Bronze Digital Product Design · 2026
Nomnow AI Calorie Tracker
by Meiqi Zhao
- A' Design Award 2026 Bronze Winner in Digital Product Design
- Awarded April 15, 2026
- CLIENT: NomNow
- Estimated Number of Design Views: 13,323
- Verification code 84B066B503 · Verify this award
About Nomnow AI Calorie Tracker
NomNow is a calorie tracking application for mobile and desktop devices. The application accepts voice, image, and text input, so meals can be logged in the way that suits each situation. Artificial intelligence identifies the food and calculates the nutrition values, which removes manual searching and form filling. The screens include daily calorie intake, a logging page, a calendar of past entries, and a weight change view. NomNow runs on iOS, iPadOS, and macOS, and aims to make nutrition monitoring simple and easy to sustain.
Meiqi Zhao's Nomnow AI Calorie Tracker represents a thoughtful contribution to contemporary health technology interface design, demonstrating how digital wellness tools can employ emotionally intelligent visual communication to transform nutritional monitoring from clinical data recording into supportive daily companionship. The three-screen presentation reveals a sophisticated understanding of behavioral design principles, wherein chromatic variation, mascot-driven feedback, and carefully calibrated messaging work in concert to provide users with immediate comprehension of their caloric consumption relative to established goals while maintaining consistent encouragement across diverse behavioral states. The interface architecture displays notable compositional achievement through its clear vertical information hierarchy, moving systematically from temporal orientation through dominant status visualization to granular macronutrient breakdown and detailed meal logging, ensuring users can extract relevant information at multiple levels of specificity without cognitive overwhelm. The designer demonstrates particular chromatic sophistication in deploying psychologically resonant color associations, employing energizing lime green and cheerful chartreuse to celebrate achievement and proximity to goals, calming cerulean blue to offer gentle encouragement when calories remain available, and attention-focusing coral pink to signal overconsumption, all while avoiding punitive or shame-inducing visual language. This chromatic storytelling operates within established color psychology frameworks while maintaining accessibility through sufficient contrast ratios and avoiding reliance on color as the sole information carrier, as evidenced by the complementary textual messaging and numerical precision accompanying each state. The mascot characters represent a significant formal achievement, their simplified organic geometry and varied emotional expressions creating empathetic connection points that humanize quantitative data, transforming abstract numbers into relational feedback from a supportive companion rather than impersonal system output. The rounded corners, soft edges, and approachable scale of these characters suggest friendliness and accessibility, deliberately avoiding authoritative or clinical associations that might trigger resistance or negative self-judgment in users navigating the complex emotional terrain of dietary awareness. The technical execution demonstrates fluency in contemporary flat design principles while incorporating subtle depth cues through layered cards, measured drop shadows, and scale variation that guide visual scanning without requiring skeuomorphic ornament. Typography choices support the overall accessibility mission through clean sans-serif letterforms, generous sizing for primary data, and consistent alignment creating visual stability. The progress bar visualization proves particularly effective, employing proportional representation with clear chromatic fill states that communicate achievement or excess at a glance while maintaining precision through accompanying numerical display, addressing both immediate emotional registration and detailed analytical needs. The persistent bottom navigation with its prominent chartreuse circular button suggests frequent interaction patterns, making primary actions immediately accessible while secondary functions remain available through flanking icons. The weekly timeline at the top of each screen, with its colorful circular indicators, provides temporal context and pattern awareness, enabling users to understand single-day data within broader behavioral trajectories without requiring navigation away from the primary view. This interface seemingly draws upon traditions of gamification and behavioral psychology, understanding that sustained engagement with health monitoring requires emotional reward structures, clear immediate feedback, varied stimuli to maintain interest, and above all, messaging frameworks that support rather than judge, encourage rather than criticize. The work invites consideration of how digital health technologies might evolve beyond mere data capture toward genuine partnership in behavior change, asking viewers to reflect on the role of emotional intelligence in interface design and the potential for visual communication to make wellness practices feel supportive rather than surveillance-oriented, empowering rather than restricting, and sustainable through daily emotional satisfaction rather than transient willpower.
Press Kit
- Unique Properties
- Tired of tracking calories with tedious manual input? Try NomNow - just snap a photo or use a voice command, and AI does the rest. No more guessing portions or endless scrolling the food list. Powered by AI-driven voice, image, and text recognition, it accurately identifies food and calculates nutrition. With advanced RAG and LLM models, NomNow personalizes tracking, adapts to your habits, and provides smart insights, making healthy eating effortless. Now live on the App Store with 5,000 users.
