Tackling the Nation’s Defense Problems.

At nou Systems, we’re advancing artificial intelligence by fusing physics with machine learning, building explainable models, and tailoring solutions for data-scarce environments. From radar performance breakthroughs and declassification tools to trajectory prediction and Warfighter training, our AI solutions are transparent, resilient, and mission-ready—delivering accurate results and actionable insights where they matter most.

Physics-Informed Machine Learning

By including physics into our machine learning models, from feature engineering to loss functions to ML model architecture, we’re solving classification and prediction problems more efficiently, more correctly, and with fewer false positives.

Example: ZODIAC classifier that uses physics-informed features to revolutionize radar performance for ballistic missile defense, and is being deployed in theater.

Explainable AI

Machine learning models are often seen as “black boxes”—data goes in and answers come out, but the reasoning behind those answers remains hidden. At nou Systems, we design ML models and methods that open that black box, giving users visibility into how decisions are made. This transparency builds understanding, trust, and confidence in AI outputs.

Example: Chiroptera Cortex is a sonogram operator training system that leverages ML to detect whale calls in sonograms. By highlighting the features used in detection, it helps operators recognize what to look for and improves their effectiveness.

Large Language Models for Insight & Interfaces

Large Language Models (LLMs) excel at summarizing information and providing intuitive natural language interfaces for asking questions, refining responses, and extracting insights. At nou Systems, we build powerful LLM-driven tools by combining prompt engineering, few-shot and zero-shot learning, retrieval-augmented generation, and fine-tuned models. These solutions are deployed securely in GovCloud or with locally installed open-source models, ensuring flexibility and compliance.

Example: iLLuMinate is an LLM-powered declassification tool that ingests security classification guides as PDFs, parses them, identifies sections that remain classified, and gives users an intuitive interface to review and approve redactions.

AI in Data-Starved Environments

Many challenges—particularly in defense—lack the large-scale datasets typically required to train advanced neural networks or transformer-based ML models. To overcome this, we combine innovative data augmentation and generation techniques with limited live data, enabling the development of effective ML models even in data-scarce conditions. This approach allows us to bring the power of advanced AI to missions and environments that traditionally lacked sufficient data to support it.

Example: Trajectory Predictor uses ML models to back-propagate target trajectories and determine their point of origin. Trained on verified and validated simulated data, and refined with limited live-fire data, it provides accurate predictions even in constrained data settings

Reinforcement Learning for Warfighter Training

Deep reinforcement learning (RL) has produced agents capable of making complex decisions and outperforming the world’s best players in games like StarCraft 2 and Go. However, without explainability, these agents cannot transfer their learned strategies to humans. As part of our explainable AI initiatives, we’ve developed advanced RL-trained agents that not only perform at a high level but also explain their decision-making, enabling Warfighters to learn and apply these techniques in real-world missions.

Example: IDART provides RL agents that recommend courses of action for missile defense, complete with clear explanations for those recommendations.