RF Signal Intelligence AI Consulting

Deploy deep learning models (CNNs, Transformers) that automatically intercept and classify complex RF modulations with >95% accuracy in under 100ms, enabling immediate threat assessment and response.

Implement AI-driven TDOA/FDOA and fingerprinting techniques to geolocate signal sources with high precision, reducing manual analysis time and enabling rapid targeting or neutralization.

Utilize unsupervised ML to establish RF environmental baselines and detect novel, zero-day jamming or spoofing signals before they impact critical communications, shifting from reactive to proactive defense.

Engineer and optimize models for deployment on ruggedized edge hardware (NVIDIA Jetson, SDRs) ensuring continuous intelligence, surveillance, and reconnaissance (ISR) capabilities in disconnected environments.

All model development, training, and validation occurs within sovereign, air-gapped infrastructure, ensuring compliance with defense regulations and protection of sensitive signal data.

Build high-fidelity AI-driven simulations of RF battlespaces to test network configurations, predict adversarial actions, and train models on synthetic yet realistic data, de-risking field deployment.

Deploy deep learning models (CNNs, Transformers) that automatically intercept and classify complex RF signals in under 100ms, even in dense, contested spectrum. We deliver systems with >95% accuracy for modulation recognition and specific emitter identification, enabling rapid threat assessment.

Engineer AI-powered systems that fuse Time Difference of Arrival (TDoA) and Frequency Difference of Arrival (FDoA) data with geospatial context to geolocate RF emitters with high precision. Our solutions reduce positional error by over 60% compared to traditional methods, critical for dynamic targeting and surveillance.

Develop unsupervised and semi-supervised ML models to detect novel jamming, spoofing, and zero-day cyber-physical attacks by identifying deviations from baseline RF patterns. We implement continuous learning pipelines that adapt to evolving threat libraries, providing early warning for critical infrastructure.

Build AI systems that forecast spectrum occupancy and predict adversarial behavior, enabling proactive dynamic spectrum sharing and electronic protection. This transforms operations from reactive to predictive, optimizing communication resilience and denying adversary use of the spectrum.

Optimize and deploy lightweight RFML models on ruggedized edge hardware (NVIDIA Jetson, SDRs) for low-latency, offline signal intelligence at the tactical edge. We ensure models operate with <2W power draw and maintain high accuracy without cloud dependency, enabling dismounted and airborne operations.

Architect systems that correlate and fuse RF signal intelligence (SIGINT) with data from other intelligence sources (GEOINT, IMINT) using multimodal AI. This creates a unified operational picture, dramatically improving situational awareness and reducing analyst cognitive load for faster decision cycles.