TG
Training Goat
Business Goals:
- Develop an AI-powered web and mobile assistant that automates training and diet planning.
- Improve user engagement through adaptive personalization and real-time feedback
- Reduce time spent on manual adjustments and decision-making in fitness planning
- Validate technical integration between AI modules (n8n, OpenaI) and backend (Express)
Tasks and measurable outcomes: Outcome 1: Functional MVP of web and mobile applications integrating AI-generated plans. Outcome 2: Real-time synchronization of user progress between mobile and web platforms. Outcome 3: AI-based training and diet adaptation verified via test users. Outcome 4: System performance metrics maintained under predefined thresholds (e..g <1s response for key operations).
Metrics and verification:
- user satisfaction (>= 80% positive feedback during pilot tests)
- stable API integration (no failed AI responses >5%)
- time reduction in manual plan adjustment (>60% compared to baseline)
- successful deployment on Render and MongoDB Atlas free tiers
Context: The fitness and health industry is increasingly relying on digital platforms to support users in achieving their goals. However, most existing training and nutrition tools provide static plants that don't adapt to users' real progrss or changing needs. Users often need to manually adjust their workouts or diets, which is time-consuming, unintuitive, and discouraging User problems:
- manual adjustment of training and diet plans is slow and error-prone
- lack of personalization - most apps rely on generic templates that don't reflect user differences in experience, goals, or performance Limitations of existing solutions: Current fitness apps focus mainly on data logging rather than automation or feedback. They faily to dynamically adjust plans or provide AI-driven insights, forcing users to act sort of as their own coaches.
Main problem: Users lack an integrated, intelligent assistant that can generate, track, and continuously optimize both training and diet plans in real time, reducing cognitive load and improving goal adherence
Final Products:
- Web Application (Nuxt 4 + Nuxt UI 4) - interactive dashboard for plan generation, progress tracking, and diet visualization
- Mobile Application (React Native / Expo) - real-time access to daily plans, workout summaries, and notifications
Supporting tools:
- AI engine (n8n workflows + OpenaI API) for personalized plan generation
- Backend (Express, MongoDB Atlas) for data management, synchronization, and API handling.
- Authentication (Clerk) for secure login and Single Sign-On across platforms
Characteristics: The system is hosted on Render.com, with repositories on GitHub and database hosted in MongoDB Atlas. It is designed for responsive use across browsers and mobile devices, enabling scalable deployment and future integrations.
End users: People interested in health and fitness who seek personalized, adaptive, and time-efficient guidance. They will benefit from automated, AI-generated plans that evlove with their progress, improving efficiency, motivation, and results Internal teams: Developers and testers gain experience with AI integration, cross-platfrom synchronization (web+mobile), and scalable backend design, improving workflow and technical capabilities External organizations: Potential fitness centers, nutritionists, or wellness brands can integrate their services or content into the system via PAIs, enabling new cooperation models and data-driven personalization. Communities: The borader health and fitness communnity benefits from the democratization of personalized training, helping users access expert-level guidance without the high cost of personal trainers
