DheeCuit Case Study

Links: App Preview · dheecuit.com · Instagram · Facebook

My Role

• Defined product strategy and content model; set the bar for <90-second weekly planning.
• Drove end-to-end UX: IA, flows, component system, UX writing, accessibility pass.
• Built interactive prototypes (Figma + coded HTML/CSS/JS); integrated public recipe APIs.
• AI-assisted implementation of auth, storage, and Railway hosting; iterated quickly.

Overview of DheeCuit’s weekly strip and core flows.

Introduction & Objectives

Home → Weekly strip with quick replace.

The Challenge We Faced

• Meal planning is a chore; too many choices lead to decision fatigue and abandonment.
• Recipe apps optimize for browsing, not planning; leftovers rarely carry forward.
• People want a plan they’ll actually use—fast, favorites-first, and grocery-ready.

Project Objectives

• Reduce time-to-plan to under ~2 minutes.
• Favor “what you already like” to increase repeat usage.
• Make recipes grocery-ready; reduce waste with leftovers reuse.

What’s New (Oct 2025)

• AI Recipe Creator (home): Prompt → title, blurb, ingredients, steps in one card.
• Planner-first layout: Simple weekly strip with fast add/replace.
• Favorites accelerator: Save go-tos; drop into week in one tap.
• Shopping List v1: Combined, deduplicated list with check-off state and recipe tags.
• Consistent cards: Tight metadata (time, servings, diet), concise steps, unified descriptions/blurbs.
• Stabler deploys: Local ≈ prod parity (Vercel) for core flows.

Our Solution Approach

Design a single-strip weekly planner with one plan per day, fast add/replacement, and a simple “save to favorites” primary action. Keep cards consistent and scannable. Optimize the first run and weekly return loops.

Features & Functionality

1) AI Recipe Creator

Type a short prompt (“hearty chicken, 30 min, feeds 4”) → get a clean recipe card with title, blurb, ingredients, and steps. Add to the week or save to favorites.

Design Rationale: Jumpstarts planning when you’re stuck; outputs follow the same card model as curated recipes.

AI-generated card matches curated structure for seamless planning.

Weekly strip: fewer branches, faster completion.

2) Weekly Planner Strip

Seven slots with quick add/replace. Momentum over micro-choices; one plan per day is enough to get dinner “done.”

Design Rationale: Minimize decision points → reduce time-to-plan.

3) Favorites-First Planning

Pin go-tos and reuse them. The fastest path to a usable plan is what you already like.

Design Rationale: Reduces cognitive load; increases return usage and plan stickiness.

Favorites act as a planning accelerator.

Consistent metadata supports shopping and reuse.

4) Consistent Recipe Cards

Clear ingredients, concise steps, and unified descriptions/blurbs. Makes scanning and execution faster.

Design Rationale: Consistency improves comprehension and lowers bounce when switching sources.

5) Shopping List v1

One merged list with checkboxes and per-recipe source tags. Keeps progress persistent while planning and shopping.

Next: Ingredient normalization 2.0 (units/plurals) and auto-grouping (produce, pantry, dairy).

Deduplicated items and clear provenance make shopping faster.

Benefits & Impact

• Faster weekly planning with fewer decisions.
• Repeat usage driven by favorites and leftovers reuse.
• Grocery-ready recipes reduce waste and increase follow-through.

Implementation Considerations

• Smooth first-run (auth → first plan) and weekly return loop.
• Accessible color/typography; consistent cards and tap targets.
• Lightweight analytics; privacy-respecting tracking of plan completion.

Challenges & Mitigations

Decision Fatigue

Mitigation: Single weekly strip; fewer, better suggestions; favorites-first.

Inconsistent Recipe Data

Mitigation: Normalize metadata; standardize ingredient formats for shopping roll-ups.

Abandoned First Run

Mitigation: Default starter plan; minimal setup; obvious “Save to Favorites.”