10088237Making the Intangible Tangible: Using AI to unlock thousands of chemist years worth of knowledgeClosedInvestment AcceleratorInnovate UKThis project aims to advance the development of an innovative intelligent data capture technology. This AI platform, initially designed for the chemical and pharmaceutical industries, utilises cutting-edge document processing and natural language understanding techniques to interpret highly unstructured, handwritten chemical data. The goal is to unlock and transform an extensive repository of preserved intellectual property into a format compatible with modern digital tools, thereby driving innovation and creating value for organisations collaborating with Data Revival. As industries increasingly embrace a digital-first approach, there is a growing need to structure and digitise the legacy knowledge that underpins organisational knowledge bases. A significant portion of this knowledge is handwritten and stored in countless lab notebooks, often tucked away and poorly catalogued. This invaluable data, often represented as chemical structures, reaction schemes, diagrams, and tables, is challenging for computers to process due to its handwritten and complex nature. Additionally, the multifaceted and contextual nature of chemical knowledge necessitates more than simple digitisation. In the era of accelerated adoption of advanced analytics and AI by research organisations, integrating preserved legacy knowledge into modern systems is paramount. The failure to revive this 'dark data' limits the potential of powerful technologies such as intelligent search and predictive modelling. This project aims to digitise and structure data from accumulated lab notebooks, thereby merging legacy and emerging research into a unified knowledge base. It paves the way for faster discoveries with societal and environmental benefits, as insights from past successes and failures inform future solutions. For instance, the intelligent incorporation of decades of polymer chemistry knowledge could reveal more sustainable production methods. Alternatively, reviving past pharmacological discoveries could work in tandem with AI to identify promising new drug compounds rapidly. The continuous shortening of innovation cycles amplifies the threat of knowledge silos. This project counters that threat through Data Revival's technology, enabling organisations to build on previous intellectual property continuously. The result is an exponential return on past and present R&D investments. In conclusion, reviving legacy materials enhances the value derived from preserved intellectual property, enabling research institutions to leverage advanced analytics on a fully contextualised foundation to generate actionable insights. This initiative serves as the crucial bridge between past knowledge and future potential.