Live service topology
Continuously maps applications, infrastructure, cloud resources, APIs, queues, data stores, owners, and business services into one operational graph.
Graph Intelligence
A living map of services, dependencies, and business impact.
Replace stale CMDB rows with a graph-native intelligence layer that gives AI agents the context to reason before they act.
Graph CMDB
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
Features
How NuralAI turns CMDB data into operational intelligence.
The Graph CMDB is the context layer behind NuralAI incident response, change risk, cloud operations, FinOps, compliance, and executive visibility.Signal-to-service correlation
Links alerts, logs, traces, incidents, changes, deployments, cost events, and policy violations to the services and owners they affect.
Dependency and blast radius
Shows upstream and downstream dependencies before a change, remediation, or AI action is approved, including impacted customers and business functions.
AI grounding context
Gives NuralAI agents the service relationships, runbooks, policy controls, knowledge articles, and prior incident history needed to reason safely.
Business impact scoring
Ranks incidents, risks, and remediation plans by service criticality, customer exposure, revenue impact, compliance exposure, and SLA urgency.
Governed graph updates
Keeps graph changes auditable with source lineage, confidence scoring, human approvals, ownership review, and rollback-ready change history.
How it works
From disconnected records to an AI-ready service graph.
Ingest
NuralAI connects to ITSM, observability, cloud, CI/CD, identity, asset, collaboration, and security tools.
Normalize
Events and records are deduplicated, typed, enriched, and mapped to NuralAI entities such as services, assets, owners, controls, and workflows.
Relate
The platform builds dependency edges across services, infrastructure, changes, incidents, policies, and business capabilities.
Reason
AI agents use the graph to identify root cause, understand blast radius, score confidence, and generate a governed action plan.
Act
Approved workflows update tickets, trigger runbooks, notify owners, document evidence, and improve the graph with outcome feedback.
NuralAI in practice
The graph is used every time NuralAI decides what to do next.
When a signal arrives, NuralAI does not treat it as an isolated alert or ticket. The platform checks the service map, recent changes, ownership, policy boundaries, customer impact, runbook history, and approval rules before recommending or executing action.
Watch Graph DemoRoot cause
Cluster noisy alerts into one incident and identify the most likely failing service, dependency, or recent change.
Change risk
Predict blast radius before approval so teams understand which services, customers, and controls may be affected.
Remediation plan
Generate a policy-aligned action plan with rollback, owner notification, evidence capture, and approval gates.
Resources
Evaluation materials for Graph CMDB implementation.
Use these resources to plan connectors, data quality, ownership, governance, and rollout paths for NuralAI graph intelligence.Graph CMDB operating model
Review NuralAI graph entities, relationships, data sources, confidence scoring, and governance controls.
OpenIncident impact analysis with graph context
See NuralAI connect an alert to affected services, owners, recent changes, and the safest remediation path.
OpenConnector and data readiness checklist
Prepare ITSM, observability, cloud, CI/CD, identity, and asset data sources for graph ingestion.
OpenAI grounding and graph governance
Understand how NuralAI agents use graph context while preserving approvals, audit trails, and policy controls.
OpenPlatform 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