Context
Operator-led AI implementation
Strongest work blends discovery, workflow design, AI-assisted build direction, validation, rollout, and adoption with the people who will use the system.
Interview Proof Pack
Enterprise SaaS operator who turns messy customer and operator workflows into AI-assisted systems people actually use.
The ask
Where this profile fits best
The useful ask is routing: Utah, startup, AI product, or customer-facing implementation teams where this proof stack matches a real need.
Scoreboard
Three signals to anchor the conversation.
The proof pack leads with AI implementation packaging, real operator adoption, and reviewable operations design.
backlog, memo, roadmap, controls
adopted scheduling recommendations
public outcome signal
scoring, QA, and follow-up loops
Read first
The three stories to read first.
Start here for interviews. These cover the implementation package, the adopted decision workflow, and the reviewable operations system.
AI Implementation / Client Delivery Prototype
AI Implementation OS
A demo-ready prototype that turns messy discovery notes into a reviewable AI implementation package.
Read AI Implementation OS case studySports Analytics / Decision Support
AI-Assisted Basketball Scheduling Decision Workflow
A staff-facing basketball scheduling workflow that turns messy NCAA data and selection-committee constraints into repeatable decision support.
Read Basketball Scheduling case studyAI Workflow / Career Pipeline Operations
Job Search HQ
A private career pipeline operations system for sourcing, scoring, QA, follow-up, and human-reviewed decisions.
Read Job Search HQ case studyInterview questions answered
What each story proves.
AI Implementation / Client Delivery Prototype
AI Implementation OS
Good fit for teams that need someone who can turn ambiguous AI discovery into prioritized, governed, rollout-ready implementation work.
- Messy workflow
- AI discovery work often leaves teams with scattered notes, unclear owners, half-formed use cases, governance concerns, and no practical rollout sequence.
- System designed
- A discovery-to-delivery package that turns intake notes into prioritized opportunities, a decision memo, governance notes, rollout milestones, and enablement tasks.
- AI-assisted build
- AI coding assistance helped build the local prototype, deterministic demo flow, data structures, and UI surfaces from the workflow spec.
- Validation
- The demo uses deterministic local logic, synthetic data, and inspectable outputs so the workflow can be reviewed without API keys or client data.
- Usage/adoption
- This is a portfolio-ready implementation package prototype, built to show how I would structure customer-facing AI discovery and rollout work.
- Proof signal
- It directly maps to AI implementation roles: intake, prioritization, business case, governance, roadmap, and enablement in one reviewable package.
- Improve next
- Add a second synthetic industry scenario and a lightweight evaluator that compares package quality across use cases.
- Public boundary
- The public version uses fake Northstar data. It is a demo-ready prototype, not a production client deployment.
Sports Analytics / Decision Support
Basketball Scheduling
Good fit for teams that need someone to turn high-stakes, messy operating decisions into AI-assisted workflows people can review and adopt.
- Messy workflow
- Schedule decisions mixed public rankings, selection-committee incentives, opponent availability, staff preferences, and downside risk.
- System designed
- A repeatable decision workflow for comparing schedule scenarios, quadrant impact, resume strength, and staff-ready recommendations.
- AI-assisted build
- AI coding assistance helped implement and iterate dashboards, scripts, scenario tables, and briefing surfaces around the workflow I defined.
- Validation
- Recommendations were checked through source consistency, quadrant classification, scenario comparison, and human review before staff use.
- Usage/adoption
- Staff adopted the recommendations as part of real scheduling decision work.
- Proof signal
- Public-safe outcome signals after adopted recommendations include NCSOS 293 -> 59, NET 46 -> 9, WAB -0.76 -> +2.59, and Q1/Q2 record 0-2 -> 5-1.
- Improve next
- Add a stronger public-safe before/after artifact that shows the decision path without exposing internal strategy or targets.
- Public boundary
- The public version uses synthetic visuals and public outcome signals. It does not claim the workflow alone caused BYU's results.
AI Workflow / Career Pipeline Operations
Job Search HQ
Good fit for teams that need someone to turn messy GTM, hiring, or operations workflows into reviewable AI-assisted systems with privacy boundaries.
- Messy workflow
- High-signal pipeline work has noisy sources, changing role details, tailored documents, follow-up timing, and many places for stale records or weak claims to slip in.
- System designed
- A local career pipeline operations system with structured records, fit scoring, status tracking, resume/packet QA, follow-up queues, and human approval before action.
- AI-assisted build
- AI coding assistance helped implement scripts, local UI surfaces, validation commands, and artifact tracking around the operating model.
- Validation
- The workflow checks source-of-truth files, PDF/resume readiness, ATS alignment, status consistency, and next-action previews before outreach or submission.
- Usage/adoption
- The system supports ongoing pipeline triage, packet preparation, review loops, follow-up tracking, and closeout updates.
- Proof signal
- It translates directly to GTM AI Ops and RevOps-style workflow design: scoring, routing, QA gates, human review, and durable records.
- Improve next
- Add a stronger synthetic executive dashboard that shows pipeline health without exposing companies, recruiters, resumes, emails, or local paths.
- Public boundary
- The public version uses synthetic screenshots only. Real applications, resumes, contacts, emails, browser/account data, and private documents stay out of the portfolio.
Supporting proof
Useful context after the top three.
Transfer Portal adds more operator adoption proof. ShotSort adds a clear example of confidence, correction, and trust design.
Sports Analytics / Recruiting Workflow
BYU Transfer Portal Evaluation Workflow
A staff-facing evaluation workflow that turns roster criteria, player data, fit notes, and value tradeoffs into repeatable candidate briefs.
Read Transfer Portal Evaluation case studyAI Productivity / Local Workflow Tool
ShotSort
An AI-assisted macOS workflow for renaming, tagging, searching, quarantining, and organizing screenshots and local files.
Read ShotSort case studyProblem definition, workflow design, evaluation criteria, source selection, validation, rollout, user feedback, adoption, and outcome framing.
Code, app structure, scripts, UI wiring, parsing, tests, and iteration support. I stayed accountable for whether the workflow was useful and honest.
Best-fit roles
Where this profile likely fits.
- AI Outcomes / AI Adoption Manager
- GTM Engineer, AI Workflow Automation
- AI Implementation / Strategy Consultant
- Internal AI Enablement Manager
- AI Sales Ops / RevOps Automation
- Healthcare AI Implementation
How to interpret the proof
Plain ownership framing.
- Best fit is implementation, enablement, customer workflow, and adoption work
- Coding-heavy work was built with AI coding assistance
- Best work happens close to operators, customers, messy workflows, and adoption