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Use Case
IT helpdesk teams spend significant time reading tickets to understand what they're about before they can assign them. A ticket labelled "it's broken" could be anything. Reading it, categorising it, assessing priority, and routing it to the right queue (repeated 50 times per day) is time that could go toward actually fixing things.
Custom AI triage classifies tickets by type, assigns priority based on business impact signals in the ticket text, suggests resolution steps from your knowledge base, and routes to the correct queue, before a human reads it. Agents open a pre-classified, pre-routed ticket with a suggested fix already attached.
Integrates with ServiceNow, Jira Service Management, Zendesk, and Freshservice. Your team continues working in the tool they already use.
Tell us about your IT helpdesk setup.
Six components that handle the triage work, so your agents handle the technical work.
Incoming tickets are classified by type (hardware, software, access, network, email, printing, or custom categories you define) and assigned a priority score (critical, high, medium, low) based on business impact signals in the ticket text. Classification happens before any human reads the ticket.
For common ticket types, the system searches your knowledge base for relevant resolution steps and surfaces them in the ticket, before the agent opens it. The agent sees the suggested resolution alongside the ticket, confirms whether it applies, and can resolve common issues without researching them each time.
Classified tickets are routed to the correct queue based on type, priority, and any custom routing rules your IT team defines. Network issues go to the network team; access requests go to the IAM queue; hardware tickets go to the on-site support queue. No manual reading and re-queuing required.
The system detects escalation signals in ticket text (multiple employees affected, production system down, executive sender, or urgency language) and flags these tickets for immediate attention even if the initial priority classification would have been lower. A VIP user's laptop issue routes differently than a standard user's.
The classification, priority, routing assignment, and knowledge base suggestions are written directly into your existing ticketing system (ServiceNow, Jira Service Management, Zendesk, or Freshservice) as structured fields and comments. Your team continues working in the tool they already use.
A dashboard tracks classification accuracy, routing correctness, and MTTR trends over time. Agents can flag incorrectly classified tickets, these corrections feed back into model retraining. The model improves on your actual ticket patterns over time.
From ticket history to live triage with a feedback loop, the build sequence.
Ticket history analysis and taxonomy definition
We pull your historical ticket data and work with your IT manager to define the classification taxonomy, the categories and priority levels that match how your team actually handles tickets. Generic IT categories are a starting point; your taxonomy is customised to your environment.
Classification model training
The classification model is trained on your labelled ticket history. We evaluate accuracy by category and flag categories with insufficient training examples, common for rare ticket types. Where training data is thin, we apply few-shot techniques to extend coverage.
Knowledge base indexing
Your IT knowledge base (whether in Confluence, ServiceNow, a SharePoint wiki, or a custom system) is indexed into the RAG pipeline. Resolution suggestion quality depends directly on knowledge base quality: we flag articles that are too vague or outdated to generate reliable suggestions.
Routing logic configuration
Routing rules are configured in the system: which ticket categories go to which queues, how priority affects routing, and which conditions trigger escalation. Business rules (e.g., VIP user list, executive escalation policy) are encoded with input from your IT manager.
Integration with your ticketing system
The triage pipeline integrates with your existing ticketing system via API. Classified tickets have their category, priority, assigned queue, and knowledge base suggestions populated automatically. Agents see fully structured tickets, not raw text to interpret.
Accuracy monitoring and feedback loop
Agents flag incorrect classifications via a simple button in the ticket interface. Feedback is collected weekly and used to retrain the model. The classification accuracy dashboard shows improvement trends and highlights categories that consistently underperform.
IT teams with consistent ticket volume and the data to train a classification model.
A three-person IT team handling 200 tickets per week spends significant time just reading and sorting tickets before doing any actual work. Automated triage means agents open pre-classified, pre-routed tickets with a suggested resolution, the support work, not the sorting work.
MSPs field tickets across multiple client environments with different systems, different priorities, and different routing rules. A custom triage system handles the initial classification and routing per client, so L1 agents spend less time understanding ticket context and more time on actual resolution.
Technical support tickets for SaaS products often contain log snippets, error messages, and feature requests mixed together. A classification layer separates bug reports from feature requests from billing questions (and routes each to the right team) without a human triaging each ticket manually.
We’d rather decline than take a project that won’t deliver value.
IT teams with fewer than 1,000 historical tickets
The classification model is trained on your ticket history. With fewer than 1,000 historical tickets, the model cannot learn reliable patterns across enough ticket categories. If you are a new team or have not tracked historical tickets well, a rules-based routing approach is more appropriate until you have sufficient history.
Teams looking to eliminate their helpdesk headcount
Automated triage reduces the time agents spend on routing and initial research. It does not replace the agent. Complex issues, multi-system outages, and anything that requires hands-on diagnosis still need a human. The right expectation is that your existing team handles more tickets per agent, not that you eliminate the team.
Tell us your approximate monthly ticket volume, which ticketing system you use, and the biggest bottleneck in your current triage process. We'll reply within one business day.