01 / Product systemsTruist · 2025–2026
Creating a shared model of product behavior
At Truist, I worked without an established end-to-end specification infrastructure or a complete set of product behavior documents available to me. I built a structured framework that connected business rules, UI interactions, system responses, exceptions, entitlements, and dependencies.
Why it matteredThe same behavioral model could inform refinement, implementation-ready requirements, acceptance criteria, QA conversations, and onboarding. The résumé describes this as the organization’s first structured product specification and system-behavior framework.
View a recreated coverage artifact ↗ SpecificationsSystem behaviorCross-team alignment
02 / Delivery definitionCommercial transfers
Turning feature scope into testable stories
I analyzed transfer capabilities across creation, review, approval, lifecycle changes, and exception paths, then translated the findings into stories with behavior based Given, When, Then acceptance criteria. The coverage framework separates behavior discovery from story formatting so teams can find gaps before writing.
User storiesAcceptance criteriaEdge cases
03 / Product improvementFeature readiness
Making features ready for story refinement
I assessed whether feature scope, UI design, interaction rules, and system behavior were clear enough for the next story stage: PI planning placeholders, interim refined stories, or final sprint-ready stories. Gaps became explicit decisions or discovery work before Scrum refinement.
Representative outputA staged feature readiness assessment showing available evidence, unresolved rules, decision owners, and the next story handoff.
View the recreated framework ↗ Feature readinessStory fidelityRefinement
04 / AI toolingReusable practice
Grounding AI in product knowledge
I designed an AI-enabled authoring approach that uses established rules and decision history as source material for specifications, stories, training, and review. I also developed an evidence-grounded learning framework that checks source fitness, contradictions, and uncertainty before turning information into action.
Knowledge systemsAI-assisted authoringEvidence quality