PRIS Corp (Warrior Invictus)
QA Manager and Test Automation Architect, Guidewire InsuranceNow
Greater Chicago Area (Hybrid)
Leadership & Reporting
My responsibilities cover quality assurance and AI strategy across a non-standard auto insurance technology platform built on Guidewire InsuranceNow, with rollouts active across more than ten states. Senior IT leadership receives my reporting on portfolio health and on the SLA framework that governs our AI programs.
Under my team the average QA review cycle compressed from 7.3 workdays to 2.0 workdays over the past year, a 72 percent reduction validated against EazyBI reporting. The metrics driving the compression were catch rate and escape rate, with rework cost translated into dollars rather than tracked as a count, and coverage expanded over the same period. New hires are brought up to platform certification and fully operational within sixty days under the onboarding program I established.
AI Programs
The AI-assisted support triage system I designed and shipped now fronts the IT helpdesk for the company's internal employees and external producer network. First response moved from regular SLA breach to under two minutes. SLA compliance moved to near 100 percent, and the program reallocates more than 1,200 hours of annual labor away from manual ticket handling.
Alongside the system, the supporting SLA framework I authored introduced a six-tier priority taxonomy and a redesigned intake form. The form lets the system derive priority from structured impact questions rather than from a user-selected dropdown, and the framework is reviewed quarterly as the operating contract for the program.
Internal AI tooling now in development supports QA analysts through Jira ticket evaluation, acceptance criteria gap analysis, and test scenario suggestion, with automated test script generation planned for a second phase. The system uses few-shot prompting and retrieval over our own Jira history and test repositories, which is what makes it context-aware rather than generic. The tooling is built around the existing review workflow rather than around the underlying model, so it has held up across model upgrades and remains in daily use.
Executive Reporting
Three executive reporting tools I built run on live work-tracking data: a QA KPI tracker, a state rollout readiness console, and an IT portfolio big board covering more than 100 initiatives. Each auto-derives status, so the executive view and the team view never disagree. All three are now standing artifacts in executive leadership reviews and have been transitioned to a successor for ongoing ownership.
Cost, Vendor & Process
Documented annual savings during the review period totaled more than $80,000. The largest contributor at over $60,000 was the AI triage program. Deployment smoke test automation contributed more than $20,000 by compressing release testing from fifteen QA hours to under one hour, and a vendor renegotiation produced roughly $2,300 while doubling licensed seats and increasing monthly cloud credits by 50 percent.
QA-led pre-refinement sessions, which I introduced, strengthen acceptance criteria before development begins and surface testing scope early. Kickback rates dropped by approximately 35 percent under the practice, and sprint predictability improved across the QA function.
Standards, Tooling & Community
The QA Policy Handbook, the Xray Test Case Management Guide, and the new-hire QA Analyst training program for the InsuranceNow platform are mine, including its integrations with comparative raters, payment processors, and adjacent policy administration systems. Xray adoption fell to me as well, from workflow design and evidence collection standards through Jira integration and team rollout.
A Rules Engine API test suite covering 129 active validation rules across the non-standard auto product line came out of this period. The suite runs through a Python execution harness against a Postman collection, with an automated reporting pipeline producing QA-ready output.
Monthly QA Town Hall meetings, which I founded and run, align quality practice across the IT organization. The AI Native certification rounds out the period.