Ticket triage and routing
Classify the issue, identify the right queue, and recommend the best-fit technician using the context your dispatch team already relies on.
Private AI for MSP operations
Private AI for MSP ticket triage and operations.
Braintek builds private AI ticketing systems that understand your technicians, your clients, and the way your service desk actually runs. Core reasoning can stay 100% local.
Tria, our own AI ticketing agent, runs this way. Ticket text is not sent to a public model, and the system is trained around our techs, clients, history, and service rules.
Service desk 18 techs 46 skills 1.2K tickets/mo
Incoming ticketT2026-1842
Priority 3 Northline Fabrication
Remote user cannot connect to VPN
Started after a password change. User is traveling and needs access before 2 PM.
CategoryNetworking
QueueL1-Remote
Confidence97%
Safety gatePassed
Assigned technician M. Carter Best skill, availability, and client-context match
Auto-assigned Also just auto-assigned
T2026-1840 New hire needs M365 and VPN access
Auto-assigned T2026-1839 Server storage alert crossed threshold
Auto-assigned DispatchTicket queue
Updated now Auto-routed28
Assigned17
Completed42
Auto
User locked out after password resetAssigned: M. Carter, L1-Remote
8mAuto
New hire needs M365 and VPN accessAssigned: K. Dawson, L1-Remote
11mAuto
Office Wi-Fi dropping in conference roomAssigned: R. Hayes, L2-Escalation
14mAuto
Server storage alert crossed thresholdAssigned: S. Miller, Server
18mAuto
Front desk printer is offlineAssigned: J. Brooks, Bench
21mTeamTechnician skill profiles
Live workload MC
M. CarterVPN 4/5M365 4/5Identity 3/5
2 openAvailable
RH
R. HayesNetwork 5/5Firewall 4/5Wireless 4/5
1 openAway
SM
S. MillerServers 5/5Security 4/5VMware 4/5
3 openAvailable
JB
J. BrooksEndpoint 4/5Hardware 4/5Backup 3/5
4 openAvailable
KD
K. DawsonM365 5/5SharePoint 4/5Email 4/5
2 openAvailable
5 of 18 techs shownLearned skills + availability + workload + queue eligibility
Built inside a working MSPUsed on Braintek's own service-desk operations
100% local model optionKeep sensitive ticket context private
Human control stays in placeYou choose what the system may do
Built inside Braintek
Private AI ticket triage, proven inside an MSP.
Generic AI does not know which technician is strong in a specific platform, which client has an unusual environment, or which routing choices create work later. Tria was built to make those daily decisions with real MSP context. Approved, high-confidence categories can be auto-assigned; exceptions stay visible to dispatch.
01Read the ticket
Identify the issue, urgency, queue, and the skills the work is likely to need.
02Bring in MSP context
Consider technician strengths, workload, client history, environment, and service rules.
03Auto-assign approved work
Assign policy-safe, high-confidence categories, record the reason, and hold exceptions for human review.
A working example
See how Tria handles an MSP ticket.
The system does not stop at a category label. It gathers the operational context behind the ticket, checks the available options, and records why it chose the route as it updates the service desk.
- Clear queue and skill classification
- Client-specific context and known constraints
- Technician fit, workload, and availability
- An explanation your dispatcher can review
Ticket decisionT2026-1842
Auto-assigned1
Ticket understood
CompleteRemote access issue after a credential change. Time-sensitive, no outage signal.
2
Client context checked
CompleteKnown VPN platform, remote-first support rule, no active change window.
3
Technician fit compared
CompleteSkills, active workload, availability, and prior client familiarity considered.
Completed routeL1-Remote / M. Carter
Skills learned from real work
Technician skill profiles improve from the tickets they actually close.
Tria builds a living picture of what each technician has demonstrated, instead of relying only on a manually maintained skills list.
On a scheduled cycle, completed ticket evidence is analyzed for the work performed, the skills shown, and how independently the issue was handled. A Markov-based rating process turns repeated evidence into controlled recommendations for human review.
- 01Digest completed work
Review the resolution evidence from recently closed service tickets.
- 02Recognize demonstrated skills
Identify the platforms, work domains, complexity, and independence shown by the technician.
- 03Govern rating changes
Use a Markov-based process to surface skill-rating recommendations without silently rewriting technician profiles.
Private by design
A 100% local AI model trained around how your MSP works.
Core AI can run inside your environment instead of sending ticket text and client context to a public chatbot. That gives your team a private foundation for daily operational work.
With your approved data, Braintek trains the system around how your technicians work, how your clients are supported, and how your dispatch team corrects decisions over time. The goal is an MSP operations system that reflects your business.
Local MSP contextPrivate knowledge layer
TechniciansSkills, queues, workload, availabilityConnected
ClientsEnvironment, history, service rulesConnected
OutcomesClosed-ticket evidence and dispatcher correctionsLearning
BoundariesApproval rules and human-only decisionsEnforced
What it can handle
MSP service desk automation that starts with the ticket queue.
Start with ticket triage, then extend the same private operational context into the places where your team loses time or consistency.
Technician prep
Give the assigned technician a concise brief with the relevant client history, environment, likely cause, and a safe place to start.
Dispatcher visibility
Show why the system made a recommendation, what evidence it used, and where a person still needs to make the call.
Service risk signals
Surface repeat issues, stalled tickets, follow-up gaps, and patterns that are easy to miss when the queue is moving fast.
What Braintek can build
Use our triager as the starting point, or build around your bottleneck.
Every MSP has different people, clients, tools, and risk tolerance. We start with the workflow that costs you the most time, then build the private system around the way your team already operates.
Talk through your use case- 01Map one real workflow
Find the decision points, context, exceptions, and human approvals.
- 02Build the private foundation
Connect only the approved operational context and define the boundaries.
- 03Prove it with your team
Run it beside the current process, compare decisions, and tighten the fit.
- 04Expand where it earns trust
Automate only the steps that are predictable, reviewable, and worth it.
Frequently Asked Questions
Can MSP AI ticket triage run on a local model?
Yes. Core ticket reasoning can run on a 100% local model inside your environment, so ticket text does not need to be sent to a public AI model.
Is this a product or a custom build?
Both paths are available. Braintek can adapt the triage system we already use, or build a private operations system around a different MSP workflow.
Can Tria automatically assign MSP tickets?
Yes, for the routing paths you approve. The system can assign routine tickets automatically while holding exceptions, low-confidence matches, and higher-risk decisions for a person.
How does the AI choose the right technician?
Tria compares the ticket's required skills with technician skill ratings, availability, workload, queue eligibility, and your routing policies. The decision and its reason remain visible to dispatch.
How does Tria learn technician skills?
On a scheduled cycle, Tria analyzes closed-ticket evidence to identify the skills technicians demonstrated. A Markov-based rating process turns repeated evidence into controlled skill-rating recommendations for human review.
Ready for IT that just works?
Book a no-pressure discovery call. We'll review your setup and show you exactly where you stand.