Code review (illustrated in Figure 1) is a significant activity within modern professional software development (Davila & Nunes, 2021), where it has been widely accepted as an essential part of professional practice and is one of the key collaborative activities that occur within teams of developers. In this proposal, we seek to expand on this preliminary empirical data and the experimental platform we have built to ask the question: “how can code reviews be made fit for purpose?” That is, how can each stakeholder in the review process receive a tailored experience that fits their informational, social and task activity needs and curates these tailored experiences for others? We hypothesise that such a system would improve productivity and efficiency of code review, but it would also improve the quality of the social experiences that developers have at work. We propose to address this partly by using machine learning to analyze repositories and their pull request and review comments in order to train and design an automatic code review assistant that can support the developers during the code reviews.