The cognitive signal intelligence inside every Yotasys instrument, from agentic AI to radio, radar and optics.
The convergence of Large Language Models (LLMs) and Convolutional Neural Networks (CNNs) is fundamentally transforming radio spectrum analysis from a manual, expertise-dependent process into an intelligent, self-improving system.
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Spectrum analysis and signal identification is challenging because of the range of waveforms that exist in any given frequency band. In addition to the crowded spectrum, the environment tends to be diverse in terms of propagation conditions and non-cooperative interference sources.
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Doppler signals of a radar are used to filter moving objects from complicated background noise. For air surveillance radar, Doppler filtering can be relatively easy because targets move faster than the background noise.
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Effective AI image recognition software not only decodes images, it also has a predictive ability. Software trained to interpret images is smart enough to identify objects, places, people, signals and actions in images or videos.
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Every Yotasys technology runs on the same stack. Precision hardware captures the signal, cognitive processing makes sense of it, and an open interface hands the result to people and machines.
Swiss-engineered RF, radar and optical hardware captures clean, calibrated data for every measurement that follows.
Machine learning classifies, decodes and locates signals and objects in real time, directly on the instrument.
On-board agents plan measurements from a plain-language brief, adapt settings as they go and verify their own results.
A native MCP server, a REST API and standard formats deliver every result into your tools, scripts and AI workflows.
The same technologies solve very different measurement problems. A few of the places they run today.
Find, classify and locate emitters in crowded bands around the clock, without an operator at the screen.
Adaptive radar and vision AI detect and track small, fast objects in cluttered scenes.
Agents run repeatable test plans on spectrum and network analyzers and report verified results.
Mobile robots carry sensing into a site and measure along the way, from RF surveys to visual checks.
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Start from the measurement task. Our engineers map your signals, environment and workflow to the right combination, often more than one technology in a single system.
Yes. They share one platform, so radio, radar and optics data can feed the same agent and the same reports.
No. Classification, decoding and agentic control run on the instrument. A connection is only needed to deliver results where you want them.
Through a native MCP server, a REST API and standard files such as PDF reports, JSON results and raw IQ captures.