RECONMATIC is a European research and innovation project in the domain of the Construction and Demolition Waste (CDW) management, focused on integrated decision making that would allow considering all aspects of CDW generation and involving all stakeholders within the construction industry in the whole life-cycle. The project focuses on one of the areas requiring increased attention in terms of improving the efficiency and sustainability of the construction industry and operation of existing built assets, followng objectives of the European Green Deal and the EU taxonomy requirements. The project proposes a suite of innovative tools, solutions and techniques to build bridges through "bottom-up" construction and demolition waste prevention or avoidance, management and handling to reach "top-down" European waste reduction goals. During the project implementation, the current practices in CDW management, from prevention and minimization of waste to its reuse, are evaluated and further developed to support the supply chains while circular economies are identified and integrated where it is possible end efficient. The aim is to develop, test and demonstrate a digital information management system for stakeholders' collaboration and waste traceability. It is envisaged that the CDW minimization will be achieved by empowering BIM and integrating waste management relevant data into the information models by product databank methodologies, waste predictors or BIM related data standard emphasizing recycling, reuse and management of CDW. Processes of converting different formats of construction information to the digital twins are going to be automated, as well as the decision-making system for repurposing, deconstruction and demolition. The project is furthermore going to employ automation for AI-assisted CDW classification and robotic segregation off-site. Methodology for CDW logistics reflecting automated and more efficient CDW sorting will be proposed, along with methodologies to provide new added-value uses to CDW seeking for higher valorization.