Applied AI

Machine learning and intelligent automation integrated into operational systems for real-time decision support.

What We Build

Signal & Anomaly Detection

ML models trained on side-channel signals, sensor data, and system telemetry to detect anomalies that indicate threats, failures, or deviations from expected behavior.

Classification & Pattern Recognition

Automated classification of hardware components, signals, imagery, and operational data using supervised and unsupervised learning approaches.

Decision Support Systems

AI-augmented tools that synthesize complex data into actionable insights for operators — reducing cognitive load without replacing human judgment.

Edge AI Deployment

Optimized ML models for deployment on resource-constrained hardware — enabling intelligent processing at the point of need without cloud connectivity.

How We Approach It

We apply AI where it solves real operational problems, not where it makes good slides. Every ML system we build is designed for the environment it will operate in — accounting for data availability, compute constraints, operator trust, and the consequences of being wrong.

  • Problem-first AI — the operational need drives the approach, not the algorithm
  • Designed for deployment, not just accuracy on test sets
  • Explainable outputs that operators can trust and act on
  • Robust to noisy, incomplete, and adversarial data

Where It Applies

Hardware security and assuranceSensor data analysisCyber threat detectionPredictive maintenanceLogistics optimizationIntelligence analysis supportAutonomous system behaviorsNatural language processing

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