Natural language meets real-time analysis – new possibilities for security and operations

With the launch of Eeva, US security provider Brivo is expanding its Eagle Eye VMS video management system with an AI-based video agent. The solution marks a further step towards autonomous, context-based video analysis – and addresses both traditional security requirements and operational use cases.

AI via voice command: analysis without barriers

At the heart of Eeva lies a novel approach: users define analysis tasks not via complex rule sets, but using natural language. Instead of configuration and programming, a simple description suffices – such as “Detect when an employee is not wearing a safety vest”.

The AI implements these specifications immediately, continuously monitors camera feeds and automatically triggers notifications or actions in response to defined events. This makes video analysis significantly more accessible – even for users without in-depth AI or IT knowledge.

Scalable AI for existing infrastructures

A key advantage: Eeva is camera-neutral and utilises existing infrastructures. Companies can deploy the AI functionality without additional hardware or specialised cameras. Integration into the Brivo Security Suite also enables a direct link to access control and other security processes.

Technologically, Eeva is based on powerful Visual Language Models (VLM) that can not only recognise objects but also interpret contexts and spatial relationships. This allows complex scenarios to be mapped more reliably than with traditional video analytics.

From reactive to proactive: New fields of application

The potential applications extend far beyond traditional security applications. Eeva supports companies in monitoring operational processes and identifying risks at an early stage:

• Industry and manufacturing: Detection of missing protective clothing to ensure compliance with safety regulations
• Retail and hospitality: Monitoring of hygiene standards and operational workflows
• Property management: Identification of illegal fly-tipping to reduce costs and environmental impact
• Energy and charging infrastructure: Detection of cable theft or vandalism

This shifts the focus from pure surveillance to data-driven process optimisation.

Practical examples: From Norway to the UK

Early implementations demonstrate the technology’s potential. In the UK, Eeva is already being used to prevent copper theft at electric charging stations. The AI reliably distinguishes between normal use and attempts at tampering – a significant advance over traditional analysis systems.

The solution is also being used in residential construction: in Norway, Eeva accurately identifies illegal fly-tipping without falsely reporting regular waste disposal operations. The result is lower operating costs and a noticeable improvement in the living environment.

Conclusion: AI is becoming an operational component of security

With Eeva, Brivo is driving the development of video surveillance systems towards intelligent, proactive platforms. The combination of natural language control, scalable architecture and context-based analysis lowers barriers to entry and significantly expands the range of applications.

For businesses, this means: less manual monitoring, more automated decision support – and a further step towards autonomous security and operational processes