The aerospace industry faces significant challenges in bringing new aircraft designs to market, as this concerns a complex certification process that relies heavily on expensive and time-consuming physical tests based on a pyramidal framework (from base to top: material, coupon, element, aerostructure). This approach has notable drawbacks, including a lack of insight into how changes at one level impact the overall aerostructure performance and the need to repeat much of the certification process if changes are made at distant levels. To address these challenges, the pAIramid project proposes a revolutionary approach based on high-fidelity virtual tests interconnected across the different levels of the certification pyramid. A digital tool is being created, which works by leveraging data-driven simulation methods and Artificial Intelligence (AI), aiming to optimize the certification process, reducing computational time, and promoting fast decision-making. This AI-driven hybrid pyramid approach breaks down barriers between different testing levels, easing knowledge transfer and faster design iterations. The pAIramid project is completed with several industrial demonstrators, which will help to check the proper performance of the digital tool while proving that it is able to effectively bring in new solutions to the aerostructures’ field. Four different use cases, all of them focused on advancing technologies related to composites’ properties (functionalized thermosets and thermoplastics) and manufacturing processes (one-shot LRI and FDM with continuous fiber reinforcement) are analyzed. All of them will be matured up to TRL4, counting with relevant collaboration of RTOs and industrial partners, which give these technologies the potential to be deployed in the market in the coming years, as well as representing valuable information for the tool learning, which will continue growing thanks to already existing and newly created data, while spreading in the market.