What does Sensei do and what problem does it solve?
Sensei provides autonomous store solutions using computer vision and AI to create a frictionless shopping experience. It solves operational challenges like manual inventory checks and theft by enabling labor-light operations with real-time shelf inventory visibility for proactive replenishment. The platform is used in supermarkets, workplaces, and other hospitality settings.
What features, integrations, or surfaces does Sensei offer?
Sensei offers a software platform that provides real-time basket intelligence, operational insights, and automation. Key features include real-time shelf inventory visibility, proactive replenishment, theft elimination, and monetisation of performance metrics. The technology is GDPR-compliant, meaning it does not collect or store biometric data points.
What is Sensei used for, and in what situations?
Sensei is used to master operational efficiency and grow retail businesses by automating store operations and generating real-time insights. It is deployed in supermarkets, workplaces, mobility hubs, education, hospitality, leisure, and health and wellness venues to offer unique seamless experiences. It enables stores to be open longer, with or without staff, to unlock new revenue opportunities.
Who is Sensei for?
Sensei is for retail businesses seeking to enhance operational efficiency and offer a frictionless shopping experience. The platform is designed for retailers in various industries including supermarkets, workplaces, hospitality, leisure, and health and wellness, as well as for shoppers who want a seamless journey without scanning or queues.
What is Sensei?
Sensei is a technology platform that provides autonomous store solutions using AI and computer vision to enhance retail operational efficiency and create frictionless shopping experiences. It falls under the Data & Analytics category.
5 of 6 research questions are answered for this product. The rest need source evidence we have not collected yet, so they are left unanswered rather than guessed.