P2 payment incidentRouted with graph context
96%
Product
Run ITSM on the same AI-native layer as infrastructure and cloud operations
Unify incidents, requests, changes, knowledge, SLAs, infrastructure context, cloud signals, and governed autonomous agents on one Enterprise IT platform.
AI-Native IT Service 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.
Incidents
Classify, enrich, route, and prioritize service issues with full reasoning and escalation controls.
Platform preview
What customers see inside NuralAI.
Responsive product preview for AI-Native IT Service Management. Text stays readable on desktop, tablet, and mobile.NuralAI IT Service ManagementLive workspace | AI context, graph intelligence, workflows, and governance
Service Operations Workspace
One queue for incidents, requests, changes, knowledge, and SLA risk
Password reset requestZero-touch runbook ready
Auto
Emergency changeBlast radius and approval gate attached
Gate
Knowledge gapArticle drafted from resolved incident
Draft
AI context
Graph context and service ownership attached
Policy, approval, and rollback state visible
Approve action
Open audit trail
Ticket deflection trend
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.
Autonomous triage
Classify, enrich, route, and prioritize service issues with full reasoning and escalation controls.
Zero-touch resolution
Execute approved runbooks for common incidents, requests, and access issues.
Change risk intelligence
Score blast radius across the graph CMDB before standard and emergency changes are approved.
Knowledge RAG
Ground every recommendation in runbooks, knowledge articles, and prior incident history.
Service portal
Give employees a governed self-service experience across web, Slack, Teams, email, and mobile.
Audit-ready operations
Record every AI decision, human approval, workflow action, and rollback.
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.
AI-Native IT Service Management overview for enterprise buyers
Built for CIO, IT operations, architecture, and security review.
OpenSee AI-Native IT Service 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