Alert storm138 alerts clustered into one customer-impacting incident
Noise -82%
Product
Predict, prevent, and remediate operational disruption
Correlate alerts, logs, topology, services, and dependencies into a live operational graph that AI agents can reason over and act on.
IT Operations Management
Governed AI active
Incident timeline
Signal ingested from observability stack
Graph impact: payments API, 4 services
AI plan requires approval: restart pool
Runbook executed with rollback ready
Agent confidence
96policy aligned
Graph CMDB
Actions
Approve runbook
Open audit trail
How it works
Built on NuralAI's shared AI, graph, workflow, and governance layer.
Every product shares the same operational graph, policy controls, integrations, AI agents, and audit model. That keeps context consistent from detection to resolution.
Shared NuralAI layer
AI + Graph + Workflow + Governance
Every workflow runs through the same governed operating model.
Signals, context, policy, approvals, automation, and evidence stay connected from intake to outcome.
Topology
Auto-discover services, infrastructure, cloud resources, dependencies, and ownership.
Platform preview
What customers see inside NuralAI.
Responsive product preview for IT Operations Management. Text stays readable on desktop, tablet, and mobile.NuralAI IT Operations ManagementLive workspace | AI context, graph intelligence, workflows, and governance
Operations Graph
Topology, event correlation, service health, and root cause in one view
Root causeConfig change mapped to degraded checkout service
Likely
Service mapAPI gateway, DB pool, and queue lag dependency path
Mapped
RunbookRestart worker group with rollback attached
Ready
AI context
Graph context and service ownership attached
Policy, approval, and rollback state visible
Approve action
Open audit trail
Service health forecast
Trend and governed resolution over time
Governed workflow loop
Each action links to graph context, policy checks, owner approval, evidence, and business impact.
Capabilities
Enterprise-grade depth for real operations.
Live topology
Auto-discover services, infrastructure, cloud resources, dependencies, and ownership.
Event correlation
Reduce alert noise by grouping related signals around affected services and business impact.
Root cause graphing
Trace symptoms to dependency paths, recent changes, failed checks, and degraded services.
Predictive health
Score service health continuously and surface failure risks before they cascade.
Runbook execution
Convert detection into governed remediation with approvals, rollbacks, and audit trails.
Operations workspace
Give NOC, SRE, and service owners one shared view of incident context.
Customer outcomes
Proof buyers can inspect, defend, and share.
Package customer evidence by industry, workflow, stakeholder, and measurable business outcome so every evaluator sees the proof that matters to them.Financial services
Cloud waste remediation across regulated multi-cloud estates.
$2.3Menvironment-based ROI modelExplore use caseHealthcare
AI-assisted incident response for clinical system availability.
65%MTTR improvementExplore use caseManufacturing
Predictive service health across plant and enterprise systems.
78%downtime reductionExplore use caseResources
Guidance for evaluation and implementation.
IT Operations Management overview for enterprise buyers
Built for CIO, IT operations, architecture, and security review.
OpenSee IT Operations Management in action
Built for CIO, IT operations, architecture, and security review.
OpenImplementation and migration checklist
Built for CIO, IT operations, architecture, and security review.
OpenModel savings and payback
Built for CIO, IT operations, architecture, and security review.
OpenFAQ
Common evaluation questions.
How is NuralAI different from legacy ITSM?
NuralAI is built around AI agents, graph context, evidence-backed governed autonomous action, and governance rather than manual ticket queues alone.
Can NuralAI work with existing tools?
Yes. The platform story should emphasize connectors and workflow coexistence before full migration.
How are AI actions governed?
Agents operate within permissions, policy checks, confidence thresholds, approvals, and immutable audit trails.
How should proof be handled?
Publish only verified metrics, approved enterprise use cases, and documented security or compliance claims.
Platform AI Control Plane
The platform exposes how NuralAI senses, reasons, governs, acts, and proves.
NuralAI combines data ingestion, graph context, agent reasoning, policy engine, approval gates, execution connectors, audit/model trace, security boundaries, and deployment controls.
NuralAI AI RuntimeModel trace active
Tickets, alerts, telemetry, cloud events, IAM, cost, changes, and knowledge are normalized.
DataAgents use graph-grounded recommendations instead of isolated prompt responses.
AgentPolicy gates, approval thresholds, identity scope, and rollback plans control action.
PolicyEvery prompt, model result, human approval, connector call, and outcome is auditable.
Trace