Success stories

Department Store

What if... we could empower the online retail store, enhancing the user shopping experience, and saving costs for the client?

SERVICE

AI Specialists, Data scientists,
Machine learning Engineer, and
Systems Integration Engineers

INDUSTRY

Retail

TECHNOLOGY

GCP, Python, Docker, Gemini Pro
Vision Model, Text Embedding
Gecko Model.

The client

Department store chain with the widest coverage across México. The combination of a broad commercial offering, an exciting shopping experience, solid infrastructure, and a profitability-focused approach allows us to serve the customer and thus earn their preference.

Benefits

Improving product descriptions on the online store to enhance the customer shopping experience and increase sales.

Substantial reduce the time needed to input product attributes by leveraging cutting-edge technologies.

Challenge

The process of completing product attributes was cumbersome and time-consuming. It also resulted in incomplete or error-filled product descriptions, impacting the user’s shopping experience

Solutions

Using computer vision, we generated product descriptions which contain information about attributes such as color, material, dimensions, size, etc. This information is published on the ecommerce along with the product images. The project is based on the use of AI generative models, specifically Gemini Pro Vision, to achieve three main objectives:
  • Create new product descriptions by extracting attributes from product images.
  • Enrich these product descriptions using previous information from other products.
  • Perform a retroactive process to update the product descriptions when new attributes are found when compared to embeddings generated by Gecko (Google model), i.e., add this information to existing similar products.
Benefits

Improving product descriptions on the online store to enhance the customer shopping experience and increase sales.

Substantial reduce the time needed to input product attributes by leveraging cutting-edge technologies.
The process of completing product attributes was cumbersome and time-consuming. It also resulted in incomplete or error filled product descriptions, impacting the user’s shopping experience.
Using computer vision, we generated product descriptions which contain information about attributes such as color, material, dimensions, size, etc. This information is published on the ecommerce along with the product images.
The project is based on the use of AI generative models, specifically Gemini Pro Vision, to achieve three main objectives:
  • Create new product descriptions by extracting attributes from product images.
  • Enrich these product descriptions using previous information from other products
  • Perform a retroactive process to update the product descriptions when new attributes are found when compared to embeddings generated by Gecko (Google model), i.e., add this information to existing similar products.
Staff Augmentation
Google Cloud Platform (GCP) was chosen for its robust cloud computing capabilities and built-in machine learning services, providing a reliable infrastructure that is highly scalable for heavy computational tasks.
Python was selected as a high-level, interpreted language with an abundance of AI-related libraries, allowing for easy readability, quick prototyping, and sophisticated AI and machine learning projects.
Docker was utilized to automate deployment and scaling of applications, ensuring the consistent operation of the AI model across various environments.
The Gemini Pro Vision Model and Text Embedding Gecko Model were used to offer specialized functionalities in computer vision and natural language processing respectively, providing capabilities such as sophisticated analysis of visual content and representing text data as normalized numerical vectors.
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