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Every capable drone produces streams that matter: video, telemetry, control, RF behavior, and mission context.
Today those streams are fragmented across proprietary links, SDKs, goggles, remotes, companion computers, flight controllers, and one-off integrations.
CoyoteSec’s avionics server normalizes those streams and brings them into NVIDIA edge AI.
Not a drone. Not a converter. The layer between.
A converter passes a picture through.
An avionics server makes the drone computable.
That is the difference between a feed and a platform. A feed can be cleaned up once. A platform can support denoise, tracking, telemetry overlays, AR, autonomy workflows, trusted video, airspace awareness, and future adapters on the same architecture.
How it works.
Ingest.
Accept drone-native video, telemetry, control, and signal context from FPV systems, SDK-supported aircraft, MAVLink-class systems, OpenIPC-style video paths, and trusted American airframes.
Normalize.
Each drone family becomes an adapter behind a common capability layer. Software sees a stable contract instead of a new integration project every time the airframe changes.
Accelerate.
Once signals reach NVIDIA compute, CUDA and the Jetson video stack can transform them in real time: denoise, color, tracking, overlays, AR, inference, encoding, recording, and secure transport.
Expose.
Video, telemetry, control state, and events become usable streams for production tools, AI models, operator dashboards, cloud systems, and trusted workflows.
Secure.
The system is designed read-side first, with policy-gated controls, authenticated access, auditable operations, and a compliance roadmap for buyers who need to trust what they fly.
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