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Egen and UltronAI in collaboration with Carnegie Mellon University today announced a strategic partnership to bring new, innovative AI-based industry solutions to retail, health, life sciences, media and entertainment, and public sector clients.
UltronAI has been accepted into the NVIDIA Inception, a program that helps startups accelerate the development of technological solutions and fast-track company growth.
Welcome to “From Idea to Impact”, a new video interview series hosted by UltronAI CEO Stefanos Damianakis. In this series, Stef will be interviewing pioneers, experts, and luminaries across a wide spectrum of disciplines, from technology innovators to business savants. In this series, we’ll explore how these technology visionaries and business leaders drive success, advance…
In the first several posts of our series, we explored both the technical challenges involved in implementing artificial intelligence (AI) computer vision in retail, the operational challenges retailers face, the complexities of computer vision-based product recognition, product enrollment and scaling, and cost considerations for deployment and the operational cycle. In this, the final post of…
In this week’s post, we talk about UltronAI’s ability to dramatically streamline the operational cycle of retail computer vision. There are ways to improve nearly every process in the world. Such improvements might be brought on simply by smart ideas and new perspectives, and other times, they involve the development or maturity of innovations that…
This week, we break down the operational cycle of a standard AI computer vision system and how design and architectural decisions impact ongoing operational costs associated with deploying and scaling AI computer vision across a retailer’s portfolio of stores. In our last blog post, we explored the hardware, software, and development costs of production-ready computer…
In this week’s post, we break down some of the computer vision deployment costs associated with developing a scalable, production-ready solutions in retail and how AI system design can impact total costs. This is the latest in our series of blog posts that explore the challenges in deploying and operationalizing computer vision for product recognition…
In the first two posts of our series, we explored both the technical challenges involved in implementing artificial intelligence (AI) computer vision in retail, as well as the operational challenges retailers face. In this post, we’ll look more closely at the issues that can crop up during product enrollment (both initial and ongoing updates), expanding…
As self-checkout faces a reckoning, advancements in computer vision can fuel the next generation of solutions and help address core self-checkout issues around shrink reduction and customer experience. The history of self-checkout First introduced in the mid-1980s, self-checkout has frequently been heralded as the future of retail. Yet, even as self-checkout became increasingly more common…
As retailers seek to untap the value of computer vision when used for product recognition, they must first overcome a fundamental challenge: getting it to work. In this blog, we explore the significant difficulties of creating a computer-vision engine sophisticated enough to address variabilities in occlusion, positioning, lighting, and minimally differentiated products. Given its vast…