Your senior engineers should not beworking the L1 queue.
GenticFlow closes routine L1 tickets and escalates anything requiring approval fully diagnosed, so your IT team spends less time on repetitive work and more time on projects that move the business. Measurable outcomes, full audit trail, on the stack you already run.
Best fit for fleets of 100+ endpoints. Smaller? Get in touch and we'll see what makes sense.
Sound familiar?
Asked to do more with less, every quarter.
Headcount is flat or shrinking. Ticket volume is not. Every budget cycle tightens and the gap between what your team can cover and what the business needs widens.
Your sysadmins are stuck on repetitive tickets.
Password resets, printer issues, VPN problems, disk space. These are not hard. They are just relentless. Your best people spend their weeks on work that does not need their skills.
AI tools without measurable outcomes don't make the budget.
Pilots that produce slideware get cut at the next budget cycle. You need automation with numbers attached: tickets closed, hours returned, an audit trail to prove it.
The IT teams that win won’t be defined by their engineers. They’ll be defined by their level of automation.
Outlook is slow again.
A user pings the helpdesk at 9:42am. Here is what the AI engineer does before a tech opens the ticket.
57 seconds. The user pings back: working again. No tech touched it.
What changes with the AI engineer.
Connects to your ticketing system in minutes. Deploys to endpoints in hours. Starts closing L1 the same day.
Routine L1 closes 24/7 without adding headcount.
Printer, Outlook, Windows Update, VPN, disk, slow performance, services, and more. Within these issue classes, the AI engineer auto-resolves the routine cases. Harder cases escalate to your team with the investigation already attached, around the clock.
Most L1 issues never become a ticket.
End users chat with your branded AI L1 technician. It runs the diagnostic on the user's endpoint, applies the fix, and confirms it back in the chat. The ticket queue stays clear for the work that actually needs a human.
Your sysadmins never start from scratch.
Tickets the AI engineer closes are done. The ones it escalates arrive already worked up: diagnostics gathered, findings written, root cause proposed, next steps recommended.
Governance built in, not bolted on.
Risk detection on every command, approval policies you attach to workflows, and a full audit trail on everything that runs. Inside workflows, dangerous commands pause for a technician's sign-off before they execute.
Every action documented.
The AI engineer logs what it ran, what it saw, and who signed off. Auditors, insurers, and compliance teams can reconstruct any ticket from those records.
Numbers you can defend in a budget review.
Tickets auto-resolved per month, hours of L1 labor avoided, escalations that arrived pre-investigated. Concrete deltas leadership can compare against the line item, not vibes.
Works with what you already have.
ServiceNow, Jira Service Management, Zendesk, Freshservice, Freshdesk, HaloPSA. Bidirectional sync, ticket of record stays where it is.
Lightweight agent runs alongside Intune, your RMM, or whatever you already manage endpoints with. No conflict, no replacement.
AD group policy, Intune, RMM script push, or scripted install. Windows, macOS, Linux, FreeBSD.
Common questions
What happens to sensitive data in the commands and outputs the AI engineer sees?
Command outputs hit the PII redactor on the GenticFlow server before any prompt is sent to the LLM. Emails, phone numbers, card numbers, API keys, and bearer tokens are replaced with tokens, the LLM provider only sees the redacted prompt, and the tokens are restored in the response surfaced to your team. Raw identifiers never reach the LLM provider, and they never appear in audit log exports either.
Can I require approval before the AI engineer runs a risky action?
Yes, inside workflows. Attach an approval policy to a workflow and any command flagged as dangerous pauses and emails the designated approvers. You configure whether any one, a majority, or all must sign off, and a timeout. The command executes only after approval.
What audit trail do I get for compliance reviews?
Every command the AI engineer runs is logged with the command text, risk level, timestamps, output, and any approval decision. Investigation evidence includes the diagnostics it checked and what it found. Your auditors can reconstruct any ticket end to end from those records.
What can I actually report to the board?
Configurable dashboards with historical charts and data metrics. You choose what to display: ticket outcomes, workflow execution results, agent conversations, and other activity measures. You can build the view your leadership expects.
Do I need to replace my ticketing system?
No. Bidirectional sync with ServiceNow, Zendesk, Jira Service Management, Freshservice, Freshdesk, HaloPSA, Autotask, and ConnectWise. Ticket of record stays in your existing system.
How do I deploy the endpoint agent across my fleet?
AD group policy, Intune, RMM software push, or scripted install. Agent supports Windows, macOS, Linux, and FreeBSD.
Run it on a real ticket.
Run the AI engineer against a ticket from your queue: supported issue classes close end to end, and escalations come back with full investigation findings.
Want to see how pricing is structured first? See pricing