- Dimensions & Packaging
- NomNow is available on iOS, iPadOS, and macOS, offering a seamless experience across Apple devices. The app is optimized for Apple Silicon and supports multimodal AI interaction using voice, image, and text input. It leverages RAG and LLM models for high-precision calorie tracking. The Apple Watch version is currently in development, aiming to provide real-time health insights and a more integrated nutrition tracking experience.
- Tags
- Health, Diet, AI, Calorie tracker, LLM, Weight loss, NomNow, Multimodal interaction, AI agents
- Production Technology
- NomNow leverages multimodal AI, integrating voice, image, and text recognition for seamless calorie tracking. Powered by Retrieval-Augmented Generation (RAG) and Large Language Models (LLM), it ensures high-accuracy food recognition and adaptive calorie estimation. AI agents refine user inputs, personalize recommendations, and build a dynamic nutrition database. NomNow transforms nutrition logging with context-aware, intelligent tracking, making it effortless and precise.
- Design Challenge
- Making AI capacities more user-centered and humanized.
- Project Duration
- June 2024 - Present
Launched on App Store in December 2024. - Operation Flow
- Log in / Sign up pages
User guidance page
In-app purchase page
Setting weight loss goals page
Home page - calorie intake today
Calorie Tracking page - logging via voice / photo / text
Calendar page - your calorie intake history
Weight change page - focus on your health goal - Research
- NomNow is now live on App Store with around 5000 users.
- Inspiration
- Manually tracking meals is frustrating - estimating portion sizes, recalling meals, and inputting data takes time. As a calorie-tracking app user, I felt these pain points firsthand. With advances in multimodal AI, AI agents, LLMs, and RAG, I saw the opportunity to redefine calorie tracking. No existing product seamlessly integrates voice, image, and text for effortless nutrition logging, so I built NomNow to make tracking smart, intuitive, and hassle-free.
- Patents
- Copyrights belong to NomNow, 2026
Colors of Nomnow AI Calorie Tracker
Grand Interview about Nomnow AI Calorie Tracker
The defining insight came from observing how unnatural traditional nutrition tracking feels in real life. People do not experience food as a spreadsheet. They experience it through memory, appetite, social context, photos, conversations, and small daily decisions. Yet most calorie-tracking products still ask users to manually search, type, select, and correct information in a very rigid flow.That disconnect convinced me that a multimodal approach was not just a technical feature, but a more natural interaction model. A user might take a photo of a meal, describe what is inside, correct a portion size by voice, or ask a follow-up question in text. These are not separate behaviors; they are how people already communicate. The product needed to follow that human rhythm.When we started building NomNow, the practical reality was more complex than the vision. Food recognition is difficult because meals are culturally diverse, visually ambiguous, and often mixed together. A single image may not reveal ingredients, cooking methods, or portion sizes. That is exactly why the multimodal strategy held up. Image input could provide visual context, voice and text could capture missing details, and the conversational layer could help the system clarify uncertainty instead of pretending to be perfectly accurate.In that sense, the original vision became stronger through implementation. We learned that the goal was not to make AI look magical. The goal was to make nutrition tracking feel less burdensome, more forgiving, and closer to how people naturally think and talk about food.
The central design challenge was to make advanced AI feel invisible without making it feel simplistic. We wanted users to benefit from machine learning, multimodal recognition, and adaptive estimation, but we did not want the product to feel like a technical demo.Our approach was to design the experience around the user’s daily routine, not around the model’s capabilities. Instead of asking, “How do we show everything the AI can do?”, we asked, “What is the smallest interaction that helps someone log a meal with confidence?” That shifted the design from feature exhibition to behavioral simplicity.The conversational flow became important here. A traditional tracker often makes users do the work upfront: search for food, choose from long lists, estimate portions, and manually adjust nutrition data. In NomNow, the AI carries more of that burden. The user can start with whatever input is easiest in that moment: a photo, a spoken description, or a short text entry. The system then structures the information, asks for clarification when needed, and turns a messy real-life meal into a usable nutrition record.We also tried to preserve lightness. Health products can easily become judgmental or overly clinical. For a daily-use product, the tone has to feel supportive, not punitive. The interface needed to be clear, calm, and conversational, so users could return to it without feeling that tracking was another source of pressure.For me, the sophistication of the product is not in showing complexity. It is in absorbing complexity on behalf of the user.
