Our Story

Great science shouldn't take years to publish because nobody told you what was wrong.

My father is a scientist. A good one. And I watched him struggle to get published for years.

It wasn't the science. His research was solid. The problem was everything around it: framing that didn't land, a discussion section that buried the main finding, statistical presentation that gave reviewers easy targets. He'd submit, wait three months, get a vague rejection, and start over. Months of work, sitting in a queue, going nowhere.

I didn't fully understand what was happening until later. I studied computer science, went into healthcare consulting, then private equity. I spent years evaluating companies and technologies built on scientific research. I saw the system from the outside: which discoveries got funded, which papers shaped clinical decisions, which research changed how people thought about problems. And I started to notice a pattern.

The science that got published wasn't always the best science. It was the best-packaged science.

Then I moved to Boston, and I heard the same story everywhere.

A postdoc who spent 14 months bouncing between journals before publishing a finding that ended up highly cited. A PI whose student's paper got desk rejected three times because the cover letter didn't explain why the journal should care. A researcher who lost a grant renewal because their best work was still “under review” when the deadline hit.

The researchers I met in the Harvard and MIT ecosystem told me these stories constantly. And then they'd say something like:

“I reviewed a paper last week that had a great finding. Would have been desk rejected at any top journal. The framing was all wrong. If someone had just told them to fix the introduction, it would have sailed through.”

These were scientists who'd published in Nature, Cell, Science, and other selective journals. They had seen manuscript review from the inside: what editors flag, what reviewers push back on, and which claims trigger skepticism.

But that expertise was locked behind informal networks.

The feedback you get depends on where you are.

Well-connected lab

Senior colleague tears your draft apart before you submit
PI knows which journals want what, and tells you
Lab meeting catches the weak figure and the overclaimed discussion
Paper goes out polished. Gets accepted faster.

Everyone else

Submit your best guess and hope for the best
Wait 3 months for a vague rejection letter
Resubmit elsewhere, wait another 3 months
A year gone. Same paper. Fixable problems nobody mentioned.

Your paper's fate shouldn't depend on whether you happen to know someone who reviewed for Nature last month.

So we built the feedback system that should have existed all along.

So I went to the source.

Not to language editors. Not to AI companies. To the scientists who actually make publication decisions: researchers publishing in Cell, Nature, and Science. People who sit on editorial boards, review dozens of manuscripts a year, and know from direct experience what gets a paper accepted and what gets it desk rejected in nine days.

Manusights was built around this standard: evaluate the manuscript the way selective-journal reviewers and editors evaluate risk before submission. When the engine flags a methodology concern or a framing problem, it is applying criteria shaped around real peer-review failure modes, not language-editing rules.

That judgment is different from generic model feedback because it is grounded in manuscript-level evidence, journal fit, and the issues that actually trigger editorial or reviewer resistance.

Every researcher should have access to the kind of honest, expert feedback that well-connected labs get informally. Not language editing. Not formatting checks. Real scientific critique, built around how selective journals evaluate claims, evidence, and fit.

The difference is timing: you get that judgment before submission, when you can still fix the problems that would otherwise come back as desk-reject language or reviewer objections.

The verification standard: why we exist in a different category.

In 2025, 21% of peer reviews at ICLR were fully AI-generated. 100 hallucinated citations passed review at NeurIPS. The market responded with cheap AI tools that generate plausible-sounding feedback for $5. They have the same core problem: they don't verify their own output.

Manusights starts from a different premise: verify first, then analyze.

Manusights checks citation-risk signals against external bibliographic sources before delivery and flags references that need author verification. The product is designed to avoid fabricated DOI and author claims, while keeping final citation responsibility where it belongs: with the author.

Beyond verification, the review standard is calibrated around the objections that decide real submissions: underpowered evidence, missing controls, overclaimed causality, weak journal fit, and unclear novelty. Those are the problems cheap feedback tools usually miss.

That is the moat we care about: not sounding like a reviewer, but finding the issues a reviewer or editor is likely to use against the manuscript.

The result is a diagnostic that pairs manuscript-grounded critique with citation-risk checks. For the author trying to make a stronger submission, that combination is the product.

Ready to see your manuscript through a reviewer's eyes?

Start with the Free Readiness Scan. Unlock the Full Review from $39, with local pricing shown before checkout. If you need deeper submission planning, choose the Submission-Ready Dossier.

Anthropic Privacy Partner. Your manuscript is never used to train any model.

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The Team

Review standards from both sides of peer review

Manusights is built around the practical standards that decide whether a manuscript is ready for a selective journal: argument, methods, evidence support, figures, citation integrity, and journal fit.

500+

Papers published

Top-tier

Publication standards

10+

Disciplines covered

ImmunologyGenomicsNeuroscienceCancer BiologyMetabolismCardiologyInfectious DiseaseBiomarker DiscoveryCell BiologyComputational Biology

“I kept reviewing papers that had good science but obvious presentation problems. The kind of thing I could fix in a 20-minute conversation. I joined Manusights because I wanted that conversation to happen before the rejection letter.”
Manusights reviewer, former editorial board member

Our reviewers come from institutions including

Why we don't publish reviewer names

Some of the people who have shaped Manusights are active scientists and reviewers. Naming individual reviewers publicly can compromise their ability to serve as anonymous peer reviewers, and reviewer rosters change over time.

What we can say publicly is narrower and more durable: the automated Full Review is calibrated around selective-journal review patterns, not around language polishing. It is designed to pressure-test whether the manuscript's claim, methods, figures, citations, and target-journal fit will survive serious scrutiny.

For Expert Review engagements, we match reviewers by field and scope the work directly. Those reviewers operate under formal NDA. The automated Full Review does not mean a named human reviewer is reading every page of the customer's manuscript.

If you need to verify reviewer quality for an Expert Review or institutional engagement, email Erik directly and we'll share relevant credentials for your field under NDA.

What we believe

Your science deserves honest feedback.

Not encouragement. Not grammar corrections. Honest critique from someone who knows your field, has no reason to be polite, and will tell you exactly what a journal reviewer would say.

Expert judgment can't be automated.

Knowing that a particular antibody doesn't work at low concentrations, or that Reviewer 2 will flag this statistical approach. That's experience. You can't shortcut it.

Feedback should come before rejection.

The current system makes you wait months to learn what was wrong with your paper. We think you should know before you submit, when you can still do something about it.

Access shouldn't depend on connections.

If you're at a top lab, you get this feedback informally over coffee. If you're not, you're on your own. That's the gap we exist to close.

If you can't verify it, don't publish it.

AI tools that hallucinate citations are making science worse, not better. We verify every reference against CrossRef and PubMed. If an AI review tool doesn't check its own work, it's part of the problem.

Transparency builds trust.

We validated our system against real peer review and published the results. We think every AI tool making claims about scientific accuracy should do the same.

Your work stays yours

You're trusting us with unpublished research. We take that seriously.

Bounded retention

Access is limited to the review workflow

No human reads your file

The AI Full Review and Dossier are automated end to end

Never used for training

Your manuscript is not used to train any model

100% IP retention

Your ideas and data remain entirely yours

My dad eventually got his papers published. It just took longer than it should have. Manusights exists so the next researcher doesn't have to figure it out alone.

Erik Jia, Founder

Ready to give your manuscript a real shot?

Two ways to get started, depending on where you are.

Stage 1. Free

Free Readiness Scan