Development of Niche AI Image Processing Platform

Niche AI is an advanced AI-powered image processing platform designed for businesses to upload, enhance, and analyze visual content through cutting-edge machine learning models. The platform enables users to process, manage, and track their AI model usage, storage, and credits—all within a sleek, analytics-driven dashboard.

Developed with a strong focus on scalability, security, and performance, Niche AI combines a smooth onboarding experience with robust backend architecture and real-time analytics. The platform supports both free-tier and subscription-based users, with integrated billing, credit limits, and usage monitoring.

As a Full Stack Developer, I was responsible for building and integrating the end-to-end system—from user authentication and company onboarding to AI image processing workflows and usage analytics—ensuring high reliability, low latency, and a seamless user experience.

Frontend: Next.js (React 18, TypeScript), Tailwind CSS, ShadCN/UI, TanStack Query, Zustand

Backend: Node.js (Express), Supabase (PostgreSQL), RESTful APIs

Authentication: Supabase Auth (Google OAuth + Email/Password)

Infrastructure: Vercel (frontend hosting), Supabase Edge Functions, Docker for backend microservices

Integrations: Stripe for billing, Cloudflare for CDN, AI model APIs for image processing

Implemented secure signup and login workflows using Supabase Auth and Google OAuth.

Built dynamic onboarding flows for business accounts, capturing company details and phone verification.

Developed real-time form validation and error-handling using Zod and React Hook Form.

Designed and built a responsive dashboard displaying credits, API calls, storage usage, and billing dates.

Integrated real-time analytics for “Usage Over Time” charts using Recharts and TanStack Query.

Created dynamic upgrade and plan management components linked with Stripe subscription tiers.

Developed RESTful endpoints for image upload, storage, and AI model processing.

Integrated with multiple AI endpoints to perform operations such as enhancement, background removal, and compression.

Implemented Supabase Storage with automatic quota enforcement and credit deduction per processed image.

Designed role-based access (Admin, Business User, Developer) via Supabase Row-Level Security (RLS).

Structured PostgreSQL schemas for users, companies, credits, image processing logs, and billing.

Ensured atomic operations for credit deductions and image processing transactions.

Created pixel-perfect, gradient-based UI consistent with brand design (dark mode support).

Optimized frontend performance through lazy loading and state memoization.

Integrated Lucide and React Icon libraries for consistent visual elements across pages.

Deployed production builds using Vercel and containerized backend services using Docker.

Set up CI/CD pipelines for automated testing and deployment.

Configured Supabase monitoring and error reporting for API performance and reliability.

Scalability: System designed to handle thousands of concurrent image uploads with quota tracking.

Performance: Average API response time reduced to under 300ms for image operations.

User Growth: Business onboarding completion rate increased by 45% due to intuitive signup flow.

Reliability: Achieved 99.9% uptime with automated health checks and error recovery.

User Experience: Introduced real-time credit and usage visibility, improving transparency and engagement.