Use case
Slow computer issues diagnosedbefore a technician starts from zero.
A slow computer support request arrives with one sentence and zero context. GenticFlow pulls live CPU, memory, disk, process, startup, and update state from the device, identifies the actual cause, and either fixes it or hands a technician the investigation already attached.
CPU sustained 92% for 47 minTop process: OneDrive.exe (74% CPU)Disk free: 4.2 GB / 256 GB (1.6%)Pending Windows updates: 3 (failed twice)Page file thrashing: 1.2 GB/s readRecent change: Acrobat Reader auto-updated 03:11
The problem
Slow PC tickets are the worst kind: vague, frequent, and time-sinking.
The user says it is slow. The technician opens a remote session, sits and watches Task Manager, scrolls through services, and burns 20 to 40 minutes finding out it was a Chrome tab, a stuck OneDrive sync, or 92 percent disk. None of that should require manual investigation from scratch.
How GenticFlow investigates
Technicians start with context instead of a blank ticket.
GenticFlow pulls live context from the affected device, correlates against fleet baselines, and produces a root-cause hypothesis with the steps it recommends next.
Profile CPU and memory
Live process tree with CPU, memory, handle, and runtime context. Identify the top consumers and how long they have been hot.
Check disk pressure
Free space, page-file activity, IOPS saturation, fragmentation on rotational disks, and recent disk-bound process behavior, with read and write throughput history charted over time to separate a one-off spike from sustained disk pressure.
Baseline against the endpoint history
Compare current resource patterns to the learned normal for this specific machine. Detect drift, not absolute thresholds.
Correlate with recent changes
Updates, new installs, driver changes, profile sync events, or scheduled tasks that started in the same window.
Run safe-listed cleanups
Clear temp files, restart memory-leaking services, kill orphaned processes, retry failed updates, free disk space.
Verify performance recovered
Re-sample CPU, memory, and disk metrics after the action. Confirm the metric returned to baseline before closing.
What gets resolved under policy.
Common deterministic causes get resolved end-to-end with before-and-after metrics attached as proof.
What gets escalated and why.
When the evidence points to hardware, environment-wide drift, or a change requiring technician judgment, the ticket arrives diagnosed and ready to act on.
FAQ
Common questions.
Specific answers for service desk and operations teams evaluating this workflow.
Does this work without an RMM?
Yes. GenticFlow deploys its own endpoint agent. RMM data is supported as additional context but not required.
What if the user reopens the ticket the next day?
Recurrence is detected automatically. A repeat ticket on the same endpoint and same root-cause class is routed differently, often as an incident, with the prior diagnosis chain attached.
Can we control what gets fixed automatically vs. queued for approval?
Yes. Every fix class has an approval policy. Safe actions like clearing temp files can run automatically while more invasive actions like uninstalling software always require approval.
What about laptops that are offline when the ticket arrives?
The investigation is queued. When the endpoint comes online, the agent runs the diagnostic, updates the ticket, and proceeds based on the configured policy.
Hand techs diagnosed cases, not vague symptoms.
See GenticFlow profile a real slow PC, identify the actual cause, and either fix it or hand a technician a complete diagnostic chain.
Explore related
Other ways teams use GenticFlow.
Each page walks the live investigation path against a real ticket so you can compare patterns across categories and stacks.