An advisor that learns what normal looks like for your deployment and surfaces drift before it becomes failure — locally, with no cloud AI. When it's unsure, it says so. The restraint is the point.
Every operator has muted an alerting tool that fired too often. Once you stop trusting the alarm, the alarm is worthless — no matter how clever the model behind it.
The advisor is built the other way around: it pauses before it speaks, runs a set of self-checks, and only surfaces a signal it can stand behind. We'd rather miss a marginal blip than train you to ignore it. An advisor you keep listening to beats one that's technically more sensitive and practically muted.
Most tooling shows you state — graphs, current values. The advisor learns the signature of how your system behaves over time, so it can tell when today doesn't look like a normal day.
The methodology runs continuously on a real 3-node mesh — live signals matched against operational history, on real workloads, not simulations. We grade every emission by hand against system logs. This is deliberately the starting line, not a finished number; the honest work is watching it rise as calibration improves. We publish the n so you can weigh it yourself.
The engine's self-test gate is already public and runnable. Install it, run one command, and read every check — you receive the mechanism, open-source, never anyone else's accumulated data.
# the advisor's "I care" self-test gate — Apache-2.0
pip install aura-pce
aura-pce proveRun the gate today, or get a note the moment a hosted advisor is ready to point at your stack.