Roughly the odds at NSF; closer to 1 in 8 at NIH. The margin between funded and declined is rarely brilliance; it is usually a handful of fixable weaknesses.
Your proposal will face a panel. Let it face ours first.
Upload your proposal and the agency's solicitation. GrantPanel's purpose-built multi-AI agents run a mock review panel that checks compliance and alignment, verifies your references, conducts a deep literature search, generates three independent reviews, debates the proposal over three rounds, and delivers a chaired consensus with a recommended revision for every weakness they identify.
Works with any solicitation: NSF · NIH · DOE · DOD · NASA · DOT · foundations & beyond
Step 1 is yours. Everything after it is GrantPanel.
PI (user) uploads
Institution names shown for identification only, not as an endorsement.
Funding favors the rehearsed.
Everything here exists for one blunt reason: better odds. Panels fund a fraction of what they read, and proposals are usually declined for avoidable reasons: an unsupported claim, a vague evaluation plan, a missed requirement. Some universities already convene mock panels for their flagship submissions. GrantPanel makes that rehearsal available for every proposal.
A real panel's critique arrives only after the deadline, and the next cycle may never come. Programs change, topics are not always re-announced, and an idea can be overtaken while you wait.
A mock panel delivers the same kind of critique before submission, while every weakness it finds is still yours to fix.
Can we promise funding? No, and we never will. What a rehearsal changes is the part you control: whether the weaknesses in your proposal are found by you before submission, or by the panel that decides after it.
Your proposal is never kept, and never used to train AI models.
Your proposal is held in encrypted storage only while the review runs, and deleted automatically the moment it finishes. Only the panel's written report remains, in your account, so you can revise against it, and you can delete it whenever you like. Your unpublished work stays yours.
Never used to train AI
Your proposal and solicitation are sent to our model providers, Anthropic and OpenAI, only to generate your review. They are not used to train any model, by us or by the providers.
Auto-deleted after your review
Your proposal and supporting files are deleted automatically as soon as your review finishes. Your report stays in your account until you delete it.
A practice tool, not an agency product
This is a simulation for your own preparation. It is not affiliated with, endorsed by, or a guarantee of any outcome from any funding agency or foundation.
The kind of thing you'd rather hear early.
Illustrative findings drawn from real review patterns: the sort of note a panel writes when a strong proposal has a fixable seam.
The Project Summary is missing the Intellectual Merit sub-heading required by PAPPG II.D.2.b. This is a return-without-review trigger, not a stylistic note.
Add the two required sub-headings verbatim. Five minutes of work; the alternative is an automatic return.
Aim 3 depends entirely on Aim 1 reaching its performance target, and no fallback is described. If Aim 1 underdelivers, two thirds of the project has nothing to run on.
Name the existing MPC baseline as an explicit contingency path for Aims 2–3, roughly half a page.
Three 2025–26 papers overlap the Aim 2 formulation and none are cited. A panelist who knows that work will read the omission as unawareness or avoidance.
Cite the three, then add one sentence in Aim 2 stating what your approach does that theirs does not.
What you actually get.
A complete run, start to finish: compliance, alignment, three independent reviews, the discussion transcript, the chaired consensus, and the supporting research, readable in the browser or saved as HTML or PDF. Read one before you pay for one.
Disagreement is the feature.
Ask an AI to be critical and it will politely agree with itself. So we don't ask: the three rules below are built into the pipeline, and the panel cannot skip them.
Independence is structural
Each reviewer runs in its own context and files a complete assessment before any other position exists to be influenced by. A prompt asking a model to be impartial is a request. Separate context is a guarantee.
Positions must be defended
A reviewer cannot restate its opening view. It has to answer the strongest objection raised against it, and any change of position carries a stated reason. Agreement has to be earned in the transcript.
The chair cannot invent
The chair may weigh only what was argued on the record. It cannot introduce a criticism no reviewer made, and where the panel did not converge it prints both readings instead of averaging them into one.
A single model, however well prompted, tends to validate the framing it was handed. Three separate readings do not share that framing.
Nothing collapses the three views into a tidy verdict. A disagreement that survives round three is reported as a disagreement.
Every finding belongs to a reviewer and a round, so you can read the argument behind it rather than take the conclusion on faith.
Every one of these is checkable after the fact: the deliberation transcript ships with the report. Read a sample one →
What a complete pre-submission review requires.
A purpose-built review workflow, not a blank chat window, and not a months-long black box.
