Works through the tools you already run
Infrastructure changed four times. Operating it didn't.
Data centres, then VMware, then cloud, then Kubernetes. Every generation added an API. None changed the work. A request is still executed by hand across three or four consoles, and an incident is still a room full of people rebuilding context from logs and memory.
Annual cost of downtime for the Global 2000, up roughly 50% in two years. Source: Splunk / Oxford Economics
You already have monitoring, ITSM and automation. The gap is between them.
The alert lives in one system, the ticket in another, topology and logs somewhere else, changes in Git, runbooks in a wiki, and the engineer who actually understands the environment somewhere else again. Each holds part of the truth. None can act.
One governed loop for everything that comes in.
Understand
Is this a request, an incident or a predicted failure? Which environment, what is missing, what risk class.
Plan
A plan built from your topology, recent changes, runbooks and policies. Not a generic playbook.
Approve
The human gate. Who has to say yes depends on the risk class. Autonomy is off by default.
Execute
Only versioned, tested workflows touch production. No free-form commands.
Learn
The outcome is written back to the ticket, the CMDB and the environment model, so the next one is faster.
Three engines. One control plane.
Deterministic engine
For service requests: provision, change, access. Classified, validated and executed the same way every time. Zero-touch for repeatable requests.
Non-deterministic engine
For incidents: gathers logs, metrics, traces and changes, builds an evidence timeline, ranks likely causes and proposes a remediation with its blast radius and rollback path.
Predictive engine
For what hasn't happened yet: forecasts likely failures from your signals and history and hands them to the other two engines to act before impact.
Requests, incidents and predicted failures, before and after.
- Waits in a queue
- An engineer chases missing inputs
- Three or four consoles
- Done by hand
- Ticket updated days later
Classified and inputs validated Workflow chosen, policy checked, plan produced Approved in minutes Executed and verified Ticket, CMDB and audit updated automatically
Zero-touch for repeatable requests.
- Alert storm and a bridge call
- Five teams rebuild context
- Tribal knowledge decides what to try
- The fix goes in under pressure with no rollback
- The RCA is written days later from memory
Service and criticality identified Evidence gathered across logs, metrics, traces and changes Likely causes ranked Remediation proposed with risk and rollback A human approves and the workflow runs
Minutes to context. Governed remediation.
- Warning signs sit unread in dashboards
- Nothing happens until a threshold breaks
- The first signal is a P1 or a customer
- The fix lands at peak impact
- The post-mortem asks why the trend was missed
A likely failure is flagged early Investigated before impact Pre-emptive remediation proposed with risk and rollback Approved and executed The ticket records an incident that never happened
Handled before it becomes an incident.
Generic AI knows Kubernetes. AegisOps knows your Kubernetes.
Built from read-only integrations with your ticketing, monitoring, cloud, Kubernetes and VMware platforms, and refreshed by use rather than by a consulting project. It knows which change preceded which incident and which fix worked last time, and it gets sharper with every ticket it handles.
Your environment is isolated. Only code patterns are shared across customers, never your data.
Zero unapproved changes. That is the architecture, not a setting.
Approvals in ServiceNow, Slack or Teams for every trigger: ticket, alert or prediction.
Destructive actions are blocked by policy; every block is reported with its reason.
Versioned, tested workflows; no free-form commands.
Customer-held, outbound-only, limited scope; you can switch it off at any time.
Every plan states its blast radius and carries a rollback path.
Who asked, what ran, who approved, what happened; exportable for auditors.
Read-only start, shadow mode first, rights expand as measured accuracy earns them.
SOC 2 Type I targeted for 2027; compensating controls documented today.
Built for hybrid estates that run on tickets.
VMware-heavy enterprises
Modernising toward cloud and Kubernetes with a material VMware estate and a ServiceNow-class ITSM. Often regulated: financial services, healthcare, manufacturing, retail, telecom.
How AegisOps fits a VMware estateMSPs and SIs
Running operations for many such enterprises. Standardise delivery, cut MTTR and scale without adding headcount, priced per incident or per request.
How MSPs and SIs use itKubernetes platform teams
Digital-native organisations with SRE or platform-engineering ownership who start with incident intelligence and Kubernetes onboarding.
Where platform teams startYou'll recognise yourself if
A recent, visible outage, or a business that cannot tolerate application downtime ServiceNow in place, 500+ tickets a month and 24×7 operations A VMware renewal inside twelve months, or a cloud or Kubernetes migration under way A cost-out mandate, or an MSP contract coming up for renewal