LookbookAI
AI Fashion Content Generator
This web-based platform developed independently automates the creation of high-quality visual assets and social media copy for select fashion brands. It addresses the challenges of delayed photoshoot schedules and limited flatlay imagery by using AI to generate brand-aligned content. The application serves as a scalable solution for social media teams to produce professional marketing materials efficiently.
Project
Brand 'X'
Period
February 2026
About
Goals
The project aims to provide a tool for generating professional product catalog images and lifestyle campaign visuals without manual photoshoots. It also seeks to automate the creation of engaging, platform-specific captions for Instagram and TikTok in multiple languages. Ultimately, it focuses on speeding up time-to-market and improving digital engagement.
Process
The developer built a full-stack application using Next.js and integrated the Gemini API for advanced image and text generation. The workflow includes implementing server-side image normalization, Base64 encoding for payloads, and custom prompt engineering based on user-selected models and angles. The system manages state via React Context and handles form data using React Hook Form.
Output
The final deliverable is a functional web application capable of generating 4K catalog and lookbook images with specific model and angle configurations. It produces platform-ready captions for Instagram and TikTok in English and Indonesian, complete with hashtag suggestions. The system ensures output consistency with the brand's visual identity and target audience persona.
Tools
Programming Languages
: Node.js, TypeScript
Frameworks
: NestJS, Next.js
Databases
: PostgreSQL (Supabase)
ORM
: Prisma
Deployment
: Railway, Netlify
Libraries
: Sharp, React Hook Form
Styling
: Tailwind CSS, PostCSS
AI & APIs
: Gemini API (models: gemini-2.5-flash-image, gemini-3-flash-preview), Google Generative AI SDK.
Method
Architecture
: REST API Design, Next.js API Routes, Client-Side Rendering (CSR)
Frontend Practices
: React Context API for state management, Custom Hooks, Client-side image compression
Workflows
: Server-side prompt engineering, Base64 image encoding, RESTful API consumption
Development Standards
: Strict TypeScript typing, Linting with ESLint.