RF Signal Intelligence
Deep learning that adapts to your hardware — high-accuracy signal classification across contested RF environments.
Neural Architecture
What We Do
01
Identifies signal types, modulation schemes, and waveform characteristics from raw IQ data — across a wide SNR range, from near-noise-floor to clean channel. No proprietary datasets. No hardware-specific training data required.
02
Models learn the RF signature of your deployment environment — hardware impairments, noise floor, channel conditions — and self-optimize without manual recalibration. Swap front-ends without retraining.
03
Current models run under 250K parameters — enough to operate on constrained embedded platforms without a GPU. The same design principles scale from tactical edge nodes to cloud inference pipelines.
Lab Infrastructure
Models train on synthetic IQ data, then adapt in-situ to the RF characteristics of whatever front-end they're deployed on — learning hardware-specific noise profiles, gain curves, and frequency response without manual recalibration.
The result: a classifier that gets sharper the longer it runs on your hardware, not one that needs to be retrained every time you swap a front-end.
Contact
Whether you're a defense researcher, an industry partner, or exploring RF intelligence for your program — reach out and we'll get back to you within 48 hours.