RULES VERIFIED 21 JUL 2026AWARD DATA UPDATED METHODOLOGY PUBLISHED
— WHY GRANT REVIEW PRO

We score your NIH application against the real review framework — without writing a word of your science.

Most grant software does one of two things: it finds you money, or it drafts your proposal. This does neither. It reads what you already wrote, scores it against a published rubric built on NIH's Simplified Review Framework, and tells you — specifically, with citations — what a fair panel would object to and what to fix first.

WE SCORE

Against a fixed, published rubric

WE DIAGNOSE

Ranked, cited, attachment-level findings

WE DON'T WRITE

Your ideas stay provably your own

Advisory readiness signal — not a prediction of NIH's funding decision.

WHERE WE FIT

An honest map of the grant-software space — and the one bucket we chose.

Three things get sold to grant writers. They are not substitutes for each other, and pretending otherwise is how teams end up with a polished document nobody scored. We name the categories, not the companies.

BUCKET 01

Discovery tools

Find the opportunity

Opportunity search, deadline feeds, funder databases. They answer 'what could I apply to?' Useful, and upstream of everything we do — we index a funding directory ourselves for exactly this reason.

Complementary. Runs before us.
BUCKET 02

AI drafting tools

Write the proposal

Generative assistants that produce Specific Aims, Research Strategy prose, or full narrative sections from prompts and prior text. They answer 'what should the page say?' by writing it for you.

Deliberately not us. See the policy section below.
BUCKET 03US

Scoring & diagnosis

Judge the application

Read the applicant's own text, score it against the published review framework, run deterministic compliance gates, and rank what to fix. Answers 'is this competitive yet, and what is holding it back?'

This is the category we build in.
FIND IT → WRITE IT YOURSELF → SCORE IT HERE BEFORE YOU SUBMIT

THE POLICY WEDGE

“We don't touch your science” stopped being a limitation. NIH made it an advantage.

The rules changed underneath the drafting-tool category. Here is what NIH has actually published — read it yourself, every source is linked.

Applications substantially developed by AI are not treated as the applicant's original ideas.

NIH's notice on the use of generative AI in application development states that NIH does not consider applications — or sections of them — substantially developed by AI to be the original ideas of the applicant, and that such applications may be referred for further review as possible research misconduct. The policy direction set in this notice now carries forward into the NIH Grants Policy Statement effective March 2026.

Peer reviewers are prohibited from putting your application into a generative AI tool.

NIH prohibits scientific peer reviewers from using generative AI technologies to analyze or critique applications, because doing so breaches the confidentiality of the review process. Your application is confidential pre-publication IP on NIH's side of the table too — which is exactly the standard we hold ourselves to on ours.

REPORTED FINDINGSNOT OUR CLAIM

Reviewers say they can tell — and say it costs applicants at triage.

Reviewer-side research and commentary has reported that a majority of applications reviewers identified as AI-written did not progress past initial triage. We are repeating a reported finding, not producing a measurement of our own: we have not run that study, we do not have the underlying cohort, and we will not attach a precise percentage to it. Treat it as directional evidence that reviewer-facing AI prose carries real downside risk.

— THE TAKEAWAY

A tool that drafts your proposal walks you toward an originality problem you now have to defend. A tool that scores your own writing without generating any of your science leaves your ideas provably yours. That is not a feature gap on our side — it is the structural reason to use us.

WE SCORE · WE DIAGNOSE · WE CHECK FORMAT · WE NEVER WRITE YOUR SECTIONS

WHAT MAKES THE SCORE DEFENSIBLE

“An AI reviewed it” is not a finding. A traceable score is.

Improvised reviewer personas can produce sharp prose and a different answer every time you ask. That is a smart demo and a weak instrument. Every number we hand you traces back to a published rubric line plus a cited source fact — which means you can argue with it, and so can your co-investigators.

FIXED RUBRIC

A published standard, not a mood

The rubric is written down, versioned, and mapped to NIH's Simplified Review Framework — Factor 1 (Importance), Factor 2 (Rigor and Feasibility), Factor 3 (Expertise and Resources, rated sufficient or not). Same application, same rubric, same reading. You can read the whole thing before you pay.

Read the methodology →
REAL AWARD DATA

Winner Intelligence runs on NIH RePORTER records

A curated, growing set of real NIH awards — organization, PI, institute, activity code, fiscal year, dollars, abstract — each row linking back to its RePORTER source. When we tell you who has been funded on this mechanism, you can click through and check it.

