Auto NOC runs network and infrastructure operations end to end. AI Employees ingest every signal, correlate alerts into real events, remediate known issues, and orchestrate on-call for the rest. Operations stops watching dashboards and starts governing the command center that does.
Topology, change history, and signal relationships collapse the alert flood into one real event.
Known issues are remediated end to end before on-call is paged.
Legacy NOC tools flood the wall with alerts and wait for an engineer to sort signal from noise. Auto NOC correlates the flood into real events and remediates the known ones automatically.
Most are noise, all of them become a human problem.
AI Employees ingest every signal and remove duplicate and transient noise automatically.
Correlation depends on brittle rules and manual interpretation.
Related signals are correlated across domains with probable root cause.
Engineers execute them manually under pressure.
Codified runbooks execute end to end under policy.
On-call escalation is manual and fragmented.
On-call is orchestrated with history and stakeholders updated automatically.
Capacity and performance issues are seen too late.
Capacity and performance risks are flagged before they become outages.
Auto NOC treats network operations as one connected cycle. Context is preserved as work moves from observation to correlation, remediation, escalation, and learning.
Metrics, logs, traces, and events enter one operational view.
Signals collapse into events with topology and change context.
Policy-cleared runbooks resolve known issues end to end.
Novel events reach the right responder with full context.
Each resolution deepens the operations graph for the next cycle.
Every component is executed by governed AI Employees against a living operations graph.
The operational view stays unified and topology-aware while duplicate and transient noise is suppressed before it reaches the team.
Ingests metrics, logs, traces, and events across network, cloud, and infrastructure.
Suppresses duplicate, transient, and maintenance-window alerts.
Learns normal behaviour per service and flags meaningful deviation.
Understands alerts in service and network context, not isolation.
Cross-domain signals are assembled into a single event with probable cause and evidence attached.
Correlates related alerts across domains into one event.
Uses topology and change history to isolate the likely cause.
Surfaces emerging patterns that static rules miss.
Attaches topology, recent changes, and prior resolutions.
Response work is executed and orchestrated under policy, with human attention reserved for novel events.
Executes codified runbooks end to end when they clear policy.
Runs communications, bridge coordination, and stakeholder updates.
Escalates with full context to the right responder.
Correlates events to recent changes and coordinates rollback where needed.
Forecasting, policy, exceptions, and operational analytics stay governed and auditable.
Forecasts risk and recommends action before an outage occurs.
Rules are authored in plain language and take effect deterministically.
Novel events arrive in the Auto Workbench with full context and applied policy.
Reports MTTR, event volume, and remediation rate as Auto Insights.
Humans stay on the loop for oversight and enter the loop only for novel events.
Duplicate, transient, and maintenance alerts are suppressed so only real signal reaches the operation.
Metrics, logs, traces, events, and topology stay connected in one operational view.
Related alerts become a single event with probable root cause across domains.
Events are tied to recent changes and rollback is coordinated where the change is the cause.
Known issues are remediated end to end from codified runbooks when they clear policy.
Communications, bridge, stakeholder updates, and escalation context are handled automatically.
Capacity and performance risks are forecast and flagged before they become outages.
MTTR, event volume, and remediation rate are reported as Auto Insights.
Every cycle runs through the four parts of an Auto. Policies set the boundary, AI Employees act, humans resolve exceptions in the Workbench, and each resolution deepens the Auto Graph.
Multi-agentic operations workers ingest, correlate, remediate, and orchestrate across monitoring and infrastructure systems, escalating only novel events.
Correlation rules, remediation runbooks, and escalation criteria are authored in plain language and enforced deterministically before an action executes.
Novel events arrive with topology, change history, and the applied policy. The responder decides once; the resolution trains the next cycle.
Services, topology, events, changes, and prior resolutions become a living per-tenant graph that sharpens correlation and remediation every cycle.
Supervity AI Employees run monitoring, correlation, and remediation across enterprise infrastructure, cutting alert noise and restoring known issues without a page. Operations moves from watching dashboards to governing the command center and handling novel events.
SOC 2 Type 2 and ISO 27001 certified. Sovereign deployment on the customer's own cloud and model contracts.
Auto NOC is deployed against a committed path to AI-first operations. Supervity keeps working at no additional cost until the milestone is reached.
AI-first operations
AI-first operations
AI-first operations
Milestones are scoped per deal during FDE baseline scoping and subject to commercial agreement. The remedy is extended engagement at no additional cost, not a refund.
See the Auto NOC command center run against your own signals and runbooks in an FDE baseline scoping session.