Reviewer Select’s new website introduces a semantic AI platform that helps editorial teams identify relevant peer reviewers with greater speed and confidence. The platform analyses manuscript concepts, ranks reviewer candidates by expertise fit, and provides evidence behind every recommendation, helping teams access relevant research expertise and find peer reviewers on a strong reviewer database.
Reviewer Select has launched its new website, giving publishers, journal editors, and editorial teams a clearer view of its semantic AI solution for peer reviewer matching. The website explains how Reviewer Select helps journal editorial teams identify researcher expertise and find relevant and available peer reviewers for each manuscript, builds a useful reviewer database, and provides evidence-based, explainable matches for manuscripts.
Reimagining reviewer search
For editorial teams, the need to find peer reviewers, based on researcher expertise, and maintain a useful reviewer database is central to the publishing ecosystem. Finding peer reviewers also remains one of the most time-consuming aspects of editorial work. Editors are not simply looking for available reviewers — they need people whose expertise closely aligns with the manuscript, topic, and level of review required. For teams trying to find peer reviewers without adding manual work, the process often depends on the quality and depth of the reviewer database they can access.
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Reviewer Select is designed to make that process faster, clearer, and more evidence-based. Rather than relying only on keyword matching, the platform uses semantic AI to understand the meaning of a manuscript by extracting the research concepts it covers. It then recommends reviewers whose published work and experience align with those concepts, helping editors find researcher profiles that reflect real domain expertise and ensuring reviewer database search remains relevant to the manuscript.
“Reviewer selection should not feel like guesswork,” said Yogesh Agarwal, Founder and CEO, Reviewer Select. “With Reviewer Select, we bring together AI, publishing workflow knowledge, and evidence-based matching so editorial teams can make faster, more accurate, and more confident reviewer invitation decisions, especially when they need to find peer reviewers from a trusted reviewer database and find researcher expertise with supporting evidence.”
What visitors can explore on the website
The new Reviewer Select website shows how the platform supports editorial teams with:
- Semantic AI that matches manuscripts to reviewers by meaning, not just keywords but with concepts, helping editors identify researcher expertise with greater precision.
- Extracted research concepts that editors can review and refine before each search, so they can find peer reviewers based on the manuscript’s meaning.
- Ranked reviewer recommendations with concept-level match explanations, supported by a reviewer database designed around evidence and expertise fit.
- Supporting evidence from published articles and clickable DOIs, helping teams find researcher connections and publication history behind every recommendation.
- Integrity signals, including retraction history and conflicts of interest, so editors can find peer reviewers with additional context.
- Expertise-driven ranking that prioritizes manuscript fit over seniority or h-index, making it easier to find researcher candidates aligned with the work.
- A reviewer database with reviewer profile cards displaying affiliation, subject expertise, and location, enabling editorial teams to find peer reviewers across subject areas, location, and affiliations.
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