Every ticket, investigated before a human sees it.

Our AI agent reads your issue, cross-references your endpoint inventory, investigates proactively and prepares a resolution — 24 hours a day.

Fortis Nomad AI operations

How it works

This is not a chatbot deflecting your support requests. It is an AI agent that does the work — with a human engineer in charge of every outcome.

Step 1

You email the issue

Problem becomes a ticket automatically. No portals, no phone queues, no forms.

Step 2

AI investigates

The agent reads the ticket, identifies the affected endpoint, and queries its live inventory — OS, patch status, agent health, recent changes.

Step 3

Proactive resolution

The agent checks logs, correlated alerts and known issues, then either resolves with documentation or prepares a full diagnosis for our engineer.

Step 4

Human in charge

Every outcome is approved by a qualified engineer. You receive a clear explanation, not a wall of automation.

What this means for you

Faster resolution

Investigation starts the moment you email — even at 2am. The diagnosis is ready when the engineer picks up the ticket.

Proactive, not reactive

The agent continuously monitors patch status, agent health and licence expiry — and flags drift before it becomes an incident.

Transparent

Every ticket carries a history of what the AI found and what the engineer did. Full audit trail, no mystery.

Affordable

AI carries the routine workload. You get the coverage of a large IT team at a fraction of the overhead.

Case study: AI-driven patch drift detection

Endpoint patching & proactive investigation

Catching a failed patch rollout before it became an incident

During a routine overnight patching cycle, our AI agent detected that a batch of endpoints had failed to apply a critical security update — the patch engine had silently returned success while the endpoint's actual patch status showed otherwise. Within hours, the agent flagged the discrepancy, cross-referenced the affected machines and re-queued the update.

The outcome: a 100% patch completion rate was verified by morning — and the drift pattern was added to the monitoring rules so it can never happen again silently.

Without continuous verification, that patch failure would have been discovered weeks later — possibly during an incident, or worse, by an attacker.