Inspect the evidence.
Measure Epok on your incidents.

The strongest proof we can offer is the product itself. A 5-service example app streams continuous synthetic traffic into a public, read-only tenant. Detection, root cause, and pattern clustering all run on it live — the same build as the trial, not a recorded video.

Keep Datadog, New Relic, or your ClickHouse workflow as the control. Mirror a representative production boundary into Epok and agree on the incident cohort and thresholds before ingest begins. Classify each outcome as correct, incorrect, abstained, or missed; then compare alert fanout, time to verified cause, telemetry coverage, and responder effort.

These are measured figures from our own production over a sample of 40 runs (N=40) — what we actually observed, not a competitive benchmark and not a marketing round number.

Plain-English search (“Ask Epok”) returns in p95 ~2.3 s. Figures change as load and infrastructure change; we update them when we re-measure.

For committed probable-cause statements, Epok links the supporting log lines, spans, or metrics. When the evidence gate is not met, it returns an abstention rather than a verdict. Review both outcomes; do not take the model's fluency as proof.

Detection, root cause, and the investigation surface improve on a steady cadence — driven by real failure modes, not a roadmap deck.

SOC 2 is in progress. Data can stay in the EU (Germany / Finland). Your telemetry is never used to train models, and we publish our subprocessors rather than hiding them. Where we're not done yet, we say so.

The hardest thing for an “AI for ops” tool to do is admit it doesn't know. Epok commits a verdict only at measured confidence — and when the evidence is thin, it says so and tells you what to check next, instead of narrating a confident guess. Confidence is scored against real outcomes, not asserted. Trust at scale is calibration, not omniscience.