Influenza places a constant burden on global health and the economy, with current antibody-based vaccines demanding continual reformulation due to mutational escape by the virus. This limitation has motivated the pursuit of vaccine strategies targeting conserved regions of the virus, primarily through CD8+ T-cell mediated immunity. The successful development of these T-cell stimulating vaccine-based strategies relies on understanding the repertoire of viral protein fragments (peptides) that are presented on the cell surface HLA molecules for recognition by the immune system, in combination with a systematic analysis of the full breadth of natural viral diversity. However, existing research in this area is restricted by a fundamental disconnect: data from laboratory immunopeptidomics experiments that map HLA-bound peptides cannot be easily cross-referenced against the hundreds of thousands of diverse viral sequences logged in global surveillance repositories. This problem is at the intersection of immunology, data science, and visualisation, as bridging this data gap requires robust, interactive computational tools capable of handling large sequence volumes. Here, we present a unified visual analytics platform designed to address this challenge, enabling immunologists to interactively map experimentally derived peptides onto the full viral proteome, quantify sequence conservation across strains, and integrate HLA binding predictions. Although developed for influenza, the platform's core framework for integrating experimental immunopeptidomics with large-scale sequence repositories is broadly applicable to conservation analysis in other highly diverse pathogens, such as SARS-CoV-2, HIV, or various bacteria.