Your NOC runs on
coffee and chaos.
It doesn't have to.
Resolve wires production-grade machine learning into your existing observability stack — replacing 4 AM war rooms with models that predict outages 23 minutes before impact.
DB latency spike in ~18min
Memory leak → 3 downstream alerts
Auto-scaled k8s pods ×3
834 duplicate alerts filtered
The alert storm is real.
The cost is measurable.
Enterprise IT teams waste 38% of engineering hours on false-positive alerts — alerts that fire, get acknowledged, and resolve without human intervention. That's not an operations problem. That's a model problem.
The verdict is
already obvious.
Twelve dimensions. Three approaches. One clear winner — with the data to prove it.
Results that hold up
under scrutiny.
Two engagements. Both anonymized per NDA. Both with data you can take to your board.
Challenge: NOC team receiving 1,200+ daily PagerDuty alerts — 68% false positives. Engineers averaging 2.4 hours of on-call interruption per night. MTTR of 52 minutes was delaying customer SLA commitments.
"The first week after go-live, our on-call engineer slept through the night for the first time in 18 months."
Challenge: Legacy Splunk + Grafana stack generating 4,800 daily alerts across 6 microservices clusters. Board-level pressure after two P1 incidents in Q3. CTO needed a defensible observability roadmap.
"We went from a board conversation about downtime to a board conversation about competitive advantage."
From chaos to clarity
in 30 days.
A repeatable four-phase process. No black boxes. No 18-month roadmaps. First model in production by day 35.
Discovery
Week 1–2Audit your existing observability stack, alert taxonomy, and incident history. We map signal-to-noise ratios across every integration and identify the top 5 alert categories generating 80% of on-call burden.
Instrumentation
Week 2–3Deploy lightweight telemetry collectors and establish baseline data pipelines. No rip-and-replace — we wire into your existing Datadog, Splunk, or Grafana without disrupting production.
Model Training
Week 3–5Train anomaly detection and correlation models on your historical incident data. Minimum 90-day lookback. Models are validated against held-out incidents before any production exposure.
Feedback Loop
OngoingAutomated model drift monitoring, weekly precision/recall reports, and quarterly retraining cycles. Your on-call team's acknowledgment patterns continuously improve model accuracy.
Your next 4 AM page
doesn't have to happen.
Take the 3-question readiness assessment. We'll benchmark your current stack against 400+ enterprise deployments and show you exactly where your signal-to-noise ratio breaks down.
No sales call required · Results in 24 hours · Benchmarked against your industry cohort