Your best techs shouldn't beFixingPrinters
From alert to closed ticket.Before your team gets paged.
Five stages. One loop. Every ticket the AI engineer closes runs through all of them.
RMM, monitoring, ticket queue, webhook, or the AI engineer itself noticing something abnormal on an endpoint. It learns what normal looks like in your environment and flags when something drifts.
Runs diagnostic commands against the actual machine. Cross-references what it finds with your ticketing and monitoring data. Keeps branching until it has an answer or knows it's stuck.
Root cause, confidence level, risk class. The AI engineer either acts or asks. It never silently hopes.
Simple fixes run immediately. Complex remediation orchestrates multi-step workflows across systems. Risky actions route to a human approver. One click.
Post-action check confirms the fix worked. If it did, the ticket closes with the full audit trail. If not, the AI engineer escalates with everything it found attached.
This is a real ticket closing itself on a real endpoint.The same loop runs on yours.
What the AI engineer does.
Closing an IT ticket end-to-end takes all five. Miss one and the loop breaks.
Connectors, webhooks, monitoring alerts, PSA tickets, endpoint telemetry, users reaching out directly. The AI engineer also learns what normal looks like in your environment and flags when something drifts.
Governed command execution on every endpoint, plus workflow orchestration for complex multi-step remediation across systems. Your team can define custom procedures. Every action is policy-gated.
Investigates across connected systems: the endpoint, your ticketing system, cloud identity, monitoring. It branches based on what it finds and keeps running until there's a root cause or it knows it needs a human.
Every ticket, every finding, every command, every approval, logged. The audit trail your auditors want to see and your insurers require.
Approval policies, risk detection, confidence gating, human-in-the-loop review. The AI engineer has authority, but the authority is bounded. You set the rules.
Every command. Every finding. Every approval.Logged.
The AI engineer logs what it does as it does it: the commands it ran, the output they returned, how it reached a conclusion, whether it acted on its own or asked for approval, and whether the verification passed.
Auditors, insurers, and compliance teams can reconstruct the full history of any ticket from the logs. Every action is traceable to a policy decision. Nothing runs in the dark.
- ✓Commands and outputs recorded verbatim
- ✓Root cause reasoning documented
- ✓Approval decisions with timestamp and approver
- ✓Post-fix verification results
- ✓Exportable for compliance reviews
Measured by one metric: Autonomy Rate.
The percentage of tickets closed end to end by the AI engineer, per issue class. Not ticket deflection. Not time saved. Tickets actually closed, with an audit trail to prove it.
Issue classes with active playbooks
Each class has dedicated diagnostic scripts and remediation actions running on real endpoints.
New issue classes are added as the AI engineer proves it can handle them reliably on real tickets.
Not a service desk. Not an RMM. Not a chatbot.
The existing AI IT tools do real work. They just stop before the ticket is actually closed. Here's where each of them stops, and what the AI engineer does next.
They categorize queues, suggest replies, summarize threads, route tickets.
When the user says "my printer is offline," a service desk can't log into the machine, check the spooler, or clear the queue. The work inside the ticket still waits for a technician.
GenticFlow logs into the endpoint, runs real commands, identifies the root cause, clears the queue, verifies the fix, and closes the ticket with a full audit trail. If it can't close it, the ticket arrives to your team pre-investigated.
They run the scripts you've already written, on the schedule you set, against the endpoints you configured.
If the issue matches a playbook you wrote, the RMM handles it. Anything new (an unfamiliar error, a combination of symptoms you haven't automated) sits there until a technician picks it up.
GenticFlow investigates from first principles and orchestrates multi-step workflows for complex remediation. It decides what to check, what to run, and whether the fix worked, without needing a playbook written in advance.
They search your knowledge base, surface articles, walk users through self-help flows.
A chatbot can quote the fix from your KB and hope the user tries the steps. It can't test whether they worked, and can't tell whether this specific case is actually what the KB article describes.
GenticFlow queries the actual system. It runs the diagnostic itself, confirms what it sees matches the problem, executes the remediation right there, and checks that the fix took. Some issues never need a ticket filed.
See it in action.
Book a slot and watch it run. Investigation is live on real endpoints today. You see the AI engineer query the machine, read the actual state, and reach a conclusion. Remediation covers printer, Outlook, Windows Update, VPN, disk, slow performance, services, OS crashes, backup and recovery, and more.