Stop fighting alerts.
Start resolving incidents.
obseria.io AIOps correlates hundreds of noisy alerts into single actionable incidents, pinpoints root causes in seconds, and suggests remediation steps — so your team spends time fixing problems, not finding them.
Anomaly Detection
ML baselines that learn your system, not your guesses
Traditional static thresholds generate constant noise — too tight and you're paged for every spike, too loose and real incidents slip through. obseria.io trains an adaptive baseline for every metric, service, and environment. It learns seasonality, traffic patterns, and release-driven shifts automatically.
- Time-of-day and day-of-week seasonality
- Automatic re-baselining after deploys
- Multi-signal correlation across metrics, traces & logs
- Configurable sensitivity per service or team
Requests / s — checkout-service
ML baseline ± adaptive band
Incident INC-4821 · Root Cause Analysis
~4 s end-to-endAnomaly detected
checkout-service p99 > 3× baseline
14:03:12
Correlated 47 alerts
Grouped into 1 incident: INC-4821
14:03:14
Root cause identified
DB connection pool exhausted — orders replica
14:03:16
Remediation suggested
Scale replica count or increase pool limit
14:03:17
Playbook linked
orders-db-scaling.md — last used 42 days ago
14:03:17
Root Cause Analysis
From 47 alerts to one answer in under 4 seconds
When an incident fires, obseria.io's AI agent traverses your entire signal graph — correlating traces, log anomalies, metric deviations, and deployment events — and presents a single root-cause narrative with evidence links. No more war-room archaeology.
- Automatic alert grouping into coherent incidents
- Causal chain tracing across services
- Deployment & change-event correlation
- One-click remediation playbook suggestions
MTTR Reduction
Resolve incidents up to 80% faster
By the time your on-call engineer acknowledges the page, obseria.io has already diagnosed the problem, linked the relevant runbook, and suggested a fix. Teams that deploy AIOps typically halve their MTTR within the first month and see it continue to fall as the ML models learn their environment.
Avg. acknowledge time
From 18 min → 2 min
Incidents per month
Down 63% after 90 days
On-call engineer load
68% fewer pages
SLA compliance
99.95% vs 99.7% before
Mean Time to Resolution (hours)
Before vs. after obseria.io AIOps
At 14:03:09, the orders DB replica exhausted its connection pool (limit: 50). Checkout requests queued, pushing p99 from 38 ms → 4.1 s.
Root cause: a slow migration query on orders_2024_q4 held connections open for 40–90 s. 47 downstream alerts were correlated to this single event.
Natural Language Queries
Ask your infrastructure anything, in plain English
No PromQL. No log query DSLs. No dashboards to learn. Just type your question and the obseria.io AI agent searches across your full telemetry graph — traces, metrics, logs, and deployment history — and returns a plain-English answer backed by evidence.
- ›"What caused the checkout spike at 14:03?"
- ›"Which services are most affected by the DB outage?"
- ›"Compare this week's error rate to last week's"
- ›"Show me all services with degraded SLOs today"
Integrations
Fits into your existing workflow
obseria.io AIOps enriches alerts before they reach your incident management tool — not replaces it.
Alert ingestion
Pulls from Prometheus, CloudWatch, Datadog, or native obseria.io rules. Any alert source works.
Correlation engine
Groups related alerts by service topology, time window, and causal dependency graph.
PagerDuty / Opsgenie
Forwards enriched, pre-triaged incidents with root-cause summary and remediation steps attached.
FAQ
Common questions
Ready to cut your MTTR by 80%?
Start your 14-day free trial — full access, no credit card required. Migration assistance included.