INFORMN builds AI-native machine learning software that detects adversarial pre-positioning, infrastructure degradation, and cross-domain cascade failure in US critical infrastructure. Our platform uses Riemannian geometric AI models, GPU-accelerated inference, and absence-based machine learning to detect what conventional monitoring misses. Built for national defense. Deployable on NVIDIA GPU infrastructure including air-gapped Jetson-class edge environments.
Explore Our CapabilityUnited States critical infrastructure (dams, nuclear plants, power grids, fuel supply chains) was constructed as a physically interconnected system and is governed as a collection of separate domains. Each sector has its own regulator, its own reporting requirements, and its own definition of a reportable incident. The result is a structural detection gap that exists independent of any single threat actor.
Cascade physics propagate in seconds to hours. Coordinated defensive response takes days to weeks. That temporal asymmetry is the vulnerability.
AI/ML cascade models map how compromise at one infrastructure node propagates through hydraulics, electrical grid physics, and supply-chain dependencies. Riemannian geometric neural networks identify cascade paths before adversaries use them.
Machine learning models read what should be present but is not. The absence of expected activity is a first-class ML detection signal. When monitoring systems report silence, our AI distinguishes between security and blindness.
Geometric AI models identify where shared architectural patterns across infrastructure create correlated vulnerability. A single exploit methodology can propagate across facilities that share structural characteristics invisible to conventional ML classifiers.
Cascade topology, monitoring blind spots, and software template monoculture interact multiplicatively. Understanding each in isolation understates the risk.
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