Try BeautyGPT

Problem Statement

Consumers often struggle to find beauty products suited to their unique skin types and concerns, leading to frustration and decreased satisfaction. BeautyGPT addresses this by leveraging Generative AI to deliver personalized product recommendations and streamline the product discovery process.

Objectives & Goals

The primary objectives for BeautyGPT are to:

• Develop an intuitive, user-friendly AI-powered tool that offers personalized product recommendations.

• Ensure inclusivity with tailored solutions covering a wide range of skin types and beauty concerns.

• Build a fully functional MVP that demonstrates core features and value for users..

For a detailed breakdown of the product vision, features, and technical roadmap, please refer to the PRD.

Data Exploration & Evaluation

In the early stages of the project, I explored multiple datasets to evaluate different approaches to powering BeautyGPT’s recommendation engine.

I reviewed the Sephora Product and Review Dataset (Kaggle), which offered extensive product listings and customer reviews but proved too brand-specific and inconsistent to support a generalized recommendation engine.

I also considered expanding BeautyGPT into computer vision by testing the Skin Segmentation Dataset (UCI) and the Skin Disease Dataset (Kaggle). These datasets could have enabled skin type or skin condition detection through image analysis. However, I deprioritized this approach due to its complexity, privacy concerns, and the technical requirements of building a reliable classifier without an engineering team.

Instead, I focused on the Skincare Ingredients - INCI List Dataset (Kaggle), which provided structured ingredient information, including benefits, recommended use cases, and skin type compatibility. I cleaned and enriched this dataset to map ingredients directly to common skin concerns such as dryness, sensitivity, acne, and hyperpigmentation. This dataset formed the basis of BeautyGPT’s knowledge base and recommendation logic.

Solution Approach

The development of BeautyGPT began with user research to identify common challenges in beauty product discovery, such as compatibility with individual skin concerns and the overwhelming number of choices available.

Using the cleaned and structured skincare ingredient dataset, I built a lightweight recommendation engine leveraging OpenAI’s API and AgentHost.AI, which allowed me to deliver an interactive chatbot without the need for an engineering team. The system matched user-inputted skin concerns to ingredients and product characteristics, generating helpful and contextually relevant product suggestions. I guided this process through iterative prompt testing and refining of the knowledge base to balance generalization with helpful specificity.

To formalize the project, I created a comprehensive Product Requirements Document (PRD) outlining technical requirements, feature definitions, and user scenarios. Throughout MVP development, I conducted iterative testing and gathered user feedback to validate recommendations and improve the conversational quality of the chatbot.

BeautyGPT is now live as an embedded prototype on my portfolio, allowing users to interact with the tool and receive tailored skincare recommendations.