This case study is published as part of the Innovate UK BridgeAI programme, under the Independent Scientific Advisor (ISA) offer delivered by The Alan Turing Institute. The ISA initiative provides transformative, evidence-based support to SMEs across BridgeAI’s priority sectors, empowering them to harness AI for strategic growth and practical impact. We gratefully acknowledge the contributions of Kyle Burke, Founder and CEO of Tomale.AI, and Dr Jennifer Williams, Associate Advisor for BridgeAI at The Alan Turing Institute, whose insights and engagement were invaluable to the development of this case study. We would also like to express our appreciation to Alexandra Araujo Alvarez, Senior Research Community Manager for BridgeAI; Dominica D'Arcangelo, Programme Manager; and Kathryn Hockman, Project Coordinator, for their leadership and support throughout this work. We further acknowledge Stuart Gillespie for his role as technical writer for this and other case studies within the ISA offer. This work is led by Dr Vera Matser, Head of Strategic Capabilities and Principal Investigator for BridgeAI at The Alan Turing Institute. For any comments, questions, or collaboration opportunities with BridgeAI, please email: bridgeAI@turing.ac.uk. Abstract This case study explores how UK-based SME Tomale.AI is using generative artificial intelligence to enhance children’s engagement with reading through personalised storytelling. At a time when reading interest among children is declining, Tomale.AI addresses this challenge by enabling families to create customised, illustrated storybooks using AI, combining creativity with accessibility. Despite the potential of generative AI, the company faced technical challenges in developing reliable and consistent outputs, particularly in maintaining narrative coherence across age groups and ensuring visual consistency of characters across illustrations and styles. Through participation in the BridgeAI programme, Tomale.AI received targeted support from an Independent Scientific Advisor at The Alan Turing Institute to address these challenges and strengthen its technical and ethical approach. The collaboration focused on improving the performance and controllability of generative models, including the application of fine-tuning techniques such as Low-Rank Adaptation (LoRA) to enhance character consistency and stylistic variation. This enabled the company to refine its AI pipeline and move towards a more robust and scalable product. The case highlights the importance of combining creative vision with technical rigour in generative AI development, as well as the value of expert guidance in navigating complex model behaviour. It offers practical insights for SMEs working with generative AI in creative industries, particularly those seeking to balance innovation, usability, and reliability in user-facing applications.