Interview proof pack

Proof, not positioning.

Three systems. Three kinds of proof. One consistent approach: start with the operator, then build for trust and use.

The evidence

What is real, what is a prototype, and what stays private.

AI implementation package 4 outputs

backlog, memo, roadmap, controls

BYU schedule NCSOS 293 -> 59

adopted scheduling recommendations

BYU schedule NET 46 -> 9

public outcome signal

Reviewable ops system Human-gated

scoring, QA, and follow-up loops

Three systems / three kinds of proof

Then read the prototype and private-system stories for a fuller view of how I work.

Sports Analytics / Decision Support

A staff-facing basketball scheduling workflow that turns messy NCAA data and selection-committee constraints into repeatable decision support.

AI-assisted workflowanalyticsbriefings

AI Implementation / Client Delivery Prototype

A demo-ready prototype that turns messy discovery notes into a reviewable AI implementation package.

AI implementationdiscovery intakegovernance

AI Workflow / Career Pipeline Operations

A private career pipeline operations system for sourcing, scoring, QA, follow-up, and human-reviewed decisions.

workflow designreview queuesdocument QA

Straight ownership framing

What I owned

Problem definition, workflow design, evaluation criteria, source selection, validation, rollout, user feedback, adoption, and outcome framing.

What AI assisted with

Code, app structure, scripts, UI wiring, parsing, tests, and iteration support. I stayed accountable for whether the workflow was useful and honest.

Best-fit work

Working boundary