AI app for healthcare · no coding needed
Photo-based nutrition tracking and meal planning workspace
Reduce scattered logging and planning effort while keeping one reviewable nutrition record.
Made for: People managing daily nutrition who want one owned app instead of several tracking subscriptions
What it does for you
The problem
Meal logging, calorie targets, menu choices and meal planning live in separate apps, so records are split and guidance is inconsistent.
What it gives you
User-corrected nutrition estimates and plans
What you give it
Meal photostyped meal descriptionsmenu photosstated goalsdietary preferences
Build your own version of Meals.Chat, Nourri Ai and more
One app with what these 7 AI tools do, yours to keep and change: Meals.Chat, Nourri Ai, Calorieasy, CalorieCounter.Pro, QuitSugar, CalPulse, MealByMeal.
Everything these tools do, in one app
- Photo-based food logging Allows users to take a photo of their meal to automatically log it and estimate nutritional content.Found in Meals.Chat, Nourri Ai, Calorieasy and 3 more
- AI nutritional analysis Uses artificial intelligence to identify foods and estimate calories, macronutrients, and other nutritional information from photos or descriptions.Found in Meals.Chat, Nourri Ai, Calorieasy and 3 more
- Textual meal input Enables users to type a description of their meal to get nutritional estimates when a photo is not available.Found in Meals.Chat
- Personalized calorie goals Sets daily calorie targets based on individual user goals and preferences.Found in Meals.Chat, Calorieasy, CalorieCounter.Pro
- Macronutrient breakdown Provides detailed information on protein, carbohydrate, and fat content of meals.Found in Meals.Chat, Calorieasy, CalorieCounter.Pro and 1 more
- Interactive corrections Allows users to provide feedback and correct AI miscalculations to improve accuracy over time.Found in Meals.Chat
- User-friendly interface Offers a simple and intuitive design that makes tracking easy for users of all experience levels.Found in Meals.Chat, Nourri Ai, CalorieCounter.Pro and 1 more
- Stress-free tracking Promotes a positive and guilt-free approach to nutrition monitoring, focusing on progress over perfection.Found in Nourri Ai
- Automated meal logging Automatically timestamps and organizes meals into a timeline or calendar for easy tracking.Found in Calorieasy
- Hassle-free onboarding Provides a straightforward setup process to minimize guesswork and start tracking quickly.Found in Calorieasy
- Food database Includes an extensive database of foods with nutritional details for manual logging and reference.Found in CalorieCounter.Pro
- Progress monitoring Tracks and displays user progress over time with visual charts and summaries.Found in CalorieCounter.Pro, QuitSugar
- Fitness app integration Connects with popular fitness trackers and health apps to sync data and provide holistic wellness support.Found in CalorieCounter.Pro, MealByMeal
- Sugar tracking Estimates sugar content in foods and helps users monitor and reduce sugar intake.Found in QuitSugar
- Personalized challenges Offers motivational challenges to encourage users to meet dietary goals, such as reducing sugar.Found in QuitSugar
- Social collaboration Allows users to collaborate with friends for shared goals, accountability, and encouragement.Found in QuitSugar
- Menu parsing Analyzes photos of restaurant menus to provide estimated calories, macros, and nutrition facts for each dish.Found in CalPulse
- Healthier swaps Suggests alternative menu items or ingredient swaps to reduce calories or improve nutritional balance.Found in CalPulse
- Multilingual support Recognizes and processes menus in multiple languages, useful for travel and international dining.Found in CalPulse
- Meal planning Generates personalized meal plans based on dietary preferences and nutritional goals.Found in MealByMeal
- Automated shopping lists Creates shopping lists automatically from planned meals to streamline grocery shopping.Found in MealByMeal
- Recipe database Provides a collection of recipes with clear instructions and nutritional information.Found in MealByMeal
- Plan flexibility Allows users to adjust meal plans according to schedule changes or ingredient availability.Found in MealByMeal
How it works, step by step
- Log meals from photos
- Estimate calories, macronutrients and sugar from photos or text
- Accept typed meal descriptions
- Set personalized calorie and macro goals
- Show macronutrient breakdowns per meal and day
- Let users correct AI estimates and keep the correction
- Timestamp and organize meals into a timeline
- Provide a searchable food database for manual logging
- Track progress with charts and summaries
- Sync with fitness trackers and health apps
- Parse restaurant menu photos in multiple languages
- Suggest healthier menu swaps and ingredient alternatives
- Generate personalized meal plans from stated preferences
- Build shopping lists from planned meals
- Offer a recipe database with instructions and nutrition
- Allow plan adjustments for schedule or ingredient changes
- Run optional personal challenges such as reducing sugar
- Support shared goals with invited friends
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned user-corrected nutrition record with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Photo-based nutrition tracking and meal planning workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Photo-based nutrition tracking and meal planning workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare23 KB
- prompt-vps.mdThe same build on your own server (Docker)23 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data199 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce scattered logging and planning effort while keeping one reviewable nutrition record. For people managing daily nutrition who want one owned app instead of several tracking subscriptions, convert meal photos, typed descriptions, menu photos and stated goals into user-corrected nutrition estimates, plans and shopping lists. The benefit is a testable hypothesis, measured through logged meals per active week and user-corrected estimate accuracy; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect meal photos, typed meal descriptions, menu photos, stated goals and dietary preferences, then follow this sequence: 1. Log meals from photos. 2. Estimate calories, macronutrients and sugar from photos or text. 3. Let users correct AI estimates and keep the correction. 4. Generate personalized meal plans from stated preferences. 5. Build shopping lists from planned meals. Resolve uncertain cases with qualified reviewers, approve user-corrected nutrition estimates and plans, and measure logged meals per active week and user-corrected estimate accuracy against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Nutrition estimates remain estimates; final dietary decisions and any clinical advice remain with the user and qualified professionals. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve user privacy, source attribution, dietary permissions and data rights. Users approve substantive changes and sharing scope. One language and one cuisine set; nutrition estimates remain estimates and final dietary decisions remain with the user. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One language and one cuisine set; nutrition estimates remain estimates and final dietary decisions remain with the user. Implement one approved input format, a bounded representative case set and the first two task modules: log meals from photos; estimate calories, macronutrients and sugar from photos or text. Support the third module with operator review: let users correct AI estimates and keep the correction. Include source references, corrections, basic account access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
User-owned meal photos, typed descriptions and menu photos. Fitness trackers, health apps and calendar or reminder services. Start with file exchange and validate destination specifications before promising direct sync. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Daily log and photo capture, Menu and meal planning, Progress and settings. Use a thumbnail gallery for logged meals, a large central editing canvas for a selected meal or plan, and a right-hand panel for nutrition details, corrections and comments. Let users compare estimated and corrected values side by side. Display draft, corrected and confirmed states. Provide a shareable plan link with comments anchored to the relevant meal. Make the task-specific outcome user-corrected nutrition estimates and plans visible beside its evidence, review state and value baseline.
