The AI engineer at work.
Watch how a single printer problem moves through the platform. A user chats in. The AI engineer picks it up, investigates the real endpoint, takes governed action, and closes the ticket with a full audit trail. Every step is something the product does in production today.
Chat answers common questions directly and, when action is needed, hands the issue to the AI engineer with full context.
What you are seeing.
The walkthrough above is scripted, but every screen is something GenticFlow does today. The chat on the left is the real end-user chat product. The investigation commands are the same PowerShell the AI engineer runs when a spooler crashes in the wild. The policy gate, the remediation, and the audit trail are all production behaviour.
Where problems arrive from.
This tour follows a chat escalation, but the AI engineer picks up work from every major intake path:
- End-user chat: the chat product triages, deflects, or escalates. Routine questions get answered directly. Issues that need action get handed to the AI engineer with full context.
- RMM and monitoring webhooks: automated signals fire tickets before a user even notices. Printer spooler crashes, service failures, disk thresholds.
- PSA tickets: direct submissions from technicians and end users in ConnectWise, Autotask, HaloPSA, ServiceNow, and other supported systems.
- Security and endpoint tools: integrations with NinjaOne, SentinelOne, and similar products surface threats and incidents directly into the queue.
What a live demo adds.
This tour is deterministic so it reads cleanly in under a minute. A live demo runs the same patterns on an endpoint we control, with you driving: change the approval policy on the fly, throw an unscripted problem at the engineer, see what it does when it hits something unfamiliar. During a pilot, the same engineer runs on a subset of your own fleet against the tickets your team is triaging today.