Case Study — Data Engineering

INDIANA
UNIVERSITY

IU's data engineering team was drowning in tickets. Senior engineers spent more time triaging than building. We deployed an autonomous AI agent that performs intelligent first-pass analysis — transforming their resolution pipeline and freeing the team to focus on architecture, not triage.

45% Productivity Increase
MTTR Reduced Significantly
Q2–Q3 Measured Impact Period
24/7 Agent Uptime

THE
PROBLEM

IU's data engineering team managed a high-throughput ticket queue serving researchers, faculty, and staff across the university. As data infrastructure grew, so did the volume and complexity of incoming issues.

Senior engineers were spending a disproportionate amount of time on initial triage — reading tickets, categorizing issues, identifying root causes, and routing them to the right person. This created a bottleneck that slowed resolution times and pulled experienced engineers away from higher-value work.

Key Challenges

  • High ticket volume with inconsistent categorization
  • Senior engineers spending hours on triage instead of engineering
  • Long mean time to resolution (MTTR) on common issues
  • Knowledge silos — only senior staff could accurately route tickets
  • No automated first-pass analysis or prioritization

THE
SOLUTION

We designed and deployed a custom AI agent purpose-built for IU's data engineering workflow. The agent integrates directly with their ticketing system and performs an intelligent first-pass analysis on every incoming issue.

Using their institutional knowledge base and historical ticket data, the agent categorizes issues, identifies likely root causes, suggests resolution paths, and routes tickets to the appropriate engineer with full context attached.

Agent Capabilities

  • Automatic ticket categorization and priority scoring
  • Root cause analysis based on historical patterns
  • Context-rich routing with suggested resolution steps
  • Continuous learning from resolution outcomes
  • Integration with existing tooling and documentation

HOW WE
BUILT IT

01

Discovery

Deep-dive into IU's ticketing data, workflows, team structure, and pain points.

02

Intelligence Layer

Built the knowledge base from historical tickets, runbooks, and institutional docs.

03

Agent Development

Designed and trained the autonomous agent with custom workflows for triage and routing.

04

Deployment

Rolled out incrementally, measured impact across Q2–Q3, iterated on feedback.

"The agent handles the first pass on every ticket. My team now focuses on engineering, not inbox management."
— Data Engineering Lead, Indiana University

THE
RESULTS

Measured over Q2 and Q3, the agent's impact was immediate and substantial. Overall team productivity increased by 45%, driven primarily by the elimination of manual triage and faster routing accuracy.

Mean time to resolution dropped significantly as engineers received tickets that were already categorized, prioritized, and enriched with likely root causes and suggested fixes.

Impact Summary

  • 45% increase in team productivity across Q2–Q3
  • Significant reduction in mean time to resolution (MTTR)
  • Senior engineers reclaimed architecture and systems time
  • Ticket routing accuracy improved with context-rich handoffs
  • Agent accuracy continued to improve through continuous learning

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