Inspect the evidence.
Measure Epok on your incidents.
No borrowed logos and no opaque accuracy claim. Inspect the live incident, review how committed conclusions map to source evidence, and use the published evaluation method to score Epok against your existing incident workflow.
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.
Open the live demo →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 live-tail figures come from a localhost WebSocket probe on our production deployment on May 30, 2026: 40 runs (n=40), measured from POST to WebSocket receipt. They do not measure browser paint or your network path, and are not a competitive benchmark.
Plain-English search (“Ask Epok”) returns in p95 ~2.3 s (n=8 questions). Figures change as load and infrastructure change; we update them when we re-measure.
Review published limits and backpressure behavior →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.
See the capabilities you can verify →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). We name Anthropic as our AI provider and explain which evidence it receives. Where we're not done yet, including automatic redaction and self-serve AI opt-out, we say so.
Read the full trust & security page →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.
How we measure it — and admit when we're not sure →We'll show customer outcomes when we've earned them.
Until then, inspect the live incident and run a controlled shadow evaluation against an agreed incident and operational scorecard.