Systems
GANNDALF
Full-featured RF MLOps for intelligence and defense. Rapidly train new signal detectors. Identify and track threat signals. Enterprise capable. Edge ready.
The problem
From RF data to deployed models without the complexity
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.
The GANNDALF advantage
Tools that bridge RF operations and machine learning
Purpose-built tools that bridge the gap between RF operations and machine learning deployment.
Accessible to all operators
Designed around familiar RF concepts and workflows. Train neural networks through point-and-click interfaces, guided workflows, and automated parameter optimization—no programming required.
Intelligent workflow automation
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.
Rapid model iteration
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.
The toolchain
Forge trains it. Runner deploys it.
Seamless integration from data collection to operational deployment.
Forge — the development loop
- RF captures
- Data curation & dataset management
- Model training
- Trained model management
- Model performance evaluation
Evaluation feeds back into curation. The loop is the workflow — a new signal type means another turn through it, not a new project.
Runner — deployment targets
- Edge processors
- Embedded GPUs
- FPGA platforms
RFML Forge
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.
RFML Runner
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.
Professional tools
Built for real-world RF challenges
A comprehensive interface that makes complex RF machine learning tasks manageable and repeatable.
Centralized dataset management
Organize and track RF collections with metadata tagging, search capabilities, and version control for reproducible model training.
Signal analysis tools
Examine time-domain, frequency-domain, and statistical features to understand signal characteristics and validate model decisions.
Interactive data exploration
Visualize complex signal environments including frequency hoppers, burst transmissions, and dense multi-signal scenarios.
Efficient label generation
Accelerate dataset preparation with ML-assisted labeling that learns from your corrections, reducing annotation time by orders of magnitude.
Deploy GANNDALF
Accelerate your RF machine learning
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.