Open the explorer →
DETERMINISTIC GATES

Some answers should never involve a model

Page limits by attachment and activity code, SBIR/STTR budget guideline levels, and the STTR work-split requirement (at least 40% small business, at least 30% research institution) are rules-table checks. They fire the same way every time, with the source rule cited, because arithmetic and published tables are not judgment calls.

See the page-limit matrix →
NO-AI PATH

The Submission Readiness Check uses no AI at all

A PDF parser and a rules table measure pages, margins, and font size against NIH format requirements, then the file is discarded when the check returns. No model call is involved in that product, so there is nothing about it to take on faith.

Run a format check →

Where judgment genuinely is required — reading whether an aim is mechanistically sound — a language model does the reasoning against our rubric, and we say so plainly rather than dressing it up as a measurement. Scores are an advisory readiness signal, never a prediction of NIH's funding decision.

FOUR INDEPENDENT SIGNALS, ONE DECISION

Four reads that do not depend on each other — so agreement means something.

Each one answers a different question with a different method. When they agree, you write. When they disagree, you know exactly where the argument is.

SIGNAL 01

Submission Match

Is this the right mechanism, and is the application ready?

A mechanism-specific rubric — one track for academic R-series, one for SBIR/STTR — scored against your own text, with deterministic gates for registrations, budget guideline levels, and the data management plan. Returns a readiness range with the reasoning behind it, not a single false-precision number.

WHY IT MATTERS

The cheapest correction in grant writing is finding out you are on the wrong mechanism before you write 12 pages for it.

See Submission Match
SIGNAL 02

Mock Study Section

What would a fair panel object to?

Three reviewer personas score Factor 1 and Factor 2, rate Factor 3 as sufficient or not, and return a reviewer-style Overall Impact readiness range as a holistic judgment — plus strengths, weaknesses, and a fix list ranked CRITICAL, HIGH_IMPACT, POLISH. Unlimited rounds, including A1 responses.

WHY IT MATTERS

You get Reviewer 2's objection while there is still time to fix it, instead of months later in a summary statement.

See Mock Study Section
SIGNAL 03

Winner Intelligence

Who is already being funded to do this?

A curated, growing set of real NIH awards from RePORTER, filterable by institute, activity code, fiscal year, disease area, and state — every row citable back to its source record, with CSV export.

WHY IT MATTERS

If nobody resembling you has been funded by this institute on this mechanism, that is the most useful no you will get all quarter.

See Winner Intelligence
SIGNAL 04

Funding Opportunity Directory

Who else funds this if NIH does not?

Verified non-dilutive and venture-philanthropy programs beyond NIH — disease foundations, state agencies, translational funds — each with real terms (equity, royalty, milestones) and a source page you can check.

WHY IT MATTERS

A NO-GO on one mechanism is not a NO-GO on funding. It is a redirect.

See Funding Opportunity Directory

TRUST AS PRODUCT

For a scientist audience, the disclosure is the feature.

You evaluate instruments for a living. So we built this the way a methods section gets built: published standard, cited inputs, disclosed processing, stated limitations. Anything we cannot substantiate, we do not say — including about ourselves.

  • PUBLISHED RUBRIC

    The scoring standard is readable before you buy, and versioned when it changes.

  • CITED FACTS

    Page limits, budget levels, deadlines and award rows carry a source and a verification date.

  • DISCLOSED PROCESSING

    Sub-processors are named, retention is stated, deletion is a button with a receipt.

  • STATED LIMITS

    We say what this cannot do: it is not the real study section and it does not validate outcomes.

  • NO FUNDING CLAIMS

    No odds, no payline predictions, no implied guarantee. Readiness signal only.

  • NO GHOSTWRITING

    We never generate your Aims, Strategy, or narrative. Your science stays yours.

Launch bonusFree with every Application Pass during the launch window

Target Intelligence Report on any target you choose.

A custom ~20-page cited scientific dossier on the target, mechanism, or disease area of your choice. Hand-prepared and delivered by email within 24 hours of purchase. One per Application Pass, LAUNCH50 sale only.

See what's inside →
— FREE, BEFORE YOU BUY ANYTHING

Everything we know, free.

NEXT SBIR/STTR STANDARD DUE DATESeptember 5, 2026~1 WEEKS OUT

Find out if your next application is worth writing.

Registrations alone can take six weeks or more, and there is no deadline extension to fix errors. Score it now, while the calendar is still on your side.

Five minutes · No card · No success fee