By Shradha
Consumer Food Ordering App
Role UX Design Lead
End-to-End UX · Product Strategy · Design Systems · User Research
Context
Busy professionals, students, and families increasingly struggle to cook at home due to time constraints and demanding routines. As a result, everyday food choices often shift toward convenience over health, impacting long-term well-being.
Maaish was envisioned as a home-cooked food ordering platform that connects consumers with independent home chefs - enabling access to fresh, healthy meals on a recurring basis. The challenge was not just food ordering, but designing an experience that supports repetition, trust, and ease for daily use.
Problem
Early exploration revealed that existing food ordering experiences were optimized for one-off restaurant orders, not for users who needed frequent, repeat meals with minimal effort during busy routines.
Why Existing Food Ordering Apps Don't Work for Daily Use
Discovery & Research approach
To understand user needs and align them with business goals, I led a structured discovery phase combining competitive analysis, user research, and stakeholder alignment. As the sole designer, I owned the entire research process and translated insights into actionable design strategy.
Research Goals & Methods
Analysis & Synthesis
Based on user research, I created user personas for different user groups for the app , and continued with their journey maps to help understand their tasks to reach end result on the app, to further determine the pain points and insights.
Key pain points identified -
Design Strategy & Principles
Based on research insights, I defined a strategy that prioritized speed, repetition, and trust - shifting away from exploration-heavy patterns toward task-oriented efficiency.
Core Design Principles
Strategic Focus Areas
Design leadership & Process
I established the UX design process, coordinated cross-functional collaboration, and aligned teams around user insights - shaping both product direction and design execution.
Solution & Design Approach
I translated the design strategy into concrete design decisions, creating flows and interfaces that directly addressed user pain points while establishing a scalable foundation for the platform.
Key Design Decisions
Before & After: Ordering Flow Transformation
AI-Assisted Feature Explorations
To further support intent and reduce decision fatigue, I explored AI-powered assist features that help users make faster, more confident choices.
Initial Paper Concepts
Execution & Concepts
These task flows map how users move through the app to complete their primary goals. Each flow was optimized to minimize steps and cognitive load based on user research insights.
Initial Wireframe
Information Architecture & Flows
I defined core journeys around high-frequency tasks, prioritizing efficiency over exploration. The work progressed from paper sketches to low-fidelity wireframes, validating structure and flow before moving into visual design.
I structured the app around three primary user tasks ensuring each screen directly supported one of these core goals.
User Task Flows
These task flows map how users move through the app to complete their primary goals. Each flow was optimized to minimize steps and cognitive load based on user research insights.
Mobile App Design mockups -
Usability testing
Early usability testing highlighted the need to surface repeat actions more clearly. Key improvements included better placement of repeat and recent orders, fewer steps to reorder meals, and refined navigation to surface frequent tasks earlier.
We conducted usability study for low fidelity prototype, and the findings were implemented to high fidelity design.
Finally, I created the high fidelity design and shared it with the set of users to incorporate the final feedback.
Usability study : Feedback 1 - Recent searches and repeat orders should be easy to locate
Outcome
The final experience delivered faster, repeat-friendly ordering, reduced effort for frequent users, clear navigation and quicker task completion, and a scalable foundation with room for AI-assisted growth.
