noether reconstructed the causal graph across the last 90 minutes.
The minimal intervention set to restore SLO:
• +1 replica to ingest-proxy
• 12% traffic shift away from eu-west-3
• rollback auth-service@2413
4.2M events/min across traces / logs / metrics.
Anomalies compressed into 41 latent regimes.
1,027 past incidents distilled into verifier-gated policies.
Counterfactuals stored as structural equations, not text runbooks.
448 edges · 4 learned confounders · 93% of incident attributions explained.
// CRITICAL FAILURE ANALYSIS:
THE REAL BOTTLENECK ISN'T
OBSERVABILITY.
IT'S THE LACK OF VERIFIABLE EXPERTISE.
Reliability engineering has long relied on dashboards, alerts, and operator instinct. Modern AI exposes the gap: it reaches everywhere but fails unpredictably on noisy alerts, partial failures, ambiguous telemetry, and cascading service graphs.
Noether is a forward-deployed reliability intelligence layer that lives inside your infrastructure.
It ingests telemetry, traces, and incident history, reconstructs deterministic replays of failures, and converts operational experience into verifier-scored policies you can test and iterate on without touching production. That deterministic execution substrate is the missing ground truth for AI-native systems: it forces decisions to be measurable, auditable, and safe.
Reliability and capability rise together here. Every advance in our causal-replay engine hardens the safety of the agents running on it; every jump in capability exposes a new failure mode for us to instrument and solve. We push the system forward at full speed while keeping its predictability ahead of its complexity.
We operate like an experiment lab with a product deadline: we theorize, hypothesize, and ship continuously. We want people who take permissionless initiative, who design their own experiments, who don't wait for structure to be handed to them.
To apply, send an email to crew@noether.one with a note on the hardest systems problem you've solved.