I believe NomNow’s use of Retrieval-Augmented Generation and Large Language Models contributes to preventive health by lowering the cost of participation. Many people understand that food awareness matters, but they do not maintain calorie tracking because the process is too manual, repetitive, and mentally demanding.RAG and LLMs help change that relationship. Retrieval can ground food information in more reliable nutritional references, while language models can interpret the messy, human way people describe meals. Together, they allow the product to move beyond a static database search and become a more adaptive assistant.This matters because preventive health often fails not from lack of intention, but from friction. A user may not want to weigh every ingredient, search through multiple entries, or correct a database every day. If the system can understand a meal from a photo, a casual description, and a short clarification, then tracking becomes much easier to sustain.I also think this approach makes health technology more accessible. People have different levels of nutrition knowledge, different cultural food backgrounds, and different relationships with dieting or wellness. A conversational AI system can meet users closer to where they are, instead of forcing everyone into the same rigid tracking model.The broader shift is from health tracking as self-discipline to health tracking as supported awareness. The product does not need to shame users or demand perfection. It can help them notice patterns, make small adjustments, and build a more sustainable relationship with food over time. That is where I see the real social value of AI in preventive health.
Winning the Bronze A’ Design Award in the Digital Product Design category is meaningful because it validates the core intention behind the Nomnow AI Calorie Tracker: to make nutrition tracking more accessible, intuitive, and genuinely usable in everyday life.From the beginning, we did not want to build another calorie tracker with a more advanced technical layer. We wanted to rethink the interaction model itself. Traditional tracking often places too much burden on users: they need to search manually, estimate portions, correct entries, and repeat the same process every day. Our goal was to reduce that burden through a conversational, multimodal experience that allowed people to log food through image, voice, and text in a more natural way.The award recognition reflects not only the product’s interface innovation, but also its user-centered thinking. It confirms that accessibility and emotional ease can be as important as technical sophistication. For a health product, the real design challenge is not simply accuracy; it is whether users can sustain the behavior over time.This validation has shaped how I think about the next phase of development. It encourages us to continue focusing on trust, personalization, and long-term habit formation. Instead of adding features for the sake of complexity, the next phase should deepen the product’s ability to understand users’ routines, food cultures, goals, and constraints, while keeping the experience light enough to become part of daily life.
One important turning point was deciding not to make the AI’s output feel overly definitive. In an early version of the product, it was tempting to let the system recognize a meal and immediately present a polished calorie result. That would have made the AI look impressive, but it would not always have been the most honest or useful experience.Food recognition is inherently uncertain. A photo may show what a dish looks like, but not the exact ingredients, portion size, cooking method, oil content, or hidden sauces. If the product pretended to know everything from one image, it could damage user trust.So we made a design decision to treat AI as a collaborative assistant rather than an unquestionable authority. The system could provide an initial estimate, but it also needed to invite clarification in a lightweight way. A user could correct the portion, add missing ingredients, or describe the meal in their own words. This made the experience less flashy, but more human and more reliable.That process taught me that thoughtful AI design is not about maximizing automation at every step. Sometimes the best design is to preserve a small amount of human input in the right place. The goal is not to remove the user from the process entirely, but to reduce the tedious parts while keeping them in control of meaningful decisions.For me, this revealed a larger principle: AI capability becomes valuable only when it is shaped by trust, context, and humility.
The origin of Nomnow AI Calorie Tracker came from my own frustration with traditional calorie logging. I had tried using nutrition-tracking apps, but the experience often felt repetitive, rigid, and mentally tiring. The hardest part was not understanding that nutrition mattered; it was maintaining the habit when every meal required manual search, selection, correction, and estimation.That firsthand experience shaped the early design language of the product. I wanted the interface to feel less like a database and more like a conversation. Instead of building the information architecture around food categories and nutrition tables first, we built it around real user behavior: taking a photo, describing a meal casually, asking questions, correcting details, and moving on.This also shaped the interaction model. The product needed to accept imperfect, incomplete input because that is how people behave in daily life. Users may not know the exact name of a dish, the weight of an ingredient, or the calorie count of a meal. The system had to support approximation, clarification, and gradual refinement.The product began to feel distinct when the experience shifted from “logging data” to “being understood.” That was the key difference. Existing calorie trackers often ask users to adapt to the structure of the app. Nomnow aimed to adapt to the way users naturally communicate about food.At that point, the product was no longer just a calorie tracker with AI features. It became a more conversational and forgiving way to build food awareness.
