Modern IT landscapes generate more telemetry than humans can process — across infrastructure, applications, services, platforms and networks. Alert noise drowns out real incidents, while reactive firefighting drives Mean Time to Resolution upward. AIOps applies data science, machine learning and agentic AI across three pillars — Monitor, Correlate, Orchestrate — to turn raw operational data into reliable, automated action. The result: faster detection, fewer avoidable incidents, lower operational waste and stronger resilience. evoila provides customised AIOps services and purpose-built agents to advance your operations — whether you are building on Microsoft Azure, Databricks or Broadcom VMware, integrating with your existing toolchain, or accelerating with our established intelligence platform, MEHO.

Modern IT infrastructures have outgrown manual operations. Complex hybrid and multi-platform landscapes generate metrics, events, logs and traces faster than human teams can review them — and critical signals get buried in noise. Traditional monitoring tools push redundant alerts until teams stop trusting them; genuinely critical events get overlooked. Reactive firefighting becomes the default operating mode, driving Mean Time to Resolution upward and increasing the likelihood of costly outages. Compounding the problem, tool silos mean Azure sees Azure, VMware sees VMware, Databricks sees Databricks — but real incidents span multiple platforms, and humans are left manually piecing together the full picture. In regulated and business-critical environments, the stakes are even higher: auditability, incident reporting, third-party risk transparency and continuous resilience testing cannot be achieved through manual operations alone.

Pain Points:

• Data overload — critical signals buried under operational noise
• Alert fatigue — redundant warnings erode trust until real incidents get overlooked
• Reactive firefighting — incidents detected only after user impact, driving MTTR upward
• Tool silos — cross-platform incidents reconstructed manually by humans
• Manual compliance — DORA and NIS2 deadlines unachievable through manual processes

Traditional monitoring has hit a wall. Modern hybrid infrastructures generate metrics, logs, and traces faster than teams can review them, burying critical signals in redundant alerts. When an incident occurs, reactive firefighting becomes the default operating mode. This structural blindness drives Mean Time to Resolution (MTTR) upward and significantly increases the risk of costly, systemic outages.

The root cause is fragmented visibility: Azure sees Azure, VMware sees VMware, and Databricks sees Databricks. Genuinely critical events span multiple platforms, leaving humans to manually piece context together across isolated dashboards. In highly regulated KRITIS, BaFin, or DORA environments, this manual approach completely fails to deliver the required auditability and continuous resilience testing.

• Zero Vendor Lock-In: We adapt entirely to your reality, connecting natively with Azure, Databricks, Broadcom VMware, or legacy ITSM frameworks
• Rigid Execution Guardrails: Every automated remediation loop is strictly fenced by role-based access and compliance-ready safety gates
• Lifecycle Accountability: Every deployment is built to be continuously operated, maintained and improved after go-live, never just handed over and forgotten

Cross-Cutting Capabilities

Across customised AIOps services, purpose-built agents and MEHO-based implementations, we build governance into the architecture from the start. Role-based access control defines who or what may trigger specific actions, while policy guardrails validate AI-supported remediation before changes are executed. Evaluation pipelines continuously measure agent quality, and audit trails document decisions and actions for compliance evidence where required. This allows AIOps to evolve from observability and incident support towards controlled, policy-governed remediation — without losing transparency, accountability or operational control.

Critical Infrastructure — Private AI for Mission-Critical Operations.

For environments where operational data cannot leave the premises, evoila delivered an AIOps setup for Broadcom VCF infrastructure, GPU resources and AI workloads, built on Broadcom VMware Private AI Foundation. AI agents support continuous monitoring, first-level incident response and routine maintenance, while human experts are involved for complex scenarios requiring judgement. The solution was designed for strict data-residency requirements with auditable operational actions.

IT Infrastructure — First-Level Support Automation.

For an IT infrastructure organisation, evoila implemented an automated first-level support agent that handles routine inquiries via chat and email, correlates context from Jira and Confluence, and either resolves issues using known workarounds or escalates with enriched, triage-ready tickets. Built on the Microsoft Agent Framework with code-first Python orchestration, the agent cuts repetitive search overhead, accelerates ticket triage and frees senior staff from constant firefighting.

Container Operations — AI-driven Kubernetes Cluster Operations.

For Kubernetes clusters supporting AI and ML workloads, evoila implemented AI-powered operational capabilities for pod health, resource utilisation and deployment status. AI-based monitoring and first-level incident response reduce manual triage effort, while human operators remain responsible for complex cluster issues and higher-risk decisions. This helps minimise operational toil without removing expert oversight.