Catch submission risk before the journal does.
Catch submission risk before the journal does. Verification-first preview for citation, reviewer-risk, and journal-fit problems. No account needed.
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Zero-retention manuscript processing. Your manuscript is not used for training.
Start with the free preview. Upgrade only if the manuscript is serious enough to justify the deeper diagnostic.
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Get a fast submission-risk check
Upload the draft and get the first signal on readiness, reviewer risk, and what is most likely to block submission.
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Anthropic Privacy Partner
Anthropic Privacy Partner. Zero-retention manuscript processing. Your manuscript is not used for training.
Learn about our Anthropic partnershipWhat you get for free
Submission-risk verdict
Get the first answer to the question that matters most: does this draft look safe to submit or not yet?
Top blockers with direct quotes
See the issues most likely to trigger editor or reviewer pushback, grounded in the actual manuscript text.
Optional journal-fit signal
Add a target journal if you want a more specific fit and reviewer-risk read. Skip it if you just want the fast first check.
Paid report example
Nature Communications sample diagnostic
Open the actual paid-report format before you decide whether this product is worth going deeper on.
Journal fit
Borderline for Nature Comms
Top issue
Translational framing is too weak
What you get
Prioritized fix list with verified references
Why people are running this earlier
Authors are facing more AI-era submission risk before peer review even starts
The review is more valuable now because the failure modes are changing: more AI-assisted drafting, more citation risk, more publisher screening, and more pressure to disclose and verify what went into the manuscript before it reaches an editor.
Detection reality
Can journals actually detect AI-written manuscripts?
What editors can catch, what detectors still miss, and why reference problems usually matter more than style classifiers.
Publisher workflow
Which journals are already using AI screening before review?
A practical read on where publishers are moving and what that changes for authors before they submit.
Security risk
Why hidden prompts in manuscripts matter for AI review
A direct explanation of prompt injection risk and why naive manuscript-review tools are easier to manipulate than they look.
Category standard
What safe AI manuscript review actually requires
The checklist serious authors should use when deciding whether an AI review product is safe enough for a high-stakes submission.
How scoring works
Manusights Readiness Score v1.0
Five dimensions are weighted into one submission-readiness score. This is a quality-control signal to prioritize fixes before submission.
Citation Integrity
Weight: 25%
Methodological Robustness
Weight: 25%
Reviewer Risk
Weight: 20%
Journal Fit Readiness
Weight: 15%
Novelty & Positioning
Weight: 15%
85-100
Strong
70-84
Promising
55-69
Needs Work
0-54
High Risk
Why generic AI is not enough
Three things general-purpose AI cannot do reliably for manuscript review
Pasting a manuscript into a general-purpose chatbot gets you fluent commentary, not a serious submission screen. Manuscript review needs live literature context, citation verification, journal realism, and manuscript-specific evidence checks that generic AI tools do not reliably provide.
It searches live literature instead of relying on a stale knowledge cutoff.
A paper published last year doesn't exist to ChatGPT. Our system runs a fresh live search across 5 academic databases on every submission (PubMed, OpenAlex, Semantic Scholar, bioRxiv, and medRxiv), routed by your field, deduplicated, and ranked by relevance. The literature your reviewers will cite is in your report.
Every citation in the report is checked before it reaches you.
Hallucinated citations aren't a theoretical risk. ChatGPT will generate a convincing DOI for a paper that doesn't exist. It will sound completely confident doing it. Every citation in your report is checked against CrossRef, PubMed, and arXiv before it reaches you. If we can't confirm the paper exists, it doesn't get cited.
It is built around manuscript review, not general chat.
Long papers trip token limits in general-purpose AI. Figures, supplementary data, and methods on page 20 get cut off or skipped entirely. We use vision-based parsing to read every page, including figure panels and tables, and we calibrate the resulting feedback to what your specific target journal's editors actually expect to see.
The scoring criteria came from actual CNS reviewers
We built the rubric with scientists who actively peer review for Cell, Nature, and Science, and trained the AI on their actual peer review documents. Not just published papers. The point is not to sound smart about science. It is to surface the specific problems that matter before submission.
Novelty gap analysis
How they spot incremental work
Figure-level scrutiny
What reviewers check in data panels
Mechanistic sufficiency
When claims need more experiments
Translational framing
How high-IF journals expect it
Sample output
This is what you receive
A six-section .docx with verified citations from recent literature, scored 1-5 per section, and a prioritized A/B/C fix list.
Pre-Submission Assessment
Nature Communications
03 · Journal Fit
Current target (NC, IF 15.7): Borderline. The mechanistic data is solid but translational framing is underdeveloped.
04 · Key Experiments
Priority A: In vivo validation required...
Shown for format. Your report is calibrated to your chosen journal. The issues, scores, and citations will reflect your manuscript and target.
Real output from delivered reports
Specific. Cited. Actionable.
Not "improve your methods section." Every issue comes with a specific fix and the rationale behind it.
Primary efficacy endpoint analyzed without a pre-specified analysis plan. Section 3.2 describes post-hoc subgroup comparisons that were not pre-registered.
Register primary and secondary endpoints before submission. Nature Medicine requires this for clinical studies. Post-hoc subgroup analysis without registration is a common desk rejection trigger.
Introduction does not distinguish your findings from Chen et al. (2023, Nat. Commun.), which reported a closely related mechanism in the same cell type.
Add a paragraph directly addressing how your data extends, contradicts, or contextualizes the 2023 Chen study. Editors at this tier read the literature. Unexplained overlap reads as a red flag.
Mechanistic depth is solid but the translational framing is underdeveloped for a Nature Medicine audience. The clinical implication is a single paragraph in Section 5.
Expand to 3-4 sentences covering patient population, therapeutic window, and the nearest analogous approved drug class. This is standard at journals with IF above 40.
Three steps
How it works
Upload your manuscript
Drop your PDF or Word doc, choose a target journal, and start the Free Readiness Scan.
See the first decision signal
Get a manuscript-specific readiness view: top blockers, direct quotes, desk-reject risk, and journal realism in about 1-2 minutes.
Unlock the verification-first report
Get the Full AI Diagnostic with reference checks, figure-level feedback, journal-fit guidance, and a prioritized fix plan delivered as a .docx in about 30 minutes.
What the full report includes
What this would cost to replicate manually
A senior postdoc typically spends 3-4 hours on literature scan and citation checks before journal fit analysis even starts.
Colleague review is often delayed and unpredictable, which can miss submission windows.
Literature scan, citation check, figure review, and journal calibration in one report.
Free scan in 1-2 minutes. Full report available for $29 after your scan.
What researchers say
The report caught things they would have missed
It caught that we were missing in vivo validation for our main claim. Obvious in hindsight but three of us missed it. Had two weeks before the Nature Medicine submission window closed so we actually had time to fix it. Worth way more than $29.
Anqing C.
Postdoc, Immunology
Medical school (US)
Submitted to Nature Medicine
Flagged a confounding variable in our causal inference framing that I genuinely thought was fine. Ran the additional analysis before submitting to Nat Comms. Reviewers raised the same issue, saw it was already addressed, minor revisions. I got lucky that I checked.
Miguel T.
PI, Computational Biology
Research institute (EU)
Submitted to Nature Communications
Found three papers from the last year that I completely missed, all directly relevant to our novelty claim, and I would've submitted without citing any of them. Added a paragraph addressing the overlap and the reviewer who flagged those same papers said we'd handled it well. Could've easily been a reject.
Sojung K.
PhD student, Neuroscience
University (Canada)
Submitted to Sci. Transl. Med.
Start here or go deeper
Two ways to know before you submit
Full AI Diagnostic
Typical report delivery in ~30 minutes
30-minute delivery. No account. No subscription. One-time.
If it doesn't find something you didn't know, you don't pay
Email us for an immediate refund. No forms, no questions.
Expert Review
3-7 business days
Intended use and limitations
- • This diagnostic is a quality-control layer before submission, not a substitute for journal peer review.
- • It prioritizes actionable manuscript weaknesses and citation integrity, not final publication decisions.
- • Deep, niche mechanistic debates may require expert human review in your exact subfield.
Common questions
A six-section .docx report delivered to your inbox. Each section is scored 1-5, with an overall recommendation and a prioritized A/B/C list of what to fix first. Every issue comes with a specific fix, a rationale, and verified citations from recent literature. You can see a real example by clicking "Get the sample report" above.
Free Readiness Scan in about 60 seconds. Full AI Diagnostic delivery is typically around 30 minutes. Runs 24/7, including weekends and holidays.
Yes. Your PDF is encrypted in transit and permanently deleted after analysis. It's never stored, never shared, and never used to train any model.
That's actually the best time. Most researchers use the report to decide on a target journal, close figure gaps, and tighten methods before they consider the paper final. Getting feedback while you can still act on it is the whole point.
Biomedical and life sciences broadly: oncology, immunology, neuroscience, cardiology, metabolism, infectious disease, cell biology, and more. We cover 40+ journals across tiers, from Nature and Cell down to field-specific journals. If your target isn't on the list, email team@manusights.com and we'll confirm before you pay.
Three things. It searches 500M+ live papers (ChatGPT's knowledge has a cutoff). Every citation is verified against CrossRef and PubMed (ChatGPT invents them). And the scoring rubric was built with CNS peer reviewers using their actual review documents, not just published papers.
Different product. The expert review pairs you with a scientist who actively reviews for journals in your field. They write 12-18 specific recommendations with a cover letter strategy. Most researchers run the AI diagnostic first to catch fixable issues, then decide if they need deeper feedback for a top-tier submission.
Yes. Each submission is priced separately. Some researchers run it, revise, then run it again to confirm the issues were addressed before final submission.
If it doesn't flag at least one issue you weren't already aware of, email us for an immediate refund. No forms, no waiting.
Find out before reviewers do
Your reviewers will find these issues. The question is whether you find them first.
Report in ~30 minutes. Full refund if not satisfied.
Upload PDF. Choose journal. Pay. Report in ~30 min.
No account needed. One-time. No subscription. Ever.