Ticket triage and routing
Classify tickets, surface missing information, route to the right queue, and flag urgency using rules and model outputs that can be audited.
IT support & MSPs
AI support workflows that improve speed without sacrificing service quality.
For support teams and MSPs, the useful work starts in the queue: ticket triage, client context, runbooks, technician assist, escalation packets, and service reporting. We turn those patterns into measured workflows your team can actually run a service desk on.
Focus
tickets · runbooks · knowledge
Risk lens
accuracy · permissions · escalation
Output
agent workflows and evals
What useful support AI looks like in the queue
Support teams already operate under pressure: queue volume, context switching, tribal knowledge, SLAs, documentation drift, and client-specific exceptions. A useful AI system has to live inside that operating reality.
The work is to retrieve approved knowledge, summarize client context, suggest next steps, draft responses, route issues, and expose uncertainty while keeping risky actions behind review. Speed only matters if service quality is easier to prove afterward.
Services that fit support operations
AI agents and automationDesign agent workflows around ticket states, escalation, approvals, runbooks, and technician handoff.LLM, RAG, and evaluation systemsCreate support knowledge retrieval, test sets from real tickets, and scorecards for response quality.AI strategy and roadmapPrioritize automations, choose vendors, plan integrations, and sequence rollout around operational risk.
Use cases
Support workflows where measurement matters.
Knowledge retrieval and runbooks
Give technicians source-backed answers from approved documentation, client notes, SOPs, and historical resolutions.
Technician-assist agents
Suggest next steps, draft responses, summarize context, and prepare escalation packets while humans approve meaningful actions.
Service quality analysis
Review ticket patterns, response quality, time sinks, recurring issues, and documentation gaps so the operating system around support improves.
How support automation usually goes wrong
Trust evaporates quickly when AI closes the wrong tickets, sends confident but incorrect instructions, exposes one client's context to another, or bypasses escalation rules. The system should know when to assist, when to ask for more information, and when to hand off to a person.
The best path is usually incremental: measure the existing workflow, pick a narrow support pattern, build the evaluation set, run the AI as technician assist, and only automate actions after quality thresholds are proven.
Answers
Questions support leaders usually ask first.
Where should support teams start?
Start with triage, routing, summaries, knowledge retrieval, runbook suggestions, response drafting, escalation support, reporting, or quality analysis where the current workflow is measurable.
What should MSPs automate first?
Start with high-volume, low-risk workflows that have clear runbooks and measurable outcomes: classification, summaries, routing, and technician-assist responses.
How do you prevent bad answers?
Use approved sources, confidence thresholds, human review, escalation paths, regression tests, and reporting against real support tickets.
What should be in hand at the end?
Workflow maps, automation candidates, RAG architecture, ticket eval sets, agent design, rollout plan, monitoring notes, runbooks, and handoff documentation.
Next step
Have a support workflow that should get smarter?
Bring the ticket type, queue, knowledge base, or escalation pattern that needs a measurable AI plan.