Over the next few years, I see Nomnow AI Calorie Tracker evolving from a meal-logging tool into a more continuous and personalized health companion.The conversational interface will remain central because it lowers the barrier to interaction. People are more likely to maintain a habit when they can engage with a product naturally, without feeling that every action requires effort. In the future, I imagine the product becoming better at understanding context: a user’s routine, dietary preferences, health goals, cultural food habits, and emotional relationship with eating.Personalization will also become more important. Calorie tracking should not be the same experience for everyone. Some users may want weight management, some may want muscle gain, some may need better awareness of eating patterns, and others may simply want to understand their meals without judgment. The product should adapt to those different needs without becoming overwhelming.Wearable integration is a natural next step. Nutrition does not exist in isolation. It connects with activity, sleep, heart rate, stress, and daily rhythm. By connecting food data with wearable signals, Nomnow could help users understand patterns more holistically, such as how meals relate to energy, recovery, or long-term health behavior.The broader potential is to make preventive care more practical. Many health tools ask users to become highly disciplined before they can benefit. I think the future of Nomnow is to do the opposite: meet users where they are, reduce friction, and turn small daily actions into useful health awareness over time.
What I hope receives wider recognition is not only the technical novelty of the Nomnow AI Calorie Tracker, but the design shift behind it: moving health tracking from a rigid act of self-monitoring toward a more conversational form of self-awareness.Many wellness products assume that users fail because they lack discipline. My experience suggested something different. Often, people already care about their health, but the tools demand too much effort, too much precision, and too much emotional tolerance. Traditional calorie tracking can make users feel judged, interrupted, or exhausted. The design opportunity was to reduce that burden without reducing the value of the insight.Through Nomnow, I became more convinced that preventive health design should not only focus on motivation, but also on friction. If a product can make a healthy behavior easier, more forgiving, and more integrated into daily life, it has a better chance of creating long-term impact.I also think the project points to an important responsibility for AI design. AI should not simply make products more powerful; it should make them more humane. In this case, the value of AI is not that it replaces human judgment, but that it helps users start, continue, and understand their own behavior with less cognitive load.Within the design community, I hope more attention is given to this kind of invisible design work: the careful choices that make technology feel less intimidating, health behavior feel less punitive, and everyday self-care feel more sustainable.
From an interaction design perspective, the main challenge was to make the AI feel like it was listening, not interrogating. Food tracking is already personal. If the product asks too many questions, it becomes annoying. If it asks too few, it may feel inaccurate or careless. The design had to find the right rhythm between guidance and freedom.Replacing the traditional search-and-scroll database model meant changing the user’s mental model. Instead of asking users to adapt to a rigid database structure, we allowed them to begin with whatever felt easiest: a photo, a short description, a voice note, or a correction. The system then interpreted, organized, and clarified the meal in a conversational way.One important design principle was progressive clarification. The product should not demand perfect information at the beginning. It should first accept the user’s natural input, then ask only for the most important missing detail, such as portion size, ingredients, or preparation method. This made the process feel lighter and more respectful.What surprised me from early users was that people responded positively not only to convenience, but also to being understood. They appreciated that they did not have to know the exact database term for a food or manually break down every ingredient from the start. The conversational dynamic made the product feel more forgiving.That feedback confirmed that in AI product design, accuracy matters, but perceived understanding matters too. Users are more willing to build a habit when the system feels adaptive to them, rather than forcing them to behave like a machine.
The guiding principle was to design around real human effort, not around technical demonstration. An AI product may impress users once with a powerful feature, but people only return when the product reduces friction in a meaningful and repeatable way.For Nomnow AI Calorie Tracker, the visual language needed to feel calm, clear, and approachable. Health products can easily become visually overloaded with charts, numbers, warnings, and goals. We wanted the interface to support awareness without creating pressure. That meant using hierarchy carefully, keeping the flow lightweight, and making the AI interaction feel like part of the daily routine rather than a separate technical process.The feedback mechanism was also important. Instead of presenting AI results as final answers, the product needed to make correction feel normal. If users adjusted a portion size or clarified an ingredient, that should not feel like the AI failed. It should feel like a natural collaboration between the user and the system.For multimodal input, the principle was flexibility. Different situations require different inputs. A user may take a photo when eating out, speak when their hands are occupied, or type when they want privacy. The product should not force one “correct” mode of interaction. It should support the easiest path in each context.What I would want an aspiring digital product designer to understand is that the difference between an impressive AI product and a useful one often comes down to restraint. You do not need to expose every capability. You need to decide what the user should feel, what they should not have to think about, and where human control still matters.Good AI design is not about making the machine look intelligent. It is about making the user feel more capable.