Roughly one third of all food produced globally is wasted. For restaurants, unsold inventory at closing time represents both a financial loss and an environmental cost. 90% of that wasted food is perfectly edible. Yet no accessible, localized solution existed to connect surplus food with nearby users in real time.
The challenge had two sides. For restaurants, managing surplus food was reactive and manual, with no existing tool making it easy to list, price, and fulfill last-minute inventory in under 2 minutes. For users, discovering affordable nearby food required switching between multiple apps with no guarantee of availability or freshness.
Problem Statement How do we make it effortless for restaurants to offload surplus food and for nearby users to discover and claim it before it becomes waste?
What made this hard Two-sided marketplace design means every decision made for one side has a direct implication for the other. Designing either side in isolation was never an option. The handoff between restaurant and diner had to feel seamless for both.
I conducted 6 interviews with frequent food delivery users and 4 interviews with restaurant staff, supplemented by a survey of 50+ respondents to validate assumptions around price sensitivity and discovery behavior.
Key findings from the research:
Restaurants had no easy way to offload surplus food Hotels actively track food waste averaging 160-180g per person. Buffet restaurants always have 10-15 portions of leftover food per dish. Most discard leftovers, some reuse or take them home, but none donate due to safety concerns around preparation and storage times. Businesses wanted guidance on proper food disposal but had no scalable tool to act on it.
Time was the biggest barrier for restaurants Restaurant staff cited time as the primary blocker. Listing surplus food needed to take under 90 seconds or it simply wouldn't happen. Any flow longer than that would be abandoned during busy closing periods.
Users prioritized proximity and price over brand Discovery needed to lead with distance and deal value, not restaurant name. Users wanted to know food quality and pickup windows before committing. Trust was a significant friction point.
Discounted food carried subtle stigma An unexpected finding from empathy mapping: users felt mild guilt about buying discounted food, worried it signaled desperation. This reframed the messaging strategy from "save money" to "smart dining."
I reviewed industry reports and case studies to understand the systemic scale of the problem and identify market opportunities.
Key findings:
Overconsumption and waste depletes resources including water, labor, and energy at every stage of the food chain. One third of food is wasted before reaching consumers. Food waste emits methane, 25 times more potent than CO2. 84.7% of food waste ends up in bins instead of being repurposed.
Three numbers shaped the product strategy from the start:
4.4M tonnes of food wasted annually, establishing the scale of the problem
20K restaurants in target metro areas representing the supply side
137g average food wasted per person per day, the behavior being designed against
These numbers confirmed that the problem was systemic and that a solution needed to work at scale from day one. Existing solutions like Robin Hood Army and Pappadavada addressed food waste but didn't prioritize user experience. None offered real-time proximity-based discovery with a seamless restaurant-side listing flow. That gap was the opportunity.
Competitor Analysis Existing solutions addressed food waste but left two critical gaps: no translation of sustainability goals into a fast, intuitive user experience, and no streamlined listing flow for the restaurant side. Takeout's opportunity was at the intersection of speed, discovery, and simplicity for both sides of the marketplace.
Meet Manish A hotel owner in Bangalore struggling with large amounts of leftover food at the end of each workday. He's always looking for better strategies to reduce waste and handle leftovers more effectively, but every existing solution adds work to an already busy closing shift.
Manish's time constraint directly informed the decision to build a "Ready in X minutes" tag on every listing, reducing the need to open individual restaurant pages to check availability. If Manish can list in under 90 seconds and the diner can decide in under 10, the marketplace works.
Empathy Map Empathy mapping surfaced the unexpected insight that users felt mild guilt about buying discounted food, worrying it signaled desperation. This single finding reframed the entire messaging strategy. The product stopped communicating around saving money and started communicating around smart dining. Framing the same action differently changed how users felt about taking it.
User Journey Map Mapping the end-to-end journey for both the diner and the restaurant revealed a critical timing mismatch. Restaurants wanted to list surplus food at peak closing hours between 9 and 10pm while users were most active for discovery earlier in the evening. This tension directly shaped the notification and listing reminder system, prompting restaurants to list before the user demand window closed.
To ensure a seamless user experience I created an information architecture that organizes content intuitively for both sides of the marketplace.
