Tier-1 Managed Service Provider Transforms IT Operations with Agentic AIOps

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Customer Snapshot

The Need

The provider operates one of the most complex managed hosting, monitoring and security environments in the industry. As that estate grew, its home-grown integration and correlation engine could no longer keep pace, leaving siloed visibility, swivel-chair operations and mounting alert noise that threatened service quality and SLA compliance.

The cost of that was measurable. Siloed data created blind spots that led to missed outages, alert and incident volumes overwhelmed engineering teams, and high integration costs made onboarding each new customer slow. The inability to aggregate asset visibility across tools created compliance gaps and operational risk at scale.

What the provider needed was a unified AIOps platform that could scale without a custom integration for every new tool or customer, reduce alert noise sharply, accelerate onboarding, and lay a foundation for autonomous operations – all while meeting data governance rules that ruled out sending operational data to external cloud AI services.

The Solution

The Fabrix.ai Agentic AIOps Platform replaced the existing home-grown platform within six months, integrating with 30+ tools spanning alerts, metrics, logs, inventory, topology and ITSM. That covered security tooling (ArcSight, Securonix, LogRhythm, Palo Alto, Fortinet), cloud platforms (AWS, GCP and Azure via CoreStack), infrastructure (VMware vSphere, Kubernetes, Docker, Red Hat), compute and storage (Dell, HPE, NetApp, EMC), backup and DR (Commvault, Zerto, Double-Take), and ServiceNow for ITSM with bidirectional CMDB sync. Ansible was integrated for automated remediation workflows.

On that foundation the platform delivered cross-domain correlation, alert noise reduction exceeding 90%, and full-stack service mapping.

Multi-tenancy and Customer Self-Service

Serving 1,900+ customers made multi-tenancy a core platform requirement rather than a feature. Fabrix.ai's native multi-tenant architecture provides strict data isolation per customer and role-based access control, plus a customer self-service portal that gives end customers direct real-time visibility into their own assets, incidents and service health – without needing an MSP engineer for routine queries.

Agentic AI, Running on the Provider's Own GPUs

Building on the AIOps foundation, the provider expanded into Agentic AI with agents for autonomous incident response and root cause analysis. The distinguishing constraint was sovereignty: the deployment runs entirely on-premises against a 120B-parameter open-source model served by Ollama on dedicated NVIDIA GPU hardware, with no reliance on external cloud AI services.

The inference stack is an HPE physical server with 128 CPUs, 512 GB RAM and four NVIDIA L40S GPUs totalling 180 GB of GPU memory, fronted by Nginx over HTTPS. All AI execution stays inside the customer's own infrastructure.

Around that, role-based AI Personas let executives, operations engineers and customer teams interact with AI through experiences matched to their context. AI Projects give teams and departments scoped AI workspaces with their own data, agents and objectives. AI Observability provides cost insights, budget tracking and spend controls across AI workloads, keeping LLM consumption governed. Dynamic tooling lets agents work directly with third-party ITOM tools without a custom integration per tool, and a library of 1,900+ pre-built bots keeps onboarding of new tools, customers and workflows fast.

Agents and Use Cases Delivered

Results

More than $1M in operational savings, and 100% SLA compliance.

Data Sources and Integrations