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BarTrack's core product monitors beer taps for bars. The NuCO2 partnership is a bet on much bigger territory — CO2 and soda equipment behind thousands of retail locations, starting with 7-Eleven, Whataburger, and Arby's. I was brought on as the sole product designer to turn that bet into a buildable platform: running the stakeholder sessions, designing all four personas end-to-end, and building AI-assisted dev ready prototypes.
"A manager doesn't know if they need to call the CO2 supplier, equipment repair, or Coca-Cola — and calling the wrong one wastes a full day."— from field stress-testing sessions
"The sensors-reporting check isn't a suggestion — it's a gate. A tech doesn't leave until the system confirms it." — stress-test decision, confirmed by field ops
Instead of static decks or free-form whiteboarding, I built each persona's flow as a rapidly-assembled interactive tool — drag steps between phases, double-click any card to edit, tag it MVP / integration point / open question. Built in Claude from interview notes and best-guess assumptions, then corrected live in the room. It turned alignment meetings into working sessions: NuCO2 and BarTrack could argue about a specific card instead of a vague impression, and open questions got tracked as data instead of getting lost after the call ended.
→ Open the interactive flow toolA full service blueprint threaded the store manager's experience — onboarding through alert response through resolution — against three backstage lanes: the BarTrack platform, the "BarTrack system" lane (labeled explicitly as the sensors & AI detection engine reading pressure and pour data in real time), and NuCO2's own dispatch systems. Naming that sensor/AI layer explicitly — instead of leaving it implied — kept the team honest in reviews about what was actually automated versus what only looked automated from the front end.
→ Open the interactive blueprintCorporate wanted fleet-wide uptime visibility; in stress-test sessions, store managers pushed back that real-time alerts to corporate would read as discipline, not support. The design resolution: a grace-period model — local resolution gets a window before anything escalates upward — paired with a regional view that frames downtime in dollars protected, not violations logged. The same reframe carried into alerting generally: NuCO2 dispatch gets every alert at the same moment the store manager does, so the store manager's screen had to answer a different question than "what's broken" — it had to answer "is this already being handled, and what do I need to do locally."
→ Open the live map — zoom in, click a storeThe regional dashboard is a scan-and-triage tool — a fleet of hundreds reduced to what needs attention. A single store manager needed the opposite: depth on the one location they're standing in. Tank-by-tank CO2 levels by zone, syrup levels per flavor, per-station dispensing detail, a 30-day supply forecast, one-tap early reorder when the math doesn't work out — built phone-first, for someone checking between customers, not sitting at a desk. High-turnover store staff also weren't going to create accounts and remember passwords for a screen they'd open twice a shift, so access is QR-first: scan, see live status, no login required.
A separate, smaller ask came from NuCO2 itself: their own admin/ops team needed visibility that nothing on the BarTrack side was falling through the cracks — flagged jobs, onboarding status, tech support queue. That got a lightweight console of its own, not the focus of this case study.
→ Open the interactive store manager prototypeThe #1 identified challenge was topology mapping — tying each physical dispenser line to the right sensor during install, entirely by hand today. I designed and prototyped a computer-vision "scan & auto-map" flow directly in Figma Make: point the camera at the fountain, the AI reads each label and proposes a mapping with a confidence score per line, and a technician corrects anything it got wrong before confirming — with a full manual-entry fallback for when the scan doesn't hold at all. Live demo on the right: select sodas, scan, watch it map all 8 stations.
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The engagement closed with a research-validated backlog across four personas, a full service blueprint, interactive working-session flows, and AI-assisted working prototypes — the foundation NuCO2 and BarTrack are now building from as the platform moves from spec into engineering, and BarTrack's opening claim on a new market.
Field Operations Managers each manage roughly a dozen dealers. Walking into a meeting, they needed to know where a dealer was outperforming or slipping — fast. The existing answer was chasing numbers across multiple Tableau workbooks that didn't match each other.
"It's too many reports with too much information, and none of it seems to match the other reports."— A former FOM now at Nissan HQ
"I would love something that didn't just report the news, but actually helped them understand how to have a conversation about how to improve at a dealer." — A former FOM now at Nissan HQ
Forty-three candidate metrics came out of stakeholder sessions. Thirty were available without new engineering — that split became the MVP line. Within the 30, an IA exercise clustered them into groups a FOM would actually navigate by: Dealer Overview, Service Retention, Parts & Accessories, Operational Efficiency — and later, Marketing, added after FOMs asked for it.
A director's stock-ticker chart became the paradigm — a treemap heat map where each tile is a dealer, sized by revenue and colored red-to-green against benchmark. A click opens a scorecard benchmarked four ways at once: Retailer, Region, District, National. A Smart Insights banner surfaces the anomaly worth discussing — scoped deliberately narrow, nothing predictive until the modeling earned that trust.
→ Open full-screen in a new tabThe engagement closed with a research-validated design, IA, and roadmap — thirty metrics now, thirteen later, predictive insights only when the data justified them. After the engagement ended, I rebuilt it as a working AI-assisted React prototype, directing Claude the way I'd direct a developer — design decisions mine, implementation AI-augmented.
Brought in to rescue a failing project and a damaged client relationship — asked to design a world-class onboarding experience for this Big Tech client's Ad Platforms engineering org. Given my prior experience with this client, my strategic approach, and my ability to read and steady a difficult client situation, I was asked to take the work over — and rebuild the relationship along the way.
The pattern was consistent: new hires got their offer letter, then heard nothing for three weeks. By Day 1, whatever excitement they'd had was mostly gone.
"From setting up your laptop to meeting people, most of the onboarding experience is self-service."— Experienced IC
"I had only met my manager once briefly in the first week, but it was great to meet the team."— Inexperienced IC
The journey map tracked three personas at once — Inexperienced IC, Experienced IC, and Experienced Manager — from Post-Offer through Post-Core Onboarding. A manager's memory of "a great week 1" sat right next to a new hire's memory of "it took a while to be connected." Same event, two different stories.


With the gaps mapped, we prioritized opportunities on an effort vs. impact matrix — separating what could move the needle right away from what was worth a bigger investment.
New hires were greeted by name and walked through a five-section roadmap — from setting up their laptop to their first code review. Warm without being saccharine, unmistakably on-brand for the client without borrowing its consumer design language.
→ Open the interactive prototypeThe project lost funding as we were completing the designs — that happens in enterprise work, and no amount of good design insulates you from it. But the rigorous methodology, the deep understanding of user needs, and the relationship rebuilt with a difficult client made this one of the most valuable engagements of my career.
For over 50 years, Southwest ran on open seating — egalitarian, familiar, core to the brand. Ending it wasn't a UI update. It was a fundamental rethink of the experience, rolled out across web and mobile without disrupting an operation this size.
Eleven designers, four from Southwest's internal team. I led the group retrofitting seat selection into mWeb, iOS, and Android — designing for the full matrix of edge cases: lap children, unaccompanied minors, customers of size, companion passes, SWABIZ bookings. Every permutation organized into micro-journeys and accounted for before a line of code was written.
A clear breakdown of MVP enhancements and ownership — entry points, bags updates, modify flows, responsive breakpoints — kept nothing falling through the cracks across eleven designers working in parallel.


The internal UX team owned Business Acceptance Testing — until capacity became a constraint and our team pivoted to run it directly alongside Southwest. Unglamorous, and exactly the kind of work that separates a project that ships clean from one that ships and needs damage control.
The redesigned experience shipped: a new seat selection model, a modernized booking funnel, and a consistent look and feel that honored the Southwest brand while moving it decisively forward.



What we could control, we controlled well: a seat selection flow that works across web, iOS, and Android, handling the full matrix of edge cases on day one. What I'd do differently — push harder, earlier, for qualitative research with the customers most likely to feel the loss.
Ending open seating was unpopular, and the rollout drew real criticism — about the feel of the change, and about the new fare tiers. That decision wasn't ours to make; our job was to design the experience customers would actually use once it had been made. I don't want to hide behind that distinction, though.
Meet Mr. Ouch — your parent, your grandparent, someone tired and hurting who just wants to get home. It takes nurses, care coordinators, intake specialists, and physicians to get him there. Every one of them is a user of naviHealth's products, and the interface was serving none of them particularly well.
The original engineering ask was a "lift and shift" — a visual facelift, no structural change. Research showed workflows varied a lot by role: a field nurse and a centralized data-entry specialist navigated the same screen to do fundamentally different jobs. That insight became the case for a role-based redesign instead — a conversation that changed how UX was valued on the project.


The legacy layout forced an inefficient, non-linear scan just to complete basic tasks. Mapping the data users needed most against the F-Pattern — the natural eye-tracking path people take through dense interfaces — put the right data in the right place and cut cognitive load.
Low-fidelity wireframes kept users focused on layout and interaction, not visual polish still being developed in parallel — letting people give honest feedback on structure. Testing surfaced real efficiencies, like re-sequencing tasks to cut extraneous clicks — observations from the people doing the work, not designer opinions.



A user-centered approach increased trust in UX from both Product and Engineering. When I left naviHealth, the UX team was helping define product direction — not treated as an addendum to supply visual design.
No redesign since 2016. Years of uncoordinated stakeholder edits left a fractured, patchwork site — outdated content, broken navigation, no shared sense of purpose. A platform migration created the forcing function: redesign it, align internally, and build the governance to keep it from fragmenting again.
A team of six designers and researchers ran 31 one-on-one interviews with potential patients, reviewed survey responses from more than 70 physicians and nurses, and gathered input from a dozen business leaders — all within a few weeks. Clustering and affinity mapping produced seven grounded personas that gave the whole team a shared language.




An analytics deep-dive with HCA's Marketing Services team mapped what people were actually looking for and where they dropped off. A substantial share of traffic was people hunting for career content — buried several levels deep. They were finding it despite the site, not because of it. That reframed the whole priority list.


I facilitated a four-hour workshop walking stakeholders through all seven personas, running How Might We exercises, then live-sorting ideas with group dot-voting.
Grounded in the research and guided by the personas, the team produced three distinct directions — low-fi wireframes through mid-fidelity visual explorations — before landing on a serious modernization that stayed inside HCA's style guide.



A modernized homepage balancing patient, provider, and investor needs within HCA's existing style guide — a serious step forward that still feels unmistakably HCA.
The project delivered a research-validated, stakeholder-aligned foundation — interactive prototypes, a new IA, and the governance to keep things from fragmenting again. The lasting outcome was bigger: HCA moved from assumption-based web management to evidence-based design. The career-content finding is the clearest example — nobody would have guessed it. It came from data and listening.
I'm a "digital nomad" these days, driving across the country while picking up freelance work. I AI-built this shared trip calendar, then kept iterating from the road — new features shipped based on real feedback from the friends actually using it to track where I'll be.
→ Open Travel Dash in a new tabSquatchbot
Mitch's (slightly hairy) assistant