Flagship case study · Quality operations
Approximately 70% less quality-review time.
A workflow transformation in a live customer-support environment—designed, deployed, adopted, and measured from the operator’s seat.
- Organization context
- 1,000+ employees in 2026
- Direct team scope
- 30–45 agents
- Verified result
- ~70% less review time
01 / Operating context
Quality work inside a live service operation.
OneSupport is a U.S.-based outsourced customer experience and technical-support provider serving enterprise clients, with 1,000+ employees in 2026. Within that company context, I lead distributed teams of 30–45 agents and own staffing, coaching, quality, escalations, performance management, and SLA/KPI results.
The QA workflow operated alongside real customer, staffing, and service-level demands. Any intervention had to fit the managers’ and agents’ day-to-day work.
02 / Constraint
A necessary workflow was consuming substantial review time.
Quality review produced signals needed for management action and coaching, but the process itself was time-intensive. The opportunity was to make the workflow materially faster without treating deployment as a purely technical exercise.
03 / Intervention
Design the system around the operating problem.
I designed and deployed a QA automation pipeline. My ownership covered the operating need, requirements, implementation, rollout, frontline adoption, and measurement—not only the technical workflow.
Start with the constraint. Automate the repeatable work. Keep the system accountable to the people and decisions it supports.
04 / Adoption
Implementation included the frontline environment.
I carried the pipeline through rollout and adoption so it could become part of the existing operating rhythm. That distinction matters: an automation is not a transformation until the people responsible for the work can use it in practice.
05 / Measured result
~70%
reduction in quality-review time
This is the verified operating outcome. Review volume, labor hours, dollar savings, avoided headcount, and secondary quality outcomes have not been calculated and are intentionally not claimed.
06 / Why it matters now
AI value depends on workflow and adoption.
This case demonstrates the transformation pattern I bring to AI enablement: identify a high-friction workflow, establish the operating need, build the intervention, introduce it into a live team environment, and measure a defensible result.
Hands-on AI platform engineering now gives me additional technical depth to evaluate architecture and controls. The operating discipline remains the same: the model is one component; the measurable workflow is the product.