Beauty Product Review Platform
A modernized beauty product review platform with a scalable Next.js and Laravel architecture, search-focused platform work, and AI-assisted content workflows.
- Next.js
- Laravel
- PHP
- TypeScript
- Tailwind CSS
- LLM Deployment
- SEO Optimization
- Docker
- Role
- Led the architectural overhaul, SEO and Core Web Vitals work, AI-first content optimization, and deployment of an internal LLM service.
- Team
- Professional work at She Communications Limited
- Timeline
- Employment context: Sep 2024 - Present
The challenge
The work focused on modernizing a legacy beauty product review platform into a more scalable and maintainable Next.js and Laravel system.
The scope also included technical SEO, Core Web Vitals, AI-assisted content workflows, and a securely deployed internal LLM service.
Key decisions
01
Separate platform responsibilities
The modernization uses Next.js and Laravel with MVC, Repository, and service-layer patterns to organize application responsibilities.
02
Design for maintainability
SOLID principles, dependency injection, and modular Factory and Strategy patterns guide the rebuilt platform architecture.
03
Treat discovery as platform work
Metadata, structured data, semantic HTML, and Core Web Vitals are handled as part of the product implementation rather than as a separate afterthought.
04
Keep AI services internal
The internal LLM service is deployed on local infrastructure through a containerized workflow, while sensitive operational details remain private.
Implementation
Architecture modernization
The platform overhaul combines a Next.js frontend with Laravel services and applies MVC, Repository, dependency injection, and service-layer patterns.
Factory and Strategy patterns support modular behavior, while the broader design follows SOLID principles to improve maintainability and extensibility.
Search and content foundations
The implementation includes metadata, structured data, semantic HTML, Core Web Vitals work, and AI-assisted content optimization.
These concerns are integrated into the platform architecture so product, content, and discovery workflows can evolve together.
Internal AI service
An internal LLM service was deployed on local server infrastructure through a containerized workflow to support company operations and content processes.
Model identity, prompts, datasets, infrastructure topology, resource allocation, costs, and internal data are intentionally outside this public case study.
Outcomes
Platform architecture
Rebuilt
The legacy platform architecture was redesigned around a modern Next.js and Laravel implementation.
Discovery foundations
Implemented
Technical SEO, structured data, semantic HTML, and Core Web Vitals work are part of the platform delivery.
Internal AI service
Deployed
A containerized internal LLM service supports private company workflows on local infrastructure.
Public platform
Live
The beauty product review platform is publicly accessible, while internal implementation details remain private.
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