Your best techs shouldn't beFixingPrinters
Most issues never need a ticket.Solved in chat. On the endpoint.
Your L1 24/7 AI support technician picks up the chat, runs the diagnostic on the user's endpoint, and applies the fix. The ticket queue stays clear for the work that actually needs a human.
Same issue, two paths. The user just wanted to print.
Sarah Chen (Accounting)- 14:22(4h 32m ago)Sarah submits the ticket through the portalOpen
- 15:05(3h 49m ago)Pulled from the L1 queue43m wait
- 16:18(2h 36m ago)L1 tech: "Hi Sarah, can you confirm which printer?"Waiting on user
- 16:25(2h 29m ago)Sarah replies: "HP-FLOOR-2 on the 2nd floor"
- 17:30(1h 24m ago)Tech remotes in to investigateIn progress1h 5m wait
- 17:48(1h 6m ago)Tech finds spooler stopped, 14 stuck jobs
- 18:02(52m ago)Tech clears the queue and restarts the spooler
- 18:30(24m ago)Tech messages Sarah to confirm it is working
- 18:54(just now)Ticket closed with resolution noteClosed
Spooler service was stopped with 14 jobs queued. Cleared the queue and restarted Print Spooler service (StartType: Automatic). Verified printing with Sarah.
- L1 technician1 person
- Active tech time24m
- Queue + wait time1h 48m
- Back-and-forth touches5
- Fully-loaded cost~$22
- -Spooler service: Stopped
- -Print queue: 14 stuck jobs
- -Spool folder: orphaned files present
- -Drivers: healthy
- Cleared print queue
- Purged orphaned spool files
- Restarted spooler (StartType: Automatic)
Every command. Every finding. Every approval.Logged.
Every command, finding, approval, and outcome is written down as it happens. Auditors, insurers, and compliance teams can reconstruct any ticket from the logs. 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
From alert to closed ticket.Before it reaches your team.
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.
AI engineer impact. 30 days.
Every ticket the AI engineer closes, every chat it deflects, and how the numbers trend on your fleet. Sample below is illustrative.
Sample data: a 1,500-ticket month for a mid-size MSP with the issue classes shown enabled. Your real autonomy and savings depend on fleet size, ticket mix, and which playbooks you switch on. We size the projection during the trial.
What the AI engineer does.
Closing an IT ticket end-to-end takes all five. Miss one and the loop breaks.
Connectors, webhooks, alerts, tickets, endpoint telemetry. Learns what normal looks like and flags drift.
Governed command execution on every endpoint plus multi-step workflow orchestration. Every action is policy-gated.
Investigates across endpoint, ticketing, identity, monitoring. Branches on findings until it has a root cause or hands off.
Every finding, command, and approval logged. The audit trail your auditors want and your insurers require.
Approval policies, risk detection, confidence gating, human-in-the-loop. Bounded authority. You set the rules.
Measured by one metric: Autonomy Rate.
The percentage of tickets closed end to end by GenticFlow, by issue class. Not deflection. Not time saved. Tickets actually closed, with an audit trail to prove it.
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 closed.