Structured editorial review
Generate manuscript assessments across summary, rigor, methods, statistics, causality, ethics, novelty, and fit-to-journal.
EditorClaw helps scientific editors parse manuscripts, generate structured editorial assessments, recommend reviewers with explainable scoring, and support faster decisions in privacy-sensitive environments.
EditorClaw is designed for journals and publishers that want practical AI assistance without losing control over manuscript privacy, reviewer selection quality, or editorial consistency.
Generate manuscript assessments across summary, rigor, methods, statistics, causality, ethics, novelty, and fit-to-journal.
Extract title, abstract, methods, results, discussion, affiliations, emails, and author metadata from manuscript PDFs.
Rank AE or reviewer candidates with semantic matching, keyword overlap, publication weighting, and explainable scoring.
Flag basic reviewer conflicts using author names, author emails, and institutional overlap signals.
Support local or institution-controlled deployment to reduce unnecessary manuscript exposure.
Adapt prompts, scoring rules, output formats, and decision language to a journal’s editorial policy.
EditorClaw is not a generic writing assistant. It is built around the real sequence of editorial work: review the manuscript, identify major issues, find better reviewers, and support consistent internal decisions.
Upload a manuscript PDF or paste title and abstract from your editorial system.
Produce a structured editorial review that covers rigor, design, methods, statistics, ethics, and journal fit.
Search your expert database and return ranked candidates with semantic match signals and plain-language explanations.
Use the output as a draft or internal memo while preserving human editorial judgment over every final decision.
Early-stage pilot with manuscript parsing, structured review, reviewer recommendation, and workflow feedback.
Workflow customization, local database integration, deployment guidance, and support for operational editorial use.
Multi-team rollout, governance support, security coordination, and product adaptation across journals.
No. EditorClaw is a decision-support system. It helps editors review manuscripts and identify better reviewer options faster, but it does not replace human editorial judgment.
Yes. EditorClaw is designed around local-first and institution-controlled deployment options, which can reduce unnecessary manuscript exposure.
Yes. The recommendation layer is designed to work with local Excel or structured expert databases and can be adapted to additional fields.
Yes. Review structure, prompts, decision language, and scoring logic can be aligned to a journal’s editorial standards.
Email: hello@editorclaw.ai
Website: editorclaw.ai
Availability: Early access and pilot collaboration