Leading European University
Case Study
A leading European university managing over 6,000 monthly support tickets sought an AI-driven solution to streamline student and IT helpdesk operations at scale
6000+
Support tickets automatically classified
70%
Reduction in manual triage workload
3x
Bug escalation to developers
Challenge
The university’s support teams were overwhelmed by the volume of IT and student service requests. Manual triage consumed hours daily, slowed response times, and made it difficult to identify recurring technical issues. This not only delayed resolutions for students and staff but also limited visibility into system reliability.
Solution
We implemented an AI-powered classification and triage pipeline tailored for high-volume support operations. Raw ticket data was extracted from BigQuery and preprocessed for downstream analysis. OpenAI LLMs were applied via LangChain and LangGraph to automatically summarize and classify issues, distinguishing between user errors and verified system bugs. The outputs were then integrated into Datadog dashboards for real-time monitoring of bug frequency, user impact, and issue trends. For confirmed bugs, Jira tickets were automatically created, ensuring that critical issues reached developers without delay. The entire workflow was delivered in Python, orchestrated through LangChain and integrated into CI/CD pipelines for continuous monitoring.
Before AI Triage
✘ 100% of 6,000+ monthly tickets triaged manually
✘ Support staff spent hours every day categorizing requests
✘ Slow response times frustrated students and staff
✘ Recurring technical issues went unnoticed
✘ Critical bugs often buried, reaching developers too late
After AI Triage
✔️ 6,000+ tickets automatically classified every month
✔️ 70% reduction in manual triage workload
✔️ Issues summarized in real time, speeding up response
✔️ Recurring bugs tracked live in Datadog dashboards
✔️ Critical bugs escalated 3x faster, accelerating fixes
Results
The university saw an immediate improvement in support efficiency. Manual triage was reduced by over 70%, freeing staff to focus on student-facing issues instead of repetitive sorting tasks. Critical bugs reached developers nearly three times faster, shortening feedback loops and accelerating fixes. With recurring issues now visible in real time, the institution gained the actionable insights needed to strengthen long-term system reliability.
In measurable terms, the project achieved a 70% reduction in manual triage workload, tripled the speed of critical bug escalation, and gave the university greater visibility into recurring system issues.
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