General Automated Neural Network Development and Adaptive Learning Framework
GANNDALF streamlines RF machine learning from months to hours. Our comprehensive framework enables operators and analysts—regardless of ML expertise—to rapidly develop, train, and deploy neural networks for signal detection and classification, bringing advanced spectrum analysis capabilities to the tactical edge.
Building effective RFML systems traditionally requires specialized expertise across signal processing, data science, and software engineering. GANNDALF consolidates these disciplines into an integrated workflow, allowing RF operators and mission analysts to create production-ready models using their domain knowledge alone.
Purpose-built tools that bridge the gap between RF operations and machine learning deployment.
Designed around familiar RF concepts and workflows. Train neural networks through point-and-click interfaces, guided workflows, and automated parameter optimization—no programming required.
Built-in ML expertise handles model architecture selection, hyperparameter tuning, and validation metrics. Focus on signal characteristics and mission requirements while the platform manages the technical complexity.
Update models for new signal types in hours instead of weeks. Our wavegen augmentation engine synthesizes comprehensive training datasets from limited samples, enabling quick adaptation to emerging signatures.
Seamless integration from data collection to operational deployment.
The complete model development environment. Forge provides intuitive tools for dataset management, signal labeling, model training, and performance analysis. Import your RF captures, annotate signals of interest, and generate optimized neural networks—all through a visual interface designed for RF professionals.
Production-grade inference for tactical systems. Deploy Forge-trained models directly to edge processors, embedded GPUs, and FPGA platforms. Runner provides consistent, low-latency classification across diverse hardware configurations, from data centers to forward-deployed sensors.
A comprehensive interface that makes complex RF machine learning tasks manageable and repeatable.
Organize and track RF collections with metadata tagging, search capabilities, and version control for reproducible model training.
Examine time-domain, frequency-domain, and statistical features to understand signal characteristics and validate model decisions.
Visualize complex signal environments including frequency hoppers, burst transmissions, and dense multi-signal scenarios.
Accelerate dataset preparation with ML-assisted labeling that learns from your corrections, reducing annotation time by orders of magnitude.
Deploy GANNDALF. Deliver Results.
Modern spectrum operations demand rapid signal identification and classification at scale. GANNDALF provides the framework to build this capability efficiently, putting advanced neural network development within reach of your existing RF teams. Discover how organizations are using GANNDALF to reduce model development cycles from months to days while improving detection accuracy.