The IA needed to serve two completely different mental models in one app. A diner opening Takeout wants to discover food near them right now. A restaurant owner opening Takeout wants to list something quickly before closing. The navigation structure had to make both paths feel like the obvious thing to do without either getting in the other's way.
I also created a Giga Map to document all research findings and organize them by section, giving me a birds-eye view of the entire project and helping identify which areas needed the most design attention before moving into wireframes.
Initial sketches explored three structural approaches: a feed-first model, a map-first model, and a search-first model.
User feedback from testing strongly favored the map-first approach for proximity-based discovery. The reasoning was clear: users wanted to see what was close to them right now, not browse a feed of options. The map put distance front and center and became the foundation for every iteration after.
I designed the onboarding flow with first-time users in mind, keeping the introduction to key features simple and approachable. New users shouldn't need to figure out how the app works. They should just see food near them and know what to do next.
Branding The Takeout brand needed to feel approachable, fresh, and trustworthy without feeling clinical or corporate. The visual system used warm, food-forward colors and clean typography to communicate both appetite appeal and reliability.
The brand had to work for two audiences simultaneously: restaurant owners who needed to trust the platform with their business and diners who needed to feel good about the food they were picking up. The color palette and identity were designed to bridge both.
Mid Fidelity Wireframes Moving into mid-fidelity, the restaurant listing flow became the primary focus. Early testing showed this was the highest-friction touchpoint and the most likely place the platform would be abandoned.
The key mid-fidelity decision was collapsing the 6-step restaurant listing flow into 3 steps by combining location, timing, and pricing into a single smart form. This reduced estimated listing time from 4 minutes to under 90 seconds, directly addressing the research finding that time was the primary barrier for restaurant participation.
Even without final visual polish, usability testing during this stage validated that users could navigate the platform and complete core tasks. The dark background was flagged as a readability issue and was addressed in high-fidelity. The core flow held up.
High-fidelity screens brought the full experience to life across both sides of the marketplace.
Map-First Discovery The map layout prioritizes proximity. A card stack at the bottom surfaces the three closest active listings without requiring any interaction, reducing time to first discovery for diners.
User Screens This flow shows how users can select and pick up mystery food bags from nearby hotels. They can filter by location, dietary preferences, and pickup times, favorite restaurants, learn how to compost food at home, view past orders, and chat with an AI bot for queries.
Hotel Screens This flow shows how hotels can upload food bundles, add details like filters and pickup times, track ingredient expiration dates, and access articles on food disposal. They can also view key stats including client data, sales, and pricing.
User Screens
This flow shows how users can select and pick up mystery food bags from nearby hotels through the app. They can filter options based on location, dietary preferences, and pick-up times, favorite restaurants, learn how to compost food at home, view past orders, and chat with an AI bot for any queries.
Hotel Screens
This flow shows how hotels can upload food bundles, add details like filters and pick-up times, track ingredient expiration dates, and access articles on food disposal. They can also view key stats such as client data, sales, and pricing.
I tested the high-fidelity prototype with participants across two rounds using both exploratory and task-based methods.
Round 1, 5 participants Identified confusion around the listing expiry system and unclear pickup window communication. Menu, journal, and dispose icons caused confusion. Navigation became easier with use and users liked the color scheme overall.
Round 2, 5 participants Validated the revised flow. Minor copy issues surfaced in the onboarding screen. Icon changes implemented based on Round 1 feedback were well received.
Key outcomes Task completion for food discovery improved from 64% to 91% between rounds. Restaurant listing flow improved from 71% to 94%.
Reflection Takeout taught me that two-sided marketplace design is fundamentally different from single-user product design. Every decision made for the diner experience had a direct implication for the restaurant experience and vice versa. The biggest challenge wasn't designing either side in isolation, it was designing the handoff between them.
I also learned that sustainability messaging needs to be secondary to utility. Users chose Takeout because it was convenient and affordable and the environmental benefit was a bonus, not a motivator. Designing around actual behavior rather than aspirational behavior made the product significantly more honest.
Given more time I would build out the restaurant analytics dashboard in detail, explore a subscription model for frequent users, and test a social sharing mechanic to drive organic discovery.