Location
Dubai
Industry
Artificial Intelligence (AI), Medical
Date
Oct 10, 2025
Tools
Notion Miro Figjam Figma
Location
Dubai
Industry
Artificial Intelligence (AI), Medical
Date
Oct 10, 2025
Tools
Notion Miro Figjam Figma
The Client
Gini offers a highly personalized approach to health, nutrition, and wellness. By combining genetic data, lifestyle inputs, and more than 200 additional data points, Gini creates dynamic, vegan, bioavailable smart multivitamins that adapt to a user’s changing needs. The platform also provides gene-adjusted nutrition targets, a personalized nutrition label, lifestyle tracking, and an AI health assistant so people can take confident, daily control of their health.
The Client
Gini offers a highly personalized approach to health, nutrition, and wellness. By combining genetic data, lifestyle inputs, and more than 200 additional data points, Gini creates dynamic, vegan, bioavailable smart multivitamins that adapt to a user’s changing needs. The platform also provides gene-adjusted nutrition targets, a personalized nutrition label, lifestyle tracking, and an AI health assistant so people can take confident, daily control of their health.
The Challenges
Gini already had a strong core service, yet it needed a dedicated Analysis Screen that could bring together health reports, personalized recommendations, and actionable plans in one coherent place. The brief extended beyond a single screen into a broader experience that allowed users to track calories, weight, BMI, stress, and anxiety, consult a doctor, and join health programs without friction. At the same time, the onboarding journey was longer than ideal, and the team wanted to make everyday tracking of food, weight, medicines, and consultations feel simple and habitual.
The Challenges
Gini already had a strong core service, yet it needed a dedicated Analysis Screen that could bring together health reports, personalized recommendations, and actionable plans in one coherent place. The brief extended beyond a single screen into a broader experience that allowed users to track calories, weight, BMI, stress, and anxiety, consult a doctor, and join health programs without friction. At the same time, the onboarding journey was longer than ideal, and the team wanted to make everyday tracking of food, weight, medicines, and consultations feel simple and habitual.
Our Approach
We began by defining a clear information architecture that aligned features with product vision and clarified how users move across the experience. Next, we mapped flows for new and existing users and for doctors, ensuring that everything from onboarding to appointment booking felt intuitive.
The work progressed from competitor research to sitemap and wireframes, into a refined UI and design system. We validated the experience through internal live testing to confirm clarity and cohesion.
Our Approach
We began by defining a clear information architecture that aligned features with product vision and clarified how users move across the experience. Next, we mapped flows for new and existing users and for doctors, ensuring that everything from onboarding to appointment booking felt intuitive.
The work progressed from competitor research to sitemap and wireframes, into a refined UI and design system. We validated the experience through internal live testing to confirm clarity and cohesion.
What We Did
We designed the Analysis hub to unify insights and actions so users don’t have to hunt across sections. Navigation and content hierarchy were refined so logging, reviewing insights, connecting with a doctor, and exploring programs are always close at hand.
We completed end-to-end flows for onboarding, consultations, booking and payment, and shopping—reducing cognitive load across the journey.
What We Did
We designed the Analysis hub to unify insights and actions so users don’t have to hunt across sections. Navigation and content hierarchy were refined so logging, reviewing insights, connecting with a doctor, and exploring programs are always close at hand.
We completed end-to-end flows for onboarding, consultations, booking and payment, and shopping—reducing cognitive load across the journey.
Design Process
The design process focused on transforming health data into an experience that feels both structured and engaging. The Dashboard served as a central hub for logging health details, tracking calories, medicines, exercise, and weight, while also enabling connections with doctors and providing personalized health tips.
The Log Food feature introduced AI-powered photo logging, turning images into calorie estimates and offering smart food recommendations.
Design Process
The design process focused on transforming health data into an experience that feels both structured and engaging. The Dashboard served as a central hub for logging health details, tracking calories, medicines, exercise, and weight, while also enabling connections with doctors and providing personalized health tips.
The Log Food feature introduced AI-powered photo logging, turning images into calorie estimates and offering smart food recommendations.
Revolutionizing AI Assistant
With My Routine, the system automatically built a personalized daily plan from lifestyle and medication inputs, while still allowing full customization and tracking.
Finally, the Gini AI Assistant became the standout feature, guiding users with reminders, conversational answers, and actionable health plans. These combined elements created a seamless and motivating daily companion.
Revolutionizing AI Assistant
With My Routine, the system automatically built a personalized daily plan from lifestyle and medication inputs, while still allowing full customization and tracking.
Finally, the Gini AI Assistant became the standout feature, guiding users with reminders, conversational answers, and actionable health plans. These combined elements created a seamless and motivating daily companion.
Testing & Iteration
We ran internal testing prior to launch to validate flow clarity, integration points, and micro-interactions. Feedback helped us tighten copy, confirm task success paths, and polish empty states.
Testing & Iteration
We ran internal testing prior to launch to validate flow clarity, integration points, and micro-interactions. Feedback helped us tighten copy, confirm task success paths, and polish empty states.
The Result
Early usage indicated that the Gini AI Assistant resonated strongly with users, who preferred getting insights and logging through conversation rather than manual entry. This behaviour shaped our recommendations for future iterations that emphasize assistant-led guidance and low-friction logging.
The Result
Early usage indicated that the Gini AI Assistant resonated strongly with users, who preferred getting insights and logging through conversation rather than manual entry. This behaviour shaped our recommendations for future iterations that emphasize assistant-led guidance and low-friction logging.
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