kmajid

We prevent discontinuities by using a Continuous Extended Kalman Filter where the physics model serves as the persistent baseline and telemetry acts only as a corrective update. When the buffer leaks, the system doesn't snap to a new position; instead, it continues propagating the state via physics while the uncertainty covariance grows smoothly. When fresh data eventually arrives, we use the innovation delta to gradually steer the state back to reality, ensuring a seamless transition rather than a coordinate jump.

Primary inference runs ground-side (K8s/GovCloud) to aggregate global data for routing. We do see the need for something like a hybrid-edge approach for tactical, sub-5s mitigation. We would target FPGAs (like Xilinx Versal) for production flight hardware to meet strict SWaP and radiation-hardening requirements.

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We're big believers in American Dynamism.

Not right now but we will be soon! Send over your resume to [email protected] if you're interested in joining.

Not currently, we're planning on opening up our seed round in 4 weeks, feel free to shoot us a note at [email protected] if you're interested in learning more.

Spot on. We categorize that <5s window as tactical fade mitigation.

Our current 3-5m window is for topology/routing, but the sub-5s window is for Dynamic Link Margin (DLM). If we can predict fast-fading signatures—like tropospheric scintillation or edge-of-cloud diffraction, we can move from reactive to proactive ACM.

Great point, telemetry reliability is the biggest hurdle for any mission-critical system. We address the "garbage in, garbage out" risk by prioritizing freshness (our pipeline treats latency as a failure).

We use a

"leaky" buffer strategy (if data is too old to be actionable for a 3-minute forecast, we drop it to ensure the models aren't lagging behind the physical reality of the link),

graceful degradation (when telemetry is delayed or broken, the system automatically falls back to physics-only models i.e. orbital propagation and ITU standards), and

edge validation (we validate and normalize data at the ingestion point, if a stream becomes corrupted or "noisy," the system flags that specific sensor as unreliable and adjusts the prediction confidence scores in real-time).