or enterprises without major hardware overhauls, and the subscription-based model reduces the cost and complexity of large-scale upgrades.
Which AI Gun Detection System Is the Right Fit?
The right choice depends less on which system detects a weapon and more on how your organization wants detection, verification, and response to work together. Each solution is built around a different operational philosophy, so the best fit usually comes down to your workflows, infrastructure, and scale.
When Omnilert Makes Sense?
Omnilert is a strong option for organizations that place a heavy emphasis on emergency communication and automated response. Its tight integration with notification systems allows alerts to trigger lockdowns, mass messages, or law-enforcement calls in seconds.
This makes it well-suited for settings that rely on alert-driven safety workflows, where quick communication and automated actions are just as important as the detection itself.
When ZeroEyes Makes Sense?
ZeroEyes fits environments where strict human verification is a top priority. Its architecture directs every detection through a centralized operations center, where trained personnel review alerts before escalation.
This approach is ideal for businesses with an extremely low tolerance for false positives and a preference for a fully managed, vendor-operated verification process.
When Coram Makes Sense?
Coram is best suited for organizations looking for a single unified security platform rather than a standalone gun-detection system. It works well for multi-site schools, campuses, or enterprises that want to manage cameras, AI detection, and response workflows from one system and interface.
For teams trying to simplify their security stack, Coram provides detection, video management, and operational controls inside the same cloud-native platform.
Final Takeaway — Tools vs Platforms
Omnilert and ZeroEyes represent the first generation of AI gun detection: specialized overlays designed to identify firearms and trigger alerts. That model works well when:
- The environment is small
- The existing security stack is stable
- Gun detection is the primary goal
But as organizations grow, the conversation changes. Security directors start requiring platforms that connect detection to video surveillance, access control, response workflows, and long-term scalability.
That’s where platform-native systems like Coram enter the picture. It doesn’t offer AI gun detection as a separate system; it embeds it into a unified, cloud-first security environment.
For buyers evaluating Omnilert, ZeroEyes, and Coram, the real decision often isn’t just which gun detection system to choose. It’s whether to add another overlay or move to a platform where detection, video, and response already work together.
If your organization is thinking beyond detection and toward full-stack security operations, that architectural shift is usually where the decision gets made.
