Every sensor reading. Every guidance decision. Every engine command. Observable, queryable, and auditable. Finally answer the question everyone asks after an anomaly: "Why did the booster do that?"
Every test flight generates terabytes of telemetry. But when something goes wrong, finding the root cause means sifting through millions of unstructured data points.
After every anomaly, regulators demand a root cause analysis. Without structured event data, teams spend weeks reconstructing what happened — delaying the next launch by months.
The advantage in aerospace is iteration speed. But you can't iterate on failures you can't query. "Show me all engine shutdowns where chamber pressure dropped before the command" should take seconds, not days.
Was this anomaly a one-off, or has it happened before? Without semantic event IDs, comparing across flights requires manual data archaeology.
Every spacecraft decision becomes a structured, queryable event.
The same domain.entity.action:version pattern that works for software telemetry
works for rocket telemetry.
Total time to reconstruct this sequence with EventIDs: 47 seconds
Previous method (raw telemetry analysis): 3 weeks
Generate anomaly reports in hours, not weeks. Every event is queryable, every decision is traceable. Get back to launching faster.
"Show me all flights where this condition occurred." Query across the entire flight history to validate fixes before the next launch.
Find the signature that precedes engine failures. Train models on structured event sequences, not raw telemetry noise.
Stream events to mission control dashboards. Trigger automated responses when anomaly patterns are detected mid-flight.
But when you fail, you need to know why. The Event Model turns every failure into a queryable lesson.
That's how you iterate from explosion to orbit to catch.
The same Event Model that makes AI code observable can make any complex system auditable. Rockets. Vehicles. Satellites. If it makes decisions, it should emit events.