FoodMate AI
FoodMate AI
A simple food tracker for Telegram that combines manual entry and AI-powered meal recognition.
What the business needed?
You can keep a food diary directly in Telegram without installing the app. AI recognizes food from photos, saving time on entering and calculating calories; the paid version offers more recognition options. Manual entry is free. Reminders and a simple interface help you quickly add meals and see your daily balance.
What we built
Telegram mini-app for food tracking
Works directly in the messenger and allows you to track your food in a familiar environment, without installing separate apps or unnecessary steps.
AI-powered meal recognition from photos
A photo is converted into a dish card with nutritional information: the service determines the composition, volume, and key indicators faster than manual entry.
Convenient manual data entry mode
You can quickly add a dish using text or manually enter ingredients and portions if AI is unavailable or you need a more precise composition.
Meal reminders
The service promptly reminds you to enter your meal plan, helps you maintain a routine, and avoid skipping meals to keep your food diary complete.
Premium: More AI tokens
The user receives more recognitions and unlimited access to all features: history, statistics, and quick meal entry.
How the project was built
01
Researched real-world eating patterns
The team tested the trackers themselves, compared their strengths and weaknesses, and interviewed users with different lifestyles.
02
We developed a product model and requirements.
We defined the logic for AI recognition, manual input, tokens, and a premium mode to define the functional boundaries and structure of the future service.
03
We developed the user flow for the nutrition tracker.
We described scenarios: adding a dish, editing, statistics, reminders, and history, to ensure interaction remained simple and consistent.
04
We prepared UX and interface mockups.
We created wireframes, simplified the main actions, and designed screens in Figma to help users add meals more quickly.
05
We implemented a mini-app in WebApp.
We built the frontend and backend, integrated the AI model and token system, ensuring stable operation of the service in the Telegram environment.
06
We tested and finalized the release.
We verified key scenarios, adjusted details based on testing results, and prepared the project for launch in approximately two months.
Tools
- React
- TypeScript
- Redis
- TanStack Query
- Telegraf