Ozni AI provides edge-deployable multi-intelligence sensing solutions for enhanced operational insight. Their platform integrates AI-driven context awareness and pattern-of-life analytics for advanced RF spectrum monitoring, drone detection, and emitter geolocation.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional radio frequency (RF) spectrum monitoring and emitter detection methods often lack the real-time analytical capabilities and edge deployment flexibility required for dynamic operational environments. This limits situational awareness and the ability to derive actionable intelligence from complex RF signals.
Solution
Ozni AI delivers agentic multi-intelligence (multi-INT) sensing solutions designed for edge deployment. Their platform integrates AI-driven context awareness and pattern-of-life analytics to provide enhanced operational insight. Key capabilities include advanced RF spectrum monitoring, detection of RF-dark drones through unintended emissions, and drone-based emitter geolocation. These solutions enable users to understand and interact with the RF environment more effectively, supporting autonomous sensing and improved situational awareness.
Target Audience
Ozni AI's primary customers are organizations requiring advanced RF sensing capabilities for edge deployment, including defense, intelligence, and public safety sectors.
Features
- **Jibber Jabber**: Natural-language commanding interface for RF spectrum monitoring, leveraging Large Language Models (LLMs) to make RF data accessible.
- **NEMESIS**: Edge-based multi-INT exploitation platform for autonomous sensing and situational awareness.
- **AURA**: System for detecting RF-dark drones by identifying unintended electromagnetic emissions.
- **REGAL**: Drone-based system for precise geolocation of RF emitters.
- AI-driven context awareness and pattern-of-life analytics for enhanced operational intelligence.
- Solutions tailored for specific sensor challenges and mission objectives.
- RF simulation capabilities that merge high-fidelity modeling with machine learning for efficient AI training.