The project "Virtual Book Browsing and AI-powered Book Discovery" will provide UK bookshops and booksellers with innovative tools and services, such as look-inside, read later and a white-label AI-powered book recommendation service to enhance their online and ecommerce capabilities post pandemic. The project is a partnership between publishing technology specialist Jellybooks and bookseller Blackwell's, the UK's second largest physical book chain, who will also be the pilot partner in this project. At the core of the project is a national ebook sample infrastructure open to any UK bookshop or bookseller. This platform will support online browsing of the first 5% to 20% of a book (look-inside) including offline reading support (read later). Every book sample featured will have an integrated buy link offering users the option from within the book to purchase a digital or physical version (click-to-buy), including the option of in-store pick-up (click & collect). The platform will use the Jellybooks retailer localisation engine to operate as a closed-loop system. This means that, unlike alternative solutions, an ebook sample offered and distributed by retailer A only has buy links to the same retailer A and not competing retailers B and C even though B and C may be using the platform for their own purposes as well, even selling the same books. Consumers will not be required to download an ebook file or install an app. Instead the platform will leverage the state-of-the-art Jellybooks Cloud Reader to support reading in the cloud on any connected device with browser support. The Jellybooks Cloud Reader is based on the open ePub standard and is able to support visually impaired readers and other readers with accessibility requirements. Data collected across retailers via the national platform will be used to develop and train machine learning algorithms (colloquially referred to as artificial intelligence or AI). The goal is to identify taste clusters allowing for better and more accurate category description of books. The use of observational user data will correct for inherent industry biases and errors in existing classification systems, support a greater diversity of authors and voices, and reduce the marginalisation of audiences and authors that can occur in the current mass-market focused system. The machine learning algorithms will also be deployed to create a book recommendation engine for book retailers and publishers. This will take the shape of a white-label direct-to-consumer newsletter service that will generate AI-powered book recommendations specific to individual users, communities, and audience niches. In short, the service will offer a Netflix-style recommendation engine adapted and optimised for the decentralised needs of independent book retailers, a major innovation. The platform will also be used to nudge users towards the purchase path generating the least packaging waste and the smallest carbon footprint in terms of book delivery. The project pilot will open to end users at the beginning of 2021\.