Where everyday grocery choices turn into healthier habits — effortlessly
Industry
Quick Commerce
Project goal
Demonstrate advanced UX capabilities, business awareness, and AI-aligned product re-design for a growing quick commerce platform.
Project Duration
Apr, 2025
Focus areas
UX Design, Product Thinking, AI Integration, Health-Tech, Grocery Commerce
Problem statement
In urban India, users frequently use quick-commerce platforms like Blinkit for their daily groceries. Data suggests users order more often on quick commerce apps than their local store. It is solely because these platforms prioritize…
"For health-conscious consumers, there’s
no guidance
on whether what
they're
buying aligns with their dietary goals, restrictions, or nutrition plans.
"
. . .
Objectives
Through early research and user conversations, it became clear that while users valued speed and convenience in quick-commerce platforms, there was a growing need for…
These objectives emerged from identifying this gap — to shift quick-
commerce from being just fast and reactive to being
smart, supportive, &
intentional
for the health-conscious urban shopper.
. . .
Design processes
Mapping Insights to Ideas, and Ideas to Real-World Behavior
To create a solution that truly fits user needs, I followed a structured design process — starting with real user inputs and gradually building toward testable ideas.
Simple Survey & Insights
To understand how users approach grocery shopping and health, I crafted a short Google Form survey, shared across social media and messaging platforms. Though the response count was modest, the data gave strong directional insights. It helped validate user habits, pain points, and their openness to healthier shopping nudges — and ultimately shaped the core product direction.
The survey questions were framed to validate these assumptions and uncover real user behaviors and desires.
User Persona
To ground the experience in real user needs, I created a persona based on early assumptions — then refined it after speaking to a user who closely fit the profile and volunteered for feedback.
How Might We?
Translating user needs and insights to into early usable design ideas. Keeping this in my mind I began writing down "How might we" statements to begin setting a clear path towards an idea that is currently very broad.
Design Challenge & Exploration
There was no central place for users to explore healthier product options in a goal-based manner.
Blinkit lacked a structured way to surface nutrient-first product suggestions — especially for macros like protein or low sugar.
Users had no clear entry point to interact with an AI assistant — no prompt, no nudge, no visibility.
Discovery was left to chance — even users actively seeking better choices had to scroll endlessly.
There was no visual feedback loop between what's in the cart and what could improve it nutritionally.
Cart insights felt isolated — they didn’t naturally tie into product discovery or selection.
I reimagined discovery around health goals by embedding contextual intelligence into the browsing experience — surfacing better choices without breaking user flow.
A Friendly Nudge Upfront
Smarter Discovery, Not Disruption
Suggestions That Stick
Flexible Formats for Varying Intent
Conversational Discovery, Tailored by Nutri Assist
. . .
Reflections as a Designer
. . .
Skills Applied & IxDF Certifications in Action 🎓
By applying these skills, I designed a proactive nutrition guidance tool that blends behavior-aware suggestions with practical UX strategy. This case study reflects my ability to use AI to simplify decision-making, reduce cognitive load, and build contextual experiences at scale.
Used AI-assisted UX tools to surface snack suggestions based on user goals like "low sugar" or "high protein," improving relevance without needing explicit filters.
Enabled predictive interaction patterns, where Nutri Assist anticipated user needs before input, using goal-aware prompt design.
Applied AI-generated tagging to create dynamic food labels (e.g., low fat, high fiber), aiding quick comprehension and faster decisions.
Created snack suggestions that were personalized, contextual, and low-effort — with meaningful labels that reduced user decision fatigue.
Showcased how generative AI can enhance product utility, even in constrained grocery decision windows.
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Prioritized micro-moment UX, understanding that user attention is limited (e.g., while ordering quickly before checkout).
Designed for high urgency, low distraction flows, by keeping prompts minimal and feedback clear.
Brought accessibility and modularity into snack recommendations, so labels and prompts adapt as context evolves.
Improved snack discoverability during critical decision windows, increasing perceived helpfulness without slowing down the journey.
Enabled scalable nutrition education, surfacing value-added suggestions without cluttering the interface.
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Considered perception and memory limits — ensuring snack choices and prompts were easy to process without overwhelming the user.
Made deliberate use of visual hierarchy and repetition to improve snack card comprehension.
Users could recognize and recall nutritional categories (e.g., “high protein”) even in fast-scrolling scenarios.
Helped convert abstract health goals into visible, tappable choices.
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Disclaimer
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