The agency's own criteria
Review criteria are read from your solicitation and the agency's policy rules rather than a generic template, with each criterion tied to its source location, so the rulebook stays visible in the review record. Solicitation-specific risks are flagged before the first reviewer reads.
Independence by construction
Each reviewer works from separate context and forms its assessment before seeing any panel position: no majority cue, no premature consensus. Then the three argue across three panel rounds. The consensus summary records where they converged, where they differed, and why.
Evidence-linked findings
Every finding points back to the page and passage it comes from, so you can verify the panel's claim against your own document.
Technical and scientific depth
Specialist agents evaluate methods, aims, and feasibility, not just formatting, before the panel weighs significance.
A deep literature search
Novelty claims are checked against the field as it stands: a live deep search across prior work, competing approaches, and recent publications, not just what a model remembers.
Honest limits
Practice, not prophecy: a simulation can expose weaknesses while you can still fix them. It cannot predict a real panel's score, and we never claim it does. Every finding comes with a transcript, so you can inspect the reasoning rather than take it on faith.
Every weakness comes with a recommended fix. Every revision gets a second look.
A revision should answer a critique, not merely sound different. Every weakness and gap the panel raises leaves with a specific recommended revision, grouped by stage and tied to the section it belongs to. Your proposal comes with three runs, so when you revise, a full panel reads the new version and returns its own verdict. You see how the rating moved between runs.
Compliance & formatting
Fix before submission- Add the Intellectual Merit sub-heading to the Project Summary
- Trim the Data Management Plan to the 2-page limit
Solicitation alignment
Meet requirements- Secure an industry letter of collaboration
Individual reviews
Strengthen the science- Expand the n=12 validation set, or add a held-out benchmark
- Operationalize the statistical analysis plan
- Run 3 · revisionCompetitive= no change
- Run 2 · revisionCompetitive↑ improved
- Run 1 · originalLow competitive—
The right model for every task. No hidden downgrades.
GrantPanel doesn't run on one model with default settings. It assigns every task to the model best at it, across OpenAI (ChatGPT) and Anthropic (Claude), and equips every agent with the tools and data sources its job needs. Other tools quietly downgrade to cheaper models to cut costs; we match the model to the task, and never downgrade one to save money.
No single model runs your review. Fast, precise models read the package, audit compliance, and extract the rubric and your citations. Frontier models from OpenAI (ChatGPT) and Anthropic (Claude) write the three reviews and argue them through three rounds, split across the two providers so the panel’s disagreement comes from different models, not one model arguing with itself. The strongest model chairs. A model is never swapped down to protect our margin.
Each agent gets the tools and data its job needs, not a chatbot’s defaults: your proposal and the call read natively as PDFs, the solicitation’s own rules and rubric, Semantic Scholar to confirm that every cited work exists and to read its abstract, and live web search for the state-of-the-art scan. Checks in our own code then pin every quoted passage to your text and inspect the panel’s summary before you see it.
Solicitations change every cycle, and so do the models best suited to reading them closely. Fine-tuning would freeze today’s program knowledge into weights we could not inspect and you could not audit — and it would make your proposal training material. Instead the review process itself is the engineering: a panel of purpose-built agents, each calibrated to its role and to your agency’s own criteria, running on whichever models are strongest at the time. The rulebook stays readable, the method stays current, and your work is never trained on.
OpenAI (ChatGPT) and Anthropic (Claude) are referenced only to identify compatible AI providers and products. GrantPanel is independent and is not affiliated with, sponsored by, or endorsed by either company. No third-party logos are used.
Built by people who've been on every side.
GrantPanel's multi-agent review process is designed by faculty researchers, panel-side reviewers, and former program managers: people who've written, won, reviewed, and funded grants.
Experience turning complex research ideas into competitive, compliant proposals under real submission constraints.
- Proposal strategy and positioning
- Evidence, aims, and narrative coherence
- Revision under deadline pressure
Experience reviewing proposals, interpreting agency criteria, and understanding how panel judgments are formed.
- Agency review criteria
- Panel deliberation and consensus
- Decision-relevant weaknesses
Experience directing funding programs: shaping solicitations, guiding panels, and deciding what a portfolio funds.
- How solicitations are written
- What panels are asked to prioritize
- What separates funded from declined
Built by researchers and former program managers who wanted a second opinion before submitting, not a startup selling AI hype. We built the tool we wished existed for our own proposals.
One price, per proposal.
No subscriptions and no plans to compare. Every purchase runs the same full panel. Each proposal gets up to 3 runs: your initial review plus two re-reviews. Nothing counts down until you start: the first run opens a 60-day window, and runs 2 and 3 are yours at any point inside it.
Up to 3 panel runs, within 60 days of the first.
- 1–2 proposals$79 each
- 3–5 proposals$69 each
- 6–10 proposals$64 each
Need more than 10? Talk to us about volume pricing →
A run is one full panel review. Buy whenever you like; nothing counts down until you start. A failed run is never charged. Prices are in US dollars; sales tax is added at checkout where it applies. See a full sample report →
For more than 10 proposals, or seats across a department.
- ✓ Volume pricing across the institution
- ✓ Invoice and purchase-order billing
- ✓ Privacy and security review support
- ✓ Onboarding for your research office
Common questions.
How is this different from pasting my draft into a chatbot like ChatGPT or Claude?
GrantPanel does not hand your proposal to a chatbot, or to a frontier model as-is. It convenes a panel of purpose-built agents, each calibrated to its role and to your agency's own review criteria: compliance and solicitation alignment checks, independent reviewer mandates, evidence checks, three rounds of deliberation, and a chaired consensus. The value is in the engineered process, not a single prompt.
What happens to my proposal, and is it stored anywhere?
We are researchers ourselves, and we would not hand our own unpublished work to a tool that kept it. Your proposal and supporting files are held only while the review runs and are deleted automatically the moment it finishes. The solicitation is kept for your proposal's 60-day revision window, so revision runs need not upload it again. Only the panel's written report is kept, in your account, so you can revise and run a revision review, and you can delete it at any time. Your files are never used to train models, by us or by our providers.
Will it predict my score?
No, and the distinction matters, because you will see ratings in your report. Each reviewer scores your proposal on the agency's own scale, and the chair records where the panel came out. Those are this panel's judgement of this reading of your draft, and they are there for a reason: they are how you tell whether a revision actually moved anything. What they are not is a forecast. A real panel turns on who is assigned your proposal, what else is in the pile that day, and how the discussion goes on the morning. GrantPanel is practice, not prophecy: read the rating as a signal about your draft, not an estimate of your odds.
Which agencies does it support?
Any program with a written solicitation and review criteria: federal agencies such as NSF, NIH, DOE, DOD, NASA, and DOT, as well as foundations and institutional opportunities.
How long does a review take?
A full run — compliance, alignment, three reviewers, panel discussion, and summary — usually takes about 5 to 15 minutes. Most of that time is the deep literature search: the panel checks your novelty and prior-work claims against the current literature instead of relying on what a model already remembers. We could return a report in seconds by skipping that step and save API credits and costs, but it would cost the technical depth and rigor that make the review worth reading.
Why not use cheaper or open-weight models to bring the price down?
We tested them extensively, across the whole pipeline. Smaller and open-weight models handle the mechanical stages competently: spotting a missing section, checking a page limit, extracting citations. Where they consistently fall short is the part that makes a review worth reading. They summarize a proposal instead of interrogating it, agree with each other instead of holding distinct positions through three rounds of argument, miss the gap between what a claim asserts and what the cited evidence actually supports, and produce weaknesses too generic to act on. A cheaper review isn't a bargain if its findings are ones you could have written yourself. So your review runs on a mixture of models, each on the task it does best: fast, precise models for the mechanical stages they handle well, and frontier models for the reviews, the debate, and the chair's synthesis. None is ever downgraded to cut costs. We would rather charge honestly for that than quietly serve you a weaker panel.
What counts as a run, and how long do I have to use all three?
A run is one complete panel: compliance and solicitation checks, a reference audit, three independent reviewers, three rounds of deliberation, and a chaired consensus. One proposal buys three of them. Nothing counts down while a proposal sits unused, so you can buy well before you are ready. The first run opens a 60-day window, and the other two are yours at any point inside it. Sixty days is deliberately longer than most revision cycles: it is meant to cover reading the panel's findings, rewriting, and running again before your deadline, without becoming an allowance you bank indefinitely against a different submission.
Why is there no subscription?
Because we are from academia and we know the work is not continuous. Proposal writing arrives in bursts around solicitations and deadlines, and in between you are teaching, running a lab, reviewing, and doing the research itself. A monthly fee would quietly bill you through every month you never open the tool. So you buy a proposal review when you have your proposal ready, an unused proposal review never expires, and nothing charges you for the semesters you do not need us.
Other tools charge less. Why does GrantPanel cost more?
Some do, and if what you want is a quick read-through of your draft, one of them may suit you. GrantPanel is doing a different amount