Even with alerts and dashboards, finding a root cause still depends on human context. Engineers waste hours stitching logs together while knowledge evaporates between incidents. Organizations know what broke — but not why or how to prevent it next time.
Context is fragmented across teams, tools, and time.
RCA quality varies by who’s on call, slowing recovery.
Learnings rarely feed back into monitoring or prevention loops.
Dstl8 connects detection, insight, and remediation into a continuous loop. Edge Distillation delivers a clean signal, Operational Inference maps dependencies and event relationships, and Möbius agents continuously surface incidents and build explainers with recommended actions. Every incident becomes a learning event that sharpens future response.
Continuously identifies incidents and manages their lifecycle, with related events to pinpoint cause and impact.
Generates plain-language incident explanation for faster understanding.
Suggests diagnostic or rollback actions drawn from historical outcomes.
Learns from each incident to improve future detection and RCA precision.
Dstl8 brings speed, consistency, and retained knowledge to incident response. It turns observability from a reactive dashboard exercise into a self-improving feedback loop.
Shorter MTTR: Move from alert to explanation in record time.
Higher RCA accuracy: AI cross-validates causes across services.
Persistent learning: Every incident improves the next.
Operational confidence: Teams act faster, backed by shared context.
