I didn't study product. I lived the problems until I learned how to build the solutions.
Years of fast-paced operations work taught me that operational bottlenecks are not just flaws, they are diagnostic signals. I learned to read them, validate them, and build systems around them. I identify the right problem, define the tradeoffs clearly, and ship focused solutions that teams can actually adopt.
Discoveryfinding the highest-leverage customer and operator pain points
Strategychoosing scoped solutions with clear tradeoffs and success criteria
Executionturning workflows, data, and constraints into usable product systems
Iterationmonitoring adoption, feedback, and quality after launch
Selected Product Work
Case studies framed around problem selection, tradeoffs, and outcomes.
Commerce SystemsActive build
Blanq MFG self-serve print ordering
Blanq MFG had real demand. I connected with multiple potential clients at a local farmers market in a single afternoon, but within a week, I couldn't deliver a quote to any of them. No pricing consistency, no transparency, no self-serve path. I reframed the opportunity around eliminating the wait, the interpretation overhead, and the pricing uncertainty sitting between a buyer's intent and a production-ready order. The result is a focused self-serve flow that cut fulfillment from a 3-to-4-week ordeal to priced on day one, proofed by the next business day, and fulfilled within two weeks.
Transparent pricingStructured order dataProduction handoff
PM signal
Shows product judgment across the full funnel: demand capture, pricing clarity, buyer UX, and fulfillment readiness.
AI DiscoveryPrototype in validation
Ask the Sommelier recommendation engine
Guests don't just need more wine information. They need it before a 10-minute wait for a sommelier makes them default to the cheapest option. I designed Ask the Sommelier around that insight. The goal was to make the restaurant's expertise instantly accessible at the moment of decision. By replacing wait times with guided inputs and curated recommendation logic, the tool actively cross-promotes food pairings and reduces cognitive load. The result is a positively trending wine attach rate, driven entirely by removing the friction between guest curiosity and expert knowledge.
Guided inputsPairing logicGuest confidence
PM signal
Shows how domain expertise can become a guided product experience with clear user value and revenue upside.
Trust & FeedbackPilot in use
Closed-loop reputation management
Most feedback systems fail because the signal arrives too late, allowing low reviews to fall through the cracks before the right people can act. I built this closed-loop system to catch the moment privately. Guests get a frictionless channel to be heard, while management gets a structured queue for targeted triage. By giving the team pointed data on exactly what to improve, the system reduced public negative reviews by 25% and eliminated 8 hours a week of manual recovery work, freeing managers to focus entirely on the live guest experience.
Private captureRating breakdownApproval workflow
PM signal
Shows closed-loop product thinking: capture feedback, route action, recover service, and turn wins into public trust.
Internal ToolsLive operations tool
Catering workflow and label automation
Catering coordination was breaking down: 30-to-40% of orders had a missing item, an incorrect label, or a missing invoice, and no department had a clear build list. I reframed these recurring errors as a product design problem, building a workflow where accuracy became the path of least resistance. The shipped tool standardized the entire lifecycle. It reduced missing items to under 10%, guaranteed 100% label and invoice accuracy, and generated explicit departmental build lists, complete with volume flags to instantly alert corporate of high-labor orders requiring additional support.
PM signal
Shows operational product thinking: improve the employee workflow first to create faster, more consistent customer outcomes.
PM Fit
Why this background is the qualification.
I didn't start in product. I started in operations: managing the Friday night rush, building the catering workflow, catching feedback before it became a public review. That background is not a detour. It is where I learned to read friction, validate real problems, and build solutions people actually adopt. The throughline is PM judgment: choose the right problem, define why it matters now, and ship something focused enough to work.
Product discovery
I start with workflow observation, customer friction, service gaps, and operational constraints before I touch the product surface.
Prioritization under constraints
I weigh team capacity, data availability, margin logic, adoption risk, and implementation cost to choose the highest-leverage next version.
Cross-functional execution
I translate customer needs into usable systems by holding business goals, operational workflows, technical constraints, and frontline realities in tension while building toward all of them.
Outcome orientation
I define success around the metrics that signal real change: conversion, cycle time, service recovery, recommendation quality, adoption, and operational reliability.
How I Work
My process is built around one conviction: good product work eliminates variables.
- Define the user problem.Identify where customers, operators, or teams are losing time, trust, money, or clarity.
- Validate with evidence.Use workflow observation, customer feedback, operational data, and constraints to confirm what matters.
- Choose a scoped solution.Compare options, name tradeoffs, define success criteria, and ship a focused first version.
- Ship for adoption and learning.Design around real users, monitor quality, and iterate based on behavior after launch.
Next Step
I've spent years building systems inside operations. Now I'm bringing that judgment to product.
Based in Las Vegas and actively pursuing product management and operations leadership roles where customer insight, systems thinking, AI fluency, and cross-functional execution matter.