AI Product

FiberFits AI Nutrition Platform

Backend & Frontend Lead

This project is relevant for clients building practical AI features where reliability and UX matter.

Overview

FiberFits is an AI-powered nutrition and fitness platform where members can log meals by text or photo and receive structured nutrition analysis, maintenance-energy planning, and progress insights.

My role covered both backend and frontend delivery, with a strong focus on making the AI workflow reliable and usable inside a real product.

What The Platform Needed

The platform needed to support member-facing nutrition workflows:

The main challenge was turning an AI response into a predictable product workflow: submit, queue, analyze, persist, notify, and display the result clearly.

  • Meal logging from text or image
  • AI-powered nutrition analysis
  • Structured macro and calorie outputs
  • Progress updates while analysis is running
  • Nutrition goals and maintenance-energy planning
  • User profiles, measurements, and preferences
  • Localization and timezone-aware daily totals
  • Coolify/Docker-based deployment on Linux

My Backend And Frontend Work

I built the backend with .NET, Clean Architecture, ASP.NET Core Minimal APIs, EF Core/Npgsql, PostgreSQL, Clerk JWT authentication, MediatR, FluentValidation, background workers, and OpenAI integration.

I also built the frontend with Next.js, React, typed API clients, Clerk auth, localization, optimistic updates, image compression, and real-time progress handling.

Key work included:

  • MealEntry domain lifecycle: pending, processing, completed, failed
  • Async OpenAI nutrition analysis pipeline
  • JSON Schema structured outputs for more stable AI responses
  • Channel-based background worker
  • SSE stream for real-time client updates
  • PostgreSQL persistence with EF Core/Npgsql
  • SQL Server to PostgreSQL production data migration
  • Clerk JWT authentication and user scoping
  • Image handling and compression
  • Maintenance-energy and nutrition-goal workflows
  • Coolify/Docker/Nginx deployment on Linux

Technical Highlights

  • Backend stack: .NET, ASP.NET Core, EF Core/Npgsql, PostgreSQL, MediatR/CQRS
  • Frontend stack: Next.js, React, Tailwind/HeroUI, typed clients
  • AI: OpenAI API, structured JSON Schema outputs, image/text analysis
  • Realtime: Server-Sent Events and background worker notifications
  • Deployment: Docker, Coolify, Linux, Nginx, Cloudflare-facing setup

Why This Project Is Relevant

This project is relevant for clients building practical AI features where reliability and UX matter.

It is useful for clients who need:

The important part was not simply sending a prompt to AI. The important part was designing the full workflow around persistence, background processing, structured output, user feedback, and safe reprocessing.

  • OpenAI API integration
  • AI-powered SaaS features
  • .NET backend development
  • Next.js frontend development
  • Background processing
  • Real-time progress updates
  • PostgreSQL-backed product workflows
  • Coolify/Docker-based deployment

Project Images

More Relevant Projects