AI for operations

Operations built for the AI era.

The AI-powered operations for enterprises that can't be down. Cross-tool correlation above your existing observability stack — Dynatrace, ELK, SolarWinds, OEM, CMDB, Jira. Topology-aware RCA. Predict before impact. Self-heal what you authorize.

70–85%
Alert noise reduction
−50–70%
MTTR improvement
40–60%
Repetitive tasks automated
Noise collapse Predictive capacity Cross-stack RCA
prod / payments / incident-882 LIVE
RAW ALERTS · LAST 60S · 4,127 TOTAL+ 5 incoming
14:02:18Dynatracepayments-apiP1latency p99 2.1s
14:02:17ELKapi-gatewayP25xx rate 0.4%
14:02:15CMDB·Jiradb-clusterP1conn pool exhausted
14:02:14Dynatraceredis-cacheP2evictions spike +840%
14:02:11SolarWindspayments-apiP1CPU 94%
14:02:08ELKpayments-apiP2timeout count +12x
14:02:04Dynatraceredis-cacheP2memory 92% utilized
—— CORRELATED · 1 ACTIONABLE INCIDENT ——
INCIDENT 882 · P1 · PAYMENTS-API · ROOT CAUSE INFERRED
Redis cache evictions → db connection pool exhausted → payments-api latency spike. Symptom, not cause.
Recommended action · raise redis-cache memory: 3GB → 6GBAWAITING APPROVAL
SONNET· CORRELATED · 1 INCIDENT· 4,127 INGEST5168.1S
↺ replay
IDENTITY
aiops:correlator · payments
scoped runbook · prod tier
NOISE COLLAPSE
4,127 1
99.97% reduction · actionable
RCA CONFIDENCE
94/100
cross-stack · 3 sources
POLICY · AUTO-REMEDIATION
requires human approval
prod tier · scope: payments-svc
AUDIT LOG · JUST NOW
10:50:04 aiops:correlator incident-882 · root cause inferred · action queued
Who moring AI Ops is for

Enterprises that can't be down.

Mission-critical workloads — government, border control, payments, aviation, financial transactions, multi-site datacenter operations. Hybrid cloud and on-prem. Active-Active and Active-Passive architectures. 24×7 operations with regulatory teeth.

Pain 01 · Alert noise

Thousands of alerts daily.

Multiple monitoring tools, no useful correlation. Duplicate and cascading alerts, false positives, alert fatigue. The signal-to-noise ratio is killing your operations team.

Pain 02 · Slow RCA

Root cause takes hours and three teams.

Correlation across observability silos is manual. Dependency mapping is in someone's head. Historical patterns aren't searchable. Every P1 is a war room.

Pain 03 · Reactive ops

You learn it broke from the customer.

Capacity issues hit before forecasts. Degradation patterns aren't predicted. Anomalies surface as outages. Every postmortem ends with "we should have caught this earlier."

Seven priority use cases

What we ship inside your operations.

Each use case is scoped to your stack, your monitoring tools, your CMDB, your ITSM, your business services. The platform is the same; the deployment is bespoke.

01 · Event correlation

Single source of operational truth.

Ingest from Dynatrace, ELK, SolarWinds, OEM, CMDB. Suppress duplicates. Cluster related events. Dependency-aware alerting.

−70 to −85% alert noise
02 · AI-driven RCA

Topology-aware, historically grounded.

Historical incident correlation. Log analytics. Topology-aware RCA across the stack. Infrastructure dependency mapping. AI-generated remediation.

−50 to −70% MTTR
03 · Predictive operations

Catch it before impact.

Capacity forecasting across compute, storage, network. Predictive anomaly detection. Service degradation forecasting. CPU and memory trend prediction.

Catch incidents 7–60 min early
04 · Automated remediation

Self-heal known scenarios.

Intelligent automation workflows. Automated service restart, stuck-process clearing, middleware restart, disk-threshold auto-clear. ITSM & change integration.

40–60% of tasks automated
05 · Business service impact

See impact as it propagates.

Business service topology mapping. Real-time dependency visualization. Transaction-level observability. Know which service is bleeding before the exec asks.

Customer impact in minutes
06 · Vulnerability management

Patch what actually matters.

Vulnerability clustering. Patch prioritization by exploitability and criticality. Risk scoring against business service mapping. AI-based remediation.

Meaningful remediation cycles
07 · Knowledge ops assistant

Your runbooks, conversational.

RAG-based knowledge engine over your operational corpus. Conversational troubleshooting assistant. Natural-language operational queries. Faster onboarding.

SME load reduced 30–50%
Integrates with your existing stack

A layer above what you already operate.

Not a rip-and-replace. moring AI Ops composes with the observability, ITSM, and platform tooling you've already invested in. Open APIs in every direction. No vendor lock-in on the layers underneath.

DynatraceTelemetry, traces, performance signals
ELKLog analytics, search, dashboards
SolarWindsNetwork & infrastructure monitoring
Oracle EMEnterprise Manager · DB ops
CMDBConfiguration & asset reference
Jira / ITSMIncident, change, problem management
OpenShift / K8sContainer platform observability
Kafka · MQStreaming, queuing, middleware health
Oracle · PostgresDatabase telemetry & query plans
Redis · CassandraCache & NoSQL operational signals
CI/CDDeploy correlation with incident windows
Custom APIsAnything else you operate, via open API
The maturity path

Five phases. You move at the pace your governance allows.

Not every enterprise jumps to autonomous operations on day one. moring AI Ops grows with your governance maturity, starting where the immediate ROI is.

01
Months 0–3

Observability + event correlation.

Cross-tool ingestion live. Alert noise collapsed. Single operational view of truth. Quick win that pays for the next phase.

02
Months 3–6

AI-assisted RCA + predictive analytics.

Topology-aware RCA online. Capacity forecasting against your business services. SME load starts to drop. Operations moves out of war-room mode.

03
Months 6–12

Automated remediation + self-healing.

Known scenarios self-heal. Predefined runbooks execute under human approval. Mean time to resolve drops dramatically. The cost curve bends.

04
Months 12–24

Agentic AI operations platform.

Operational agents act on intent. Multi-step workflows execute autonomously under policy. Human-in-the-loop remains for novel situations.

05
Year 2+

Semi-autonomous enterprise operations.

Your operations org runs at multiples of its prior scale. The substrate has compounded. SMEs work on novel problems, not repetition.

Enterprise expectations

Built for the architecture review.

Mission-critical environments have non-negotiable requirements. moring AI Ops meets them as table stakes, not roadmap items.

  • Hybrid deployment — cloud, on-prem, multi-site, Active-Active and Active-Passive.
  • RBAC + Zero Trust alignment — your identity provider, your policy enforcement.
  • Audit logging & encryption — every action recorded, every key under your control.
  • Explainable AI — every RCA, prediction, and remediation comes with its reasoning chain.
  • Human-in-the-loop governance — nothing self-heals beyond the thresholds you set.
  • Custom model extensibility + open APIs to CMDB, ITSM, and automation platforms.
moring mark Book your workshop

From thousands of alerts to twelve actionable incidents.

A 30-minute AI Discovery workshop gets you a baseline metric from your current observability stack, an architecture sketch against your tooling, and a yes/no on outcome pricing — before anyone signs anything.