Cosmetic ecommerce portal

Anthemion

Project brief.

Anthemion is an ecommerce platform dedicated to cosmetic items. Unlike typical portals, users here can either purchase products at fixed prices or negotiate with sellers for discounted rates, offering a unique shopping experience.

Budget
$25k - $50k
Duration
7 Months
Deliverables
- Wireframes
- UI/ UX Web
- Web application
- User guide resources (doc and videos)

The technology ecosystem we used in this project.

Design

Invision

Figma

Illustrator

Project Model

Waterfall

Documentaion

Gdocs

Postman

Backend

Node JS

Express JS

Frontend

Angular 7

HTML Bootstrap

DB

Mysql

CI/CD Pipeline

Github Actions

Project Management

Trello

Cloud services

AWS

EC2

Describe overall role of Techuz team in this project?

Challenges & Solutions

Technical Infrastructure:

During the development of Anthemion's ecommerce platform, we encountered a significant challenge with Technical Infrastructure. Our platform needed to support dynamic negotiation processes while maintaining stability and scalability. To address this, we invested in robust infrastructure, leveraging cloud-based solutions and scalable architectures. Implementing load balancing, auto-scaling, and containerization techniques ensured that our platform could handle fluctuating demand and maintain performance during peak times. Additionally, we adopted microservices architecture to enhance modularity and resilience, allowing us to scale individual components independently. Continuous monitoring and optimization of our infrastructure allowed us to identify and resolve bottlenecks proactively, ensuring a seamless user experience. Through strategic investment in technical infrastructure, we successfully overcame the challenges posed by Anthemion's unique negotiation feature, laying the foundation for a reliable and scalable ecommerce platform.

Negotiation Algorithm:

Developing Anthemion's ecommerce platform presented a challenge with Negotiation Algorithm. We needed an algorithm that facilitated fair negotiations while ensuring profitability for sellers. Through extensive research and iterative testing, we devised a dynamic algorithm that considers various factors such as product demand, seller margins, and user preferences. Implementing machine learning techniques enabled the algorithm to adapt and optimize negotiation outcomes over time. User feedback and data analytics played a crucial role in refining the algorithm, ensuring a balanced and transparent negotiation process. Ultimately, our innovative approach empowered users to negotiate confidently while maintaining profitability for sellers.