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Solution · IT service management

Predict incidents. Before the outage.

MELT data ingested, signals correlated across layers, early warnings detected. War rooms auto-created. Response starts before users notice.

Book a callFor agencies and partners
IT service management · department
Incident prediction (AIOps)Early warning before outages

Delivers

  • Faster time to respond
  • Proactive early warning
  • Alert noise prioritized
  • Automated incident response

Time to live6-8 weeks

Where it breaks

Downtime is expensive. And you're reactive.

Alert noise drowns out real signals. No correlation between infrastructure layers. IT teams find out about incidents when users complain.

The fix

We correlate signals, detect early warnings, and trigger response before users notice.

Incident prediction (AIOps) · Before / after3 changes
  1. Detection: After user complaintsEarly warning before impact
  2. MTTR: HoursShorter, with context
  3. Alert noise: OverwhelmingPrioritized actionable alerts
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.

What we build

An agent is a stack.

2 capabilities working as one, built in your it service management stack.

Incident prediction (AIOps) · capabilities2 parts
Signal correlationMELT 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 responseWar rooms auto-created. SRE notified. Status pages updated. Incident response starts before escalation.
  • Auto-created war rooms
  • SRE notification
  • Status page updates
  • Context attached for responders

How it works

Ingest. Correlate. Prevent.

  1. Step 01Connect monitoringIntegrate with Splunk, Datadog, New Relic, PagerDuty.
  2. Step 02AI correlates signalsCross-layer analysis. Early warnings surfaced.
  3. Step 03Response triggeredWar room created, SRE notified, status updated - before escalation.

Under the hood

Plugs into your stack.

Incident prediction (AIOps) works inside the tools your team already uses, on infrastructure you own. Typically live in 6-8 weeks.

Incident prediction (AIOps) · Integrations5 connected
  • 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)

Time to live6-8 weeks

Questions

Before you ask.

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?

Models are trained on your historical incident data to separate real incidents from noise. Your team tunes the thresholds, and every change is on the record.

What results should we expect?

Earlier warning on real incidents, less alert noise, and SRE time back from firefighting. We baseline MTTR with you before go-live and measure against it.

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.

Next step

Want this in your company?

Start hereBring one workflowA 30-minute working call. We’ll map your it service management workflow to an agent stack and tell you honestly whether it’s worth building.
Book a callFor agencies and partners
The departmentIT service management in TrinityEvery solution, agent and question for this department, in one place.

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