For Site Reliability Engineers
Hit your SLOs.
Without burning out.
Automated Service Level Objectives, error budget tracking, and predictive multi-window burn-rate alerts: computed directly from your production traces and telemetry.
Zero-overhead kernel probes for TCP, HTTP/2, and DNS latency without application restarts.
Automatic span injection with W3C baggage propagation across polyglot microservice runtimes.
Tracks token consumption, prompt drift, model invocation latency, and tool execution trees.
Single ClickHouse backend for logs, traces, metrics, and profiles with zero storage silos.
Indexes span IDs directly into log metadata, eliminating manual grep during production outages.
Continuous IQR & seasonal forecasting to alert on statistical anomalies before customers report errors.
Correlates telemetry with git commit history and engineering runbooks to draft instant post-mortems.
Routes rich incident cards with trace IDs, flamegraphs, and error frequencies directly into Slack and PagerDuty.
Human-confirmed operator actions: traffic shedding, canister isolation, and canary rollbacks.
Problem & Solution
The challenges every site reliability engineers faces
And how aiAxonIQ solves them with unified telemetry.
SLO Blindness & Static Thresholds
Service Level Objectives defined in static spreadsheets or forgotten wiki pages with zero real-time visibility into error budget consumption, leaving teams blindsided when monthly SLAs are breached.
Real-time multi-window, multi-burn-rate SLO alerting computed directly from production traces and Prometheus metrics. Receive proactive notifications when 2% or 5% of error budgets burn before customer impact occurs.
Explore SLOs & Error BudgetsChaotic Post-Mortem Reconstruction
Reconstructing the exact sequence of events during high-severity incidents requires days of combing through disjointed Slack channels, Grafana screenshots, and ephemeral console logs.
Automated AI incident summaries assemble complete timeline reconstructions, correlating latency spikes, deployment rollouts, and anomaly clusters into an exportable root-cause dossier.
Explore AI InsightsUnsustainable On-Call Load
Repeated non-actionable pages and flaky threshold alerts trigger severe on-call fatigue, causing critical production degradations to be ignored or delayed in triage.
Z-score anomaly detection, rolling baseline silences, and flap damping cut false alarms by up to 85%, ensuring that every page represents a genuine threat to service reliability.
Explore AlertingExecution Flow
How it works in practice
Your improved workflow from ingestion to resolution, step by step.
Unified Architecture
Platform modules used
Everything site reliability engineers need, in one place.
Why it works
Reliability Engineering You Can Quantify
Modern distributed architectures cannot rely on simplistic uptime pings. aiAxonIQ continuously computes availability, latency percentiles (p50, p95, p99), and custom query SLIs over 7, 14, 28, and 90-day rolling evaluation periods. Our AI engine can even draft recommended SLO targets based on historical baseline telemetry, bridging the gap between business agreements and technical metrics.