Case studies
Each case study is a full report in the format of the original project documents: business context, root-cause analysis, solution design, technical architecture, testing, results and limitations. The figures keep the classification used in the source documents. Across all six the method repeats: find the rule underneath the symptom, redesign the flow, automate the retrieval, make it visible, add the control, and codify the knowledge so the improvement stays.
SLA recovery & capacity
A client cut the per-case SLA from 24 hours to 3 hours while daily volume grew from about 100 cases to peaks above 2,000. I rebuilt the queue, the allocation rules, shift coverage, alerting and performer enablement around the new deadline.
Workflow automation
A new client went live without the automatic missing-document follow-up the standard workflow relies on. I built the join across three data feeds and a reviewed batch-reminder workflow, so prepared reminders stopped being assembled by hand.
Queue control & SLA visibility
Four insufficiency queues with client-specific deadlines were tracked in a workbook assembled by hand twice a day. I replaced the manual assembly with scheduled refresh, queue routing, per-queue SLA clocks and deadline alerts.
Management visibility
An 80-performer department's operating picture was split across live sheets, daily reporting and separate turnaround reports. I built a single rule-driven supervision view covering productivity, quality, SLA exposure and performer status.
Analytics & quality improvement
Leadership needed one reliable daily picture, and error reports stopped at the field level rather than the rule level. I built one reporting routine for the management view plus an assessment loop that turns error patterns into a rule-level coaching agenda.
Knowledge codification & AI
A validation team transcribed 15–20 fields per case under more than 100 conditional rules that had never been written as rules. I wrote the rule book and built the tools that apply it, moving the team from transcribing to auditing.
How to read these reports. Every project document distinguishes between what existed before, what I personally designed and built, what was tested, what went into use, and what remained a proposal or an estimate. Results that were not independently audited are labelled owner-reported; results from a run of the live system are labelled observed; sample measurements say so. No client, employee, applicant or case data is published, and all interface visuals are labelled reconstructions or synthetic mockups.