Long-term event collection, retention and offline mission analysis, feeding validated capability back into live operations.
Collected missions replayed, reprocessed and interrogated long after the event, without disturbing operations.
Analyze spectrum allocation and utilization by frequency, technology, protocol, geography and time.
Identify anomalies, emerging activity, unknown signals and other events of interest across large datasets.
Replay collected signals and complete missions for detailed investigation and reprocessing.
Find related signals using spectrogram image similarity, feature matching and AI-assisted search.
Re-model historical data, validate new detection methods and return improved capability to operations.
Customer teams scan, label, validate and deploy new signal libraries without vendor dependency.
Visually annotate and classify new signals of interest directly from the live spectrum display.
Re-train and deploy updated AI models on-premise, in completely air-gapped environments.
An interface that lets analysts, not only elite DSP engineers, actively build threat libraries.
The libraries your teams build stay yours, on your premises, under your control.
Protocol, spectrum image and I/Q stream generation across selectable channel, DSP and medium models, so capability does not wait for a waveform to appear in the field.
Generate variants that are difficult or dangerous to collect under operational conditions.
Build representative datasets to extend detection coverage before an encounter, not after it.
Amplified playback of simulated or recorded streams for EW ranges and training environments.
Controlled live testing, system testing and enemy emulation against known ground truth.
Newly observed activity becomes validated operational capability at the speed of relevance, compounding the advantage with every deployment.
Live operations at the edge: detection, tactical triage and automated reaction.
What the edge could not resolve is routed to research rather than discarded.
RF Studio, signal simulation and model validation in the national research center.
Improved AI models pushed back to the edge as new, customer-owned capability.
It is the question that separates a detection product from a capability you own.