Designing for Health
Designing for Health
turning hungerBox into a wellness partner
turning hungerBox into a wellness partner
The Brief
The Brief
Implementing a health layer inside HungerBox that uses AI and nutrition intelligence to help users make smarter, healthier food choices every day.
Implementing a health layer inside HungerBox that uses AI and nutrition intelligence to help users make smarter, healthier food choices every day.
The Challenge
The Challenge
Bring transparency and personalized guidance to cafeteria food-without adding friction to the fast, high-frequency ordering experience.
Bring transparency and personalized guidance to cafeteria food-without adding friction to the fast, high-frequency ordering experience.
The Impact
The Impact
Positioned HungerBox as a wellness partner-not just a cafeteria solution. Driving:
18% higher engagement
12% healthier food adoption
1.5 lakh AI scans
Positioned HungerBox as a wellness partner-not just a cafeteria solution. Driving:
18% higher engagement
12% healthier food adoption
1.5 lakh AI scans
A Cafeteria
Blind Spot
A Cafeteria
Blind Spot
Cafeterias in corporate India are designed for efficiency, not awareness. You stand in line, scan a menu, order fast, eat fast. What you don’t know? How many calories that biryani has, or if the paneer butter masala you eat daily pushes your cholesterol.
HungerBox had digitized ordering, payments, and feedback. But when it came to nutrition transparency, there was nothing. Users cared. Corporates cared. Vendors had the data buried in spreadsheets. But no one had put it together into something actionable.
This wasn’t just a missed feature. It was a missed opportunity to make HungerBox relevant beyond convenience.
Cafeterias in corporate India are designed for efficiency, not awareness. You stand in line, scan a menu, order fast, eat fast. What you don’t know? How many calories that biryani has, or if the paneer butter masala you eat daily pushes your cholesterol.
HungerBox had digitized ordering, payments, and feedback. But when it came to nutrition transparency, there was nothing. Users cared. Corporates cared. Vendors had the data buried in spreadsheets. But no one had put it together into something actionable.
This wasn’t just a missed feature. It was a missed opportunity to make HungerBox relevant beyond convenience.
a health conscious user trying to figure calories on the go
Listening to the
Health- Conscious User
Listening to the
Health- Conscious User
We began by talking to the people most frustrated with the blind spot.
Interviews: 20+ employees who tracked food manually with MyFitnessPal or Excel sheets.
Diary Studies: Asked users to record what they ate at work and how they tracked it.
Corporate Wellness Teams: Learned what companies were already offering (yoga sessions, health camps), but none touched daily food choices.
We began by talking to the people most frustrated with the blind spot.
Interviews: 20+ employees who tracked food manually with MyFitnessPal or Excel sheets.
Diary Studies: Asked users to record what they ate at work and how they tracked it.
Corporate Wellness Teams: Learned what companies were already offering (yoga sessions, health camps), but none touched daily food choices.
Everyday, I log into my health app to add calories, but it doesn't even have my cafeteria dishes.
Everyday, I log into my health app to add calories, but it doesn't even have my cafeteria dishes.
What's the point?
What's the point?
Partnering with
Nutritionists
Partnering with
Nutritionists
Numbers alone weren’t enough. We partnered with nutritionists and FSSAI experts to:
Map nutritional values for 500+ cafeteria items
Standardize serving sizes
Build a database of allergens and dietary restrictions (e.g., vegan, Jain food)
This gave us a scientific backbone. Without it, the feature would be dismissed as “gimmicky.”
Numbers alone weren’t enough. We partnered with nutritionists and FSSAI experts to:
Map nutritional values for 500+ cafeteria items
Standardize serving sizes
Build a database of allergens and dietary restrictions (e.g., vegan, Jain food)
This gave us a scientific backbone. Without it, the feature would be dismissed as “gimmicky.”
master menu management
allergen tags
Making it helpful,
not preachy
Making it helpful,
not preachy
Core design principles we followed:
Clarity over complexity → show calories and macros upfront, deeper data only if the user wants it.
Nudges, not nags → gentle prompts like “You’ve hit your protein goal, balance with veggies at dinner.”
Integrated, not separate → health woven into ordering, not a separate tab.
We prototyped three approaches:
Static labels (always-on calorie numbers)
Expandable overlays (tap to see macros)
Smart recommendations (suggested add-ons like curd, salad)
User testing showed a hybrid worked best: upfront calories + optional deeper dive.
Core design principles we followed:
Clarity over complexity → show calories and macros upfront, deeper data only if the user wants it.
Nudges, not nags → gentle prompts like “You’ve hit your protein goal, balance with veggies at dinner.”
Integrated, not separate → health woven into ordering, not a separate tab.
We prototyped three approaches:
Static labels (always-on calorie numbers)
Expandable overlays (tap to see macros)
Smart recommendations (suggested add-ons like curd, salad)
User testing showed a hybrid worked best: upfront calories + optional deeper dive.
expandable menu card to show macros clearly
calorie tags & AI suggestions
The AI Magic:
Scanning Plates
The AI Magic:
Scanning Plates
The boldest feature was AI food recognition. Users could snap a photo of their plate, and the model would:
Detect dishes
Estimate portion sizes
Fetch macros from our database
This wasn’t easy. Cafeteria food is messy, lighting is poor, plates overlap. Our data science team trained the model on thousands of real cafeteria images (not Instagram-perfect photos).
We kept the UX grounded: users could correct dish names or portion sizes if AI guessed wrong. This transparency built trust in the machine.
The boldest feature was AI food recognition. Users could snap a photo of their plate, and the model would:
Detect dishes
Estimate portion sizes
Fetch macros from our database
This wasn’t easy. Cafeteria food is messy, lighting is poor, plates overlap. Our data science team trained the model on thousands of real cafeteria images (not Instagram-perfect photos).
We kept the UX grounded: users could correct dish names or portion sizes if AI guessed wrong. This transparency built trust in the machine.
camera detects dishes and estimated portion size & macros
Implementation
Challenges
Implementation
Challenges
Messy menus: We built a master menu management system (parent-child mapping, allergens, nutrition inheritance) to standardize dishes across 600+ cafeterias.
Vendor adoption: Had to train vendors to update menus with accurate nutrition info.
Privacy concerns: Ensured data storage was compliant, and all health insights stayed user-side.
Messy menus: We built a master menu management system (parent-child mapping, allergens, nutrition inheritance) to standardize dishes across 600+ cafeterias.
Vendor adoption: Had to train vendors to update menus with accurate nutrition info.
Privacy concerns: Ensured data storage was compliant, and all health insights stayed user-side.
our master menu was messed up on whole another level
vendors needed to be trained with nutritional info
Rollout &
Adoption
Rollout &
Adoption
We piloted the module with 3 corporates (20k+ employees). To drive adoption, we:
Launched “Know Your Food Week” campaigns in cafeterias
Added gamification badges (“Balanced Plate”, “Protein Champ”)
Partnered with corporate HR to integrate into wellness challenges
We piloted the module with 3 corporates (20k+ employees). To drive adoption, we:
Launched “Know Your Food Week” campaigns in cafeterias
Added gamification badges (“Balanced Plate”, “Protein Champ”)
Partnered with corporate HR to integrate into wellness challenges
Impact
Impact
Within the first 3 months:
Within the first 3 months:
18% higher engagement
18% higher engagement
in the app's ordering journey
in the app's ordering journey
12% increase
12% increase
in healthier dish sales in pilot cafeterias
in healthier dish sales in pilot cafeterias
20+ corporates
20+ corporates
requested access for their wellness programs
requested access for their wellness programs
know your food week was organised to boost the adoption
wellness challenges were organised to increase awarness
Reflection: Designing beyond Orders
Reflection:
Designing beyond Orders
This was more than a feature. It was HungerBox’s first step into wellness design.
For users, it turned the cafeteria into a place of awareness. For corporates, it became a tool to prove investment in employee health. For HungerBox, it meant expanding from “ordering convenience” into “daily well-being.”
For me personally, it reinforced that AI and data mean nothing without empathy. A plate scanner is cool, but what matters is whether the user feels supported in their goals. Designing this module taught me to respect both science and sensitivity.
This was more than a feature. It was HungerBox’s first step into wellness design.
For users, it turned the cafeteria into a place of awareness. For corporates, it became a tool to prove investment in employee health. For HungerBox, it meant expanding from “ordering convenience” into “daily well-being.”
For me personally, it reinforced that AI and data mean nothing without empathy. A plate scanner is cool, but what matters is whether the user feels supported in their goals. Designing this module taught me to respect both science and sensitivity.
thank you!
keep exploring
thank you!
keep exploring
Bringing ideas
to life,
one product
at a time
Made with 🔥
Bringing ideas to life,
one product at a time
Made with 🔥