Case Studies
Real outcomes from real engagements. All case studies are anonymized to protect client confidentiality, but the results speak for themselves.
The Problem
A large operations team was spending excessive time on manual incident triage. Alerts lacked context, ownership was unclear, and average response times were unpredictable. The team was reactive rather than proactive.
What I Did
Mapped the end-to-end incident flow, identified bottlenecks in the triage process, implemented ownership clarity rules, and built a triage dashboard with contextual alert enrichment.
The Outcome
Reduced manual triage effort by 20–40%. Improved mean-time-to-acknowledge by establishing clear ownership paths. Team shifted from reactive firefighting to proactive monitoring.
Key Metrics
The Problem
Leadership had no visibility into operational health. Multiple teams maintained separate spreadsheets with inconsistent metrics. Monthly reporting was a manual, error-prone process that consumed days of effort.
What I Did
Audited existing metrics across teams, established a unified KPI framework, designed automated dashboards pulling from source systems, and created tiered reporting for different audiences.
The Outcome
Monthly reporting time reduced by 60–70%. Single source of truth established across 4 teams. Leadership gained real-time visibility into operational metrics.
Key Metrics
The Problem
Ticket backlog had grown unmanageable. Intake was chaotic with no consistent prioritization. Teams spent more time managing tickets than resolving issues. SLA compliance was inconsistent.
What I Did
Redesigned intake workflow with structured forms, implemented a prioritization matrix based on business impact, automated routine categorization and routing, and built hygiene scoring to maintain ticket quality.
The Outcome
Backlog reduced by 35–50% within 8 weeks. SLA compliance improved significantly. Team reported spending 25–30% less time on ticket administration.
Key Metrics
The Problem
High-volume ticket environment with inconsistent categorization. Misrouted tickets caused delays and frustration. Manual classification was time-consuming and subjective.
What I Did
Analyzed historical ticket data to identify classification patterns, implemented an AI-assisted classification model with confidence scoring, maintained human oversight for edge cases, and measured accuracy against manual baseline.
The Outcome
Classification accuracy improved by 15–25% over manual baseline. Routing errors reduced significantly. Team gained capacity to focus on complex issues.
Key Metrics
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