Elara-AI Methodology
Vega's forecasting models improve continuously as your telemetry calibrates predictions to your specific system.
What It Does
[NEEDS REVIEW — no existing source material describes Elara-AI as a standalone product beyond the telemetry feedback loop. Suggest adding 3-4 bullets here similar to the forecasting page's "What It Does" section.]
How It Works
Telemetry Feedback Loop
Telemetry data and ground readings feed into a machine learning layer that continuously improves the forecasting models. Your measured Eb/N₀ and noise floor telemetry calibrate Vega's predictions to your specific system—rapidly improving accuracy.
Pattern-of-Life Modeling
Observed behavioral patterns for interfering satellites are grouped by satellite type, operator, and orbital regime—enabling more refined outputs and confidence scoring. The model continuously ingests raw ground samples to further inform the patterns exhibited by all satellites in the field of view at the time of each reading.
[NEEDS REVIEW — additional "How It Works" steps if applicable]
Getting Started
[NEEDS REVIEW — no existing source material describes a standalone onboarding flow for Elara-AI. May mirror the Production Integration section from forecasting, or may need its own flow.]