01 Data ingestion
Tickets, alerts, metrics, logs, traces, cloud inventory, IAM, CI/CD events, cost records, knowledge, and changes.
AI Architecture
The governed AI architecture behind NuralAI.
NuralAI is built as an enterprise AI control plane: data ingestion, graph context, agent reasoning, policy engine, approval gates, execution connectors, audit/model trace, security boundaries, and deployment governance.
Architecture Layers
From enterprise signals to auditable AI action.
Each layer is inspectable by CIO, CTO, CISO, architecture, service operations, and platform engineering teams.02 Graph/context layer
Services, owners, dependencies, policy scope, business impact, blast radius, risk, and cost context.
03 Agent reasoning
Policy-aware agents generate graph-grounded recommendations with confidence, evidence, and next-best action.
04 Policy engine
Risk tier, identity scope, environment, approval threshold, rollback readiness, and data boundary checks.
05 Approval gates
Human-in-the-loop approval for production, privileged, regulated, high-cost, or low-confidence actions.
06 Execution connectors
ITSM, cloud, observability, identity, CI/CD, collaboration, finance, and evidence systems.
07 Audit/model trace
Prompt, model result, context, policy decision, approver, connector call, outcome, rollback, and value update.
08 Security boundaries
SSO, RBAC, tenant isolation, least privilege, data minimization, secrets handling, and deployment controls.
09 Deployment model
Security review, integration waves, operating model rollout, governance reports, and executive value tracking.
Architecture proof patterns
Control boundaries that make enterprise AI inspectable.
NuralAI exposes how data, context, reasoning, policy, approvals, execution, model trace, RBAC, and audit storage work together before AI acts.Signals are normalized by source, tenant, sensitivity, owner, retention, and allowed use before agent reasoning.
Services, resources, dependencies, tickets, changes, policies, users, cost, and risk form the grounding layer.
Recommendations carry context, confidence, prompt trace, policy inputs, and a human-readable explanation.
Risk tier, identity, environment, business impact, data scope, and rollback readiness decide action eligibility.
Human approval is required for privileged, production, regulated, low-confidence, or high-cost actions.
ITSM, cloud, observability, identity, CI/CD, collaboration, and finance actions run through bounded connectors.
Prompt, context, model result, policy decision, approver, connector call, output, and evidence are linked.
Every action can be exported into security review, compliance evidence, post-incident review, and executive reporting.
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