AI-Powered Candidate Screening and Shortlisting: A Case Study on Ideal Image MedSpa

Novartis, a global healthcare company, was faced with the challenge of managing and making sense of a vast amount of data. The company had decades of data on how various compounds affect protein targets, with about a billion data points in total. This historical data was critical but sparse compared to the granular data currently being collected. Novartis uses an automated process that captures high-content image data showing how a particular compound has affected an entire cell culture, generating terabytes of phenotypic data. The challenge was to combine this historical data with the burgeoning phenotypic data and place it within the larger context of ongoing medical research from around the world. The team also wanted to combine its data with medical information from NIH’s PubMed, which contains about 25 million abstracts from some 5,600 scientific journals.

Book2Meet, the largest online booking platform for meeting spaces across Europe, approached N-iX with the goal of creating an easy-to-use platform for reserving meeting venues. The challenge was to design an intuitive application that simplified the booking process, shortened the user flow, and incorporated all search filters within a single widget, all while adhering to the platform's style guide and preserving its image. The UX/UI team faced the task of placing numerous filters on the page without overloading the design and ensuring that setting search criteria wouldn't be time-consuming. The initial user journey included numerous options spread across three pages, which was cumbersome and time-consuming for users.

autoRetouch, a Germany-based company, provides an automated image processing platform for fashion image producers worldwide. The shift to e-commerce has increased the demand for high-quality product images, which often require edits such as background removal and skin retouching. For retailers with hundreds of product lines, manually editing each image to perfection is a time-consuming and costly process. autoRetouch aimed to revolutionize this process by making it more time- and cost-effective. However, to train and run the models for its custom-developed machine learning algorithms, autoRetouch required powerful CPU and GPU processors that need to be available on demand. The platform had to be available around the clock and scale from processing a few images to hundreds of thousands, in seconds.

Patient cost savings are attributed to the fact that GDI is not owned by a hospital network. To drive superior quality of service and patient care, in 2017 GDI embarked on an enterprisewide ‘data-driven initiative’. GDI sought to set a financial and operational course to leverage consumption and usage of analytics for its biomedical engineering teams with machine data analytics to achieve true predictive maintenance on the imaging machines as well as drive superior patient care through deep analysis of its physician referral networks.

ITOCHU Techno-Solutions (CTC) operates the Technical Solution Center (TSC), a comprehensive facility that allows client companies to evaluate and verify systems to be introduced using actual equipment. It is Japan's largest verification facility for a multi-vendor environment and offers full technical support from experienced engineers. In recent years, the TSC has seen a significant increase in the number of AI/deep learning verification projects, and opportunities to verify data and service integrations between the TSC system and public cloud services like AWS, Microsoft Azure, and Google Cloud Platform. This growing need for verification of hybrid cloud and AI/deep learning drove CTC to create a new verification environment, AI_LAB, in late 2018. AI_LAB was designed to verify large-scale AI utilization and to build learning models. However, the challenge was to establish a system that could handle the advanced verification requirements of AI/deep learning.

Vasiliy Karpitski and Alexei Dulub, entrepreneurs and blockchain enthusiasts, identified a significant challenge in the rapidly growing NFT market. The NFT market, which hit $17.6 billion in sales in 2021, has attracted a large number of creators, businesses, and investors. However, the rapid growth of the market has also led to an increase in scams and fraudulent activities such as blacklists, wash trades, and duplicates. This has made it difficult for NFT buyers, creators, and businesses to make quality investment decisions and avoid risks. To address this challenge, the entrepreneurs decided to develop a smart solution that would help NFT investors make informed decisions by providing them with actionable data on NFT collectibles, their provenance, and ownership.