Where it breaks today
Alert noise drowns out real signals. No correlation between infrastructure layers. IT teams find out about incidents when users complain. $5,600 per minute of downtime. And you're reactive.
The fix
We correlate signals, detect early warnings, and trigger response before users notice.
What we build
An agent is a stack.
Signal correlation
MELT data (Metrics, Events, Logs, Traces) ingested and correlated across infrastructure layers.
- MELT data ingestion
- Cross-layer signal correlation
- Early warning detection
- Alert noise reduction
Automated response
War rooms auto-created. SRE notified. Status pages updated. Incident response starts before escalation.
- Auto-created war rooms
- SRE notification
- Status page updates
- 40-60% MTTR reduction
How it works
Ingest. Correlate. Prevent.
Connect monitoring
Integrate with Splunk, Datadog, New Relic, PagerDuty.
AI correlates signals
Cross-layer analysis. Early warnings surfaced.
Response triggered
War room created, SRE notified, status updated - before escalation.
What it delivers
Outcomes, in weeks.
Before → After
Detection: After user complaints
Early warning before impact
Before → After
MTTR: Hours
40-60% reduction
Before → After
Alert noise: Overwhelming
Prioritized actionable alerts
Industry benchmark
PagerDuty AIOps customers report 87% fewer incidents, up to 91% alert noise reduction, and 14-70% faster MTTR. One customer reduced network failure resolution from 40 minutes to 2 minutes.
No lock-in
Built in your stack.
Live in 6-8 weeks. You own the system — sovereign, no vendor lock-in.
- Monitoring & Observability (Splunk, Datadog, New Relic, etc.)
- Service Management (ServiceNow, Jira, or your platform)
- Team Communication (Slack, Teams, or your tool)
- Incident Management (PagerDuty, Opsgenie, or your system)
- Runtime & Orchestration (Trinity by Ability AI)
Questions
Questions about incident prediction
What signals does it correlate?
MELT data (Metrics, Events, Logs, Traces) from Splunk, Datadog, New Relic, and application logs. Cross-layer correlation identifies patterns that predict outages.
How does it prevent false alarms?
ML models trained on historical incident data learn what signal combinations predict real incidents vs. noise. Precision improves over time as it learns your environment.
What results should we expect?
IT teams typically reduce MTTR by 40-60% (hours → minutes for detection) and prevent 20-30% of incidents from escalating. For companies with $1M+/hour downtime cost, preventing even 2-3 incidents/year = $5M-$10M impact. Plus SRE capacity reclaimed from firefighting: 15-20 hours/week = $80K-$120K/year.
How long does implementation take?
6-8 weeks from kickoff to production. Week 1-3: MELT data integration. Week 4-5: ML model training on historical incidents. Week 6-8: Alert configuration and war room automation.
Do we own the system?
Yes. You own the system. We build the infrastructure in your stack, hand over the keys, and you own it forever - no vendor lock-in.
Start here
Bring one workflow.
A 30-minute working call. We’ll map this workflow to an agent stack and tell you honestly whether it’s worth building.
Incident prediction (AIOps)
40-60% MTTR reduction
Correlate signals across infrastructure. Detect incidents before they escalate into outages.