For peer reviewers
Built for peer reviewers
Your editor sent you a 40-page manuscript on a Friday. Your deadline is Tuesday. Run the AI pre-pass, then spend your human time on what actually matters.
How experienced reviewers use the tool
Peer review is triage plus depth. Experienced reviewers allocate the first 60 minutes to triage (what's the claim? is it defensible? is the methodology sound in principle?) and the next 3–5 hours to depth (do the statistics hold? are the figures honest? is the literature complete?).
The AI pre-pass takes on much of the triage. You get back a structured report on 8 dimensions before you crack the PDF, so more of your time goes to deep reading.
What the 8 agents check for you
Each language-model agent is calibrated on real reviews from 19+ open-review platforms (69K+ in all); the Prior Publication agent is a deterministic lookup:
- Methodology — Audits study design, statistical power, and analytical choices against field-specific rigour standards (CONSORT, STROBE, PRISMA).
- Formulas & Equations — Verifies mathematical derivations, checks dimensional analysis, and flags algebraic errors.
- Originality — Checks the manuscript's novelty claims against papers in our 4.5M-paper local library and flags possible overlap and self-plagiarism signals.
- Literature Coverage — Evaluates citation completeness, missing seminal references and self-citation balance, with a live OpenAlex snapshot of the field (volume, top venues, peak year) for context.
- Reproducibility — Inspects code availability, dataset accessibility, and sufficiency of methods detail for independent replication.
- Clarity & Language — Assesses readability, structural flow, and adherence to scholarly writing norms.
- Figures & Tables — Checks figure quality, caption completeness, and appropriateness of visual encodings.
- Prior Publication — A deterministic lookup, not a language model: fans out in parallel to 7 external sources — CrossRef, PubMed, arXiv, bioRxiv, medRxiv, Europe PMC and Unpaywall — and our local library to detect prior publication and duplicate submission.
The honest use pattern
We do not suggest you copy-paste AI output into a review report. Editors don't want that, authors don't want that, and frankly the AI is wrong enough of the time that you shouldn't either.
Instead:
- Read the agent summaries before the manuscript. Form a prior.
- Read the manuscript. Confirm or refute each agent's flagged issue.
- Write your own review, anchored in the 3–5 issues that actually matter after reading.
- Use the AI output as a 'did I miss anything' second-pair-of-eyes pass before submitting to the editor.
Editor-view and platform integrations
No journal runs our review engine end-to-end today, and there is no editor dashboard or platform integration. Whatever journal or venue you review for, run the AI pre-pass on the PDF only where that journal's policy allows it (several large publishers ask reviewers not to upload manuscripts to AI tools), and copy the report as Markdown, JSON or plain text.