Solution
Support that can movefrom conversation to action.
GenticFlow connects end-user chat, support workflows, device investigation, remediation, remote support, and technician tools, so support does not stop at a reply or ticket summary. When it cannot or should not act, technicians get a fully investigated escalation with evidence and the next step.
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The problem
Most AI in IT is a wrapper around a knowledge base.
Most AI in IT stops at the request: it suggests an article, summarizes the ticket, or routes it to a queue. That does not inspect the affected device, run an approved fix, verify the result, or leave a complete resolution record. Support teams need more than answers. They need context, action, and evidence.
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.
Reason about the ticket
Classify the issue, identify which endpoint or set of endpoints is involved, decide what evidence is needed before any action runs.
Collect live context
Pull endpoint state, event history, service status, dependent system health. Compare against fleet baseline.
Form a root-cause hypothesis
Combine endpoint observations with prior similar cases. Produce a hypothesis with confidence and supporting evidence.
Choose the response path
Built-in playbook, custom workflow, technician investigation, or escalation. Routed by risk, confidence, approval policy, and verification requirement.
Execute and verify
Run the action, capture the result, verify the outcome, and write a complete resolution record. If verification fails, the diagnostic chain is handed to a technician with the logged before-state so they can decide whether to revert.
Document and learn
Every action, approval, and verification is recorded. Recurring patterns surface as candidates for new playbooks.
What support can do when device action is built in.
Not text suggestions. Concrete support work with device context, verification before close, and evidence after.
What gets escalated and how it arrives.
When GenticFlow escalates, it hands the case to a technician diagnosed, contextual, and routed correctly.
FAQ
Common questions.
Specific answers for service desk and operations teams evaluating this workflow.
How is this different from an LLM chatbot?
Chatbots produce text. GenticFlow produces device context, approved action, verification, and a complete resolution record. It deploys an endpoint agent, observes affected device state, runs approved fixes, and respects approval policy.
Can we customize what it can close under policy?
Yes. Every fix class has an approval policy at the global and client level. You can require approval for anything, including running a service restart.
What models does it use?
GenticFlow runs on multiple model providers including Anthropic, OpenAI, Google, and self-hosted options. Model routing is configurable per workload to balance cost, latency, and accuracy.
How does it integrate with our PSA and RMM?
Native integrations with Halo, ConnectWise, Autotask, ServiceNow, Datto, NinjaOne, and others. GenticFlow reads tickets from the PSA, writes back updates, and triggers actions through the RMM or its own agent.
Give every technician a complete case.
See GenticFlow move from support request to device investigation, approved action, technician context, and a complete resolution record.
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.