The paper describes a potential platform to facilitate academic peer review with emphasis on early-stage research. This platform aims to make peer review more accurate and timely by rewarding reviewers on the basis of peer prediction algorithms. The algorithm uses a variation of Peer Truth Serum for Crowdsourcing Radanovic et al. (2016) with human raters competing against a machine learning benchmark. We explain how our approach addresses two large productive inefficiencies in science: mismatch between research questions and teams and redundancy of research projects. Better peer-review for early research creates additional incentives for sharing it, which simplifies matching ideas to teams and reduces redundancy.