Success stories

WEVO – UX RESEARCH PLATFORM

What if… AI-powered user research could scale instantly, without increasing costs or complexity?

SERVICE

UX Research Support Associates UX Research Analyst Customer Success Manager Executive Assistant Bookkeeper / Finance Operations

INDUSTRY

Consultancy

TECHNOLOGY

Qualtrics

The client

Wevo is an AI-powered UX research platform that delivers user insights in minutes. With Wevo Pulse, it uses AIsimulated personas trained on over one million studies to help teams test ideas, predict behavior, and optimize digital experiences at scale.

Benefits

Challenge

Scale AI-powered user research without increasing costs or losing quality
Reduce manual intervention and improve AI model accuracy
Launch lower-cost, express studies to expand market reach
Support rapid growth with scalable operational and customer functions

Solutions

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:
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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