Infor, a leading provider of business applications, has announced the acquisition of Predictix, a provider of machine-learning solutions for retailers. Predictix will become part of Infor CloudSuite Retail, a new suite of enterprise applications delivered in the cloud and designed for today's retailing landscape. The acquisition comes six months after Infor announced an investment in Predictix. "The synergies between Infor and Predictix were greater than we could have hoped, and we've come to appreciate a great cultural alignment where both teams have passionate people who work hard and want to make a difference in retail and beyond," said Charles Phillips, CEO of Infor. "Buying out the other Predictix investors makes sense to bring the teams together and provide the scale and resources needed to accelerate the retail revolution."
Enterprise software vendor Infor said it has acquired Starmount to bolster CloudSuite Retail with more converged commerce offerings. Terms of the deal were not disclosed. Starmount makes point-of-sale, mobile shopping, and store inventory management software for omni-channel retailers. Infor has been keenly focused on the retail industry since the launch of its Infor Retail business unit last summer. The launch coincided with a collaboration with Whole Foods Market to implement and iterate on the CloudSuite Retail platform, with Whole Foods serving as a sort of test lab for how the software could revamp its merchandising and supply chain operations.
Infor, a leading provider of beautiful business applications specialized by industry and built for the cloud, today announced Coleman(TM), an enterprise-grade, industry-specific AI platform for Infor CloudSuite(TM) applications. A pervasive platform that operates below an application's surface, Coleman mines data and uses powerful machine learning to improve processes such as inventory management, transportation routing, and predictive maintenance; Coleman also provides AI-driven recommendations and advice to enable users to make smarter business decisions more quickly. Coleman uses natural language processing and image recognition to chat, hear, talk, and recognize images to help people use technology more efficiently. Coleman develops a conversational relationship with the user which can be rendered in Infor Ming.le, a social collaboration platform, or a synthetic conversational user interface. User efficiency is increased as studies show humans are able to speak and hear 3-4 times as many words per minute as they can type.
In this episode of Reinventing Retail, we find out how predictive analytics are giving retailers the power to make data-driven decisions that will forever change assortment planning. Our guest is Richard Wright, VP of merchandising for Predictix: a leading provider of retail machine learning solutions. Infor announced it had completed its acquisition of Predictix on June 28, 2016. Using the artificial intelligence of machine learning technology, retailers are able to forecast potential market fluctuations and trends--with the agility to quickly adapt when disruptions are imminent. Hear what Richard has to say about this next generation retail tech, and subscribe to Reinventing Retail on iTunes for more episodes.
Infor has announced the general availability of its Infor Coleman Digital Assistant, which is designed to help maximise human work potential by enabling natural language extensibility and accessibility of Infor CloudSuite. At its annual Inforum customer conference, the company also introduced its Infor Coleman AI Platform for embedded machine learning models, which it expects to deliver in the Spring of 2019. The Infor Coleman Digital Assistant is the first in a series of new products rolled out under the Coleman AI umbrella and provides a conversational interface to the Infor OS platform, the underlying foundation of Infor CloudSuite. It offers custom skill building, a voice user experience (UX) and navigation, and natural language processing (NLP) extensibility. As a digital assistant, Coleman uses a conversational UX and NLP – with deep domain and industry knowledge – to chat, hear, talk, and in the future, it is expected to analyse images.