AI-Powered Virtual Shopping Marketplace Platform for the Hair Integrations Industry
The average wig industry revenue over the last five years has a steady growth to $415.2 million per year. This industry caters to four distinct consumer groups: 1) individuals that purchase wigs for aesthetic purposes, 2) those that have lost their hair due to a medical condition or treatment, 3) those that follow their religious practice for specific hair restrictions, and 4) film/theatre directors who purchase wigs as part of character costumes. A wig costs from $600 to $1500 or more. In addition, with the COVID-19 outbreaks, online shopping inevitably became the leading trends.
However, shopping for a perfect wig online is not an easy task. We will build an AI-powered marketplace to solve the problem. In the AI-powered marketplace, customers get expert advice from AI as if customers are served by domain experts. AI will extract customers’ head shape, skin tone, and personality from the image and video, and make the best recommendations.
Computer vision powered digital twin for tracking manual manufacturing processes
Over 70% of tasks in manufacturing are still manual; therefore, over 75% of the variation in manufacturing comes from human beings. Human errors were the primary driver behind $22.1 billion in vehicle recalls in 2016. Currently, when plant operators want to gain an understanding of their manual processes, they send out their highly paid industrial engineers to run time studies. These studies produce highly biased and inaccurate data that provides minimal value to manufacturing teams. This project aims to develop a computer vision powered digital twin prototype that is ready to test on the client’s site, which helps manufacturing plant operators gain unprecedented visibility into their manual production operations, allowing them to optimize their worker efficiency while maximizing productivity. This will be done by automated data generation using computer vision, conversion of raw data into useable information, visualization of information using standard Business Intelligence methodologies and lastly, prediction of future plant performance based on historical information, as well as information about other market drivers.