Availability of integrated, high-quality personal health data (PHD) remains limited, with impact on quality & cost of care and limiting possibilities for research and analytics. Indeed, PHD is currently distributed, heterogeneous, captured through different modalities, with variable quality. Findability and Accessibility of this data – following the FAIR principles – is addressed in numerous projects; Interoperability and Reuse remains a challenge due to several factors that are addressed by the intelligent virtual assistant being prototyped in the AIDAVA project. Concretely, the objective of the project is to maximise automation in data curation & publishing3 of heterogeneous PHDs while empowering individuals – patients or their deputies and data curators – when automation is not possible due to lack of contextual information. Through the data curation workflows (Deliverable 2.2. Data curation and publishing process), the AIDAVA virtual assistant prototype is expected to transform each patient's PHD into a Personal Health Knowledge Graph (PHKG). All PHKGs will be generated in compliance with the AIDAVA Reference Ontology (Deliverable 2.1. AIDAVA Reference Ontology as a Global Data Sharing Standard). From the PHKGs and the mapping information contained in the Reference Ontology, the publishing module will generate different target outputs as required by different use cases (see Deliverable 1.1. Description of Use Cases). This deliverable focuses on the solution design of the AIDAVA prototype virtual assistant. The solution includes a backend and a frontend. The backend includes foundational components such as the user directory, a master data reference repository, the catalogue of data sources with metadata supporting automation, a library of curation tools used in the automation workflows, the reference ontology which is the standard of reference for each PHKG, the repository of patient data – from raw format to PHKG and published format in the target standard – and the overarching orchestration module that supports automation and interaction with the end users. The frontend is the module that interacts with the end users. In Generation 1 of the AIDAVA prototype, user interaction will be minimum; in Generation 2, the user interface will build on advanced technologies from human-computer interaction, with explainability to facilitate understanding of the questions raised by the virtual assistant during the curation process. Explanations will be tailored to users categorised through different user profiles as identified in Deliverable D1.2. Report from user survey with personas canvas. These different components are described in the solution architecture, with the related Epics, in turn consolidated in Initiatives for the development team. In addition, the first 2 levels of a formal description of the system - based on the C4 model - is provided; an in-depth description of lower levels of the C4 models will be developed in Deliverable D3.1. VA Architecture. This deliverable also describes the proposed support model to be implemented when evaluating the prototype across the different sites. Finally, this deliverable introduces potential target customers. A full market analysis, with in-depth analysis of customers, market size and market potential for a product that could be developed on the result of the AIDAVA project will be provided in Deliverable 2.4, as an updated version of this deliverable, after evaluation of the first generation of the prototype.