September 15, 2025
Food App
RG App — "Good Rated" Food
Content
In cities where choice becomes noise, RG App makes eating well effortless. A subscription-first food app that curates only "4.5★+ "restaurants in dense markets (e.g., Manhattan); open the app, pick a monthly plan, and see today’s best near you. Less scrolling, more great meals—on repeat.
Only high-quality restaurants. Simple, predictable ordering.
Role: Product Designer (end-to-end: Research, Growth, IA, interaction/visual design)
Surface: Mobile app (iOS/Android) · Responsive web (MVP)
I created RG App around a simple promise: in places like Manhattan, where choice is noise, you shouldn’t have to sift through mediocre options to eat well. The product curates only 4.5★+ restaurants and turns dinner into a fast, confident decision. Instead of endless scrolling, members pick a plan (Lite, Standard, or Unlimited with fair-use etc), open the app, and get a small, vetted set of today’s best within realistic delivery windows. One tap later, food’s on the way—no second-guessing, no promo clutter.
The insight came from watching busy people bounce between apps: abundance created friction, not freedom. Ratings existed but weren’t operationalized into confidence + speed. So I treated quality as a first-class constraint and designed the experience like a system, not a feed—clear eligibility rules (rating floor, review recency, photo freshness), transparent fees and policies, and microcopy that answers the “should I complete this?” pause. Discovery becomes quiet and useful; the default “Today’s Pick” can be swapped with a single tap; dietary filters and allergen hard-excludes are built in.
To keep us honest, the prototype ships with a research plan, not just screens. I'll recruit Manhattan professionals, run short diary studies on decision fatigue, and moderate tasks that measure time-to-order, first-week activation, and post-meal satisfaction. On the growth side, onboarding maps location → cuisine likes → plan fit; small experiments test profile depth, CTA placement, and nudge timing. Lifecycle is gentle—streaks without anxiety, a daily “chef’s pick,” and a weekly quality digest when new 4.8★ entrants join.
Guardrails matter. Ratings can inflate, so the catalog requires a minimum review count and recent velocity; partners are re-audited; MOQs/lead times and surge logic are transparent; accessibility and performance budgets are part of the component specs. The goal isn’t to replace taste—it’s to remove busywork so taste wins more often.
Status: prototype is ready for a Discovery → Profile → First Order → Habit Loop walkthrough
Only high-quality restaurants. Simple, predictable ordering.
Role: Product Designer (end-to-end: Research, Growth, IA, interaction/visual design)
Surface: Mobile app (iOS/Android) · Responsive web (MVP)
I created RG App around a simple promise: in places like Manhattan, where choice is noise, you shouldn’t have to sift through mediocre options to eat well. The product curates only 4.5★+ restaurants and turns dinner into a fast, confident decision. Instead of endless scrolling, members pick a plan (Lite, Standard, or Unlimited with fair-use etc), open the app, and get a small, vetted set of today’s best within realistic delivery windows. One tap later, food’s on the way—no second-guessing, no promo clutter.
The insight came from watching busy people bounce between apps: abundance created friction, not freedom. Ratings existed but weren’t operationalized into confidence + speed. So I treated quality as a first-class constraint and designed the experience like a system, not a feed—clear eligibility rules (rating floor, review recency, photo freshness), transparent fees and policies, and microcopy that answers the “should I complete this?” pause. Discovery becomes quiet and useful; the default “Today’s Pick” can be swapped with a single tap; dietary filters and allergen hard-excludes are built in.
To keep us honest, the prototype ships with a research plan, not just screens. I'll recruit Manhattan professionals, run short diary studies on decision fatigue, and moderate tasks that measure time-to-order, first-week activation, and post-meal satisfaction. On the growth side, onboarding maps location → cuisine likes → plan fit; small experiments test profile depth, CTA placement, and nudge timing. Lifecycle is gentle—streaks without anxiety, a daily “chef’s pick,” and a weekly quality digest when new 4.8★ entrants join.
Guardrails matter. Ratings can inflate, so the catalog requires a minimum review count and recent velocity; partners are re-audited; MOQs/lead times and surge logic are transparent; accessibility and performance budgets are part of the component specs. The goal isn’t to replace taste—it’s to remove busywork so taste wins more often.
Status: prototype is ready for a Discovery → Profile → First Order → Habit Loop walkthrough
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