ModPro

Building a garage that fits in your pocket

Image of the ModPro application showing a Dodge Challenger profile

Picture the late-night garage hum: sockets clink, a phone screen smudged with grease, and a proud “before/after” shot. That’s the moment ModPro wanted to honor—a place where car folks could track every modification, show their builds proudly, and get smart, personal ideas for what to do next. Our job: turn that love into a product that feels fast, friendly, and trustworthy.

Image of the user profile within the ModPro app design
Image of the feed screen of the ModPro mobile application design
Image of the ModPro AI functionality application design

THE CHALLENGE
A one-year engagement focused on discovery and UX/UI to define and design ModPro’s first release: a mobile-first product where users create a “garage,” track every modification, share builds, and preview an AI-powered planner. Success meant a clear MVP, a tested flow that anyone could use on the first try, and a design system that would support Phase 2 (the recommendation engine).


What we made (and why)
We designed a compact information architecture around four places people naturally go: garage (all cars), vehicle profile, mod list (installed and planned), and activity (shares, comments). The mod composer is the heart: scan or search a part, snap photos, tag cost/time, and save—fast. We wrapped it in small kindnesses: auto-save drafts, offline capture, clear “share vs. private” toggles, and side-by-side before/after photos that feel like a mini victory lap.
“Log it before the hood cools.” One tap to capture, one tap to brag—done.

How we kept it human—and fast

Capture first, sparkle second.
We prioritized a “two-tap log” over flashy feeds → mod tracking got fast enough to use with gloves on, and the feed still feels proud—just not in the way.

Parts as data, not decoration.
We structured every entry (part, vendor, cost, install date, mileage, notes, photos) → cleaner profiles and a reliable foundation for AI suggestions later.

Opinionated defaults
Pre-filled vendors, common categories, and price ranges → fewer abandoned entries and more consistent data across users.

Results (and what’s next)
Greenstead left our engagement with a clear story, a usable MVP, and developer-ready tickets for the next release. Early walkthroughs with chefs and growers validated the flow and surfaced the right next features (standing orders; route batching). The company later closed due to the pandemic’s devastating effect on the restaurant industry and upstream providers; the brand, working product, and backlog continue to live on as reusable patterns for future farm‑to‑restaurant tools.
Before → After: Texts, spreadsheets, guesswork → one place to browse, order, and confirm with receipts that match the truck.

Image of the AI Tuning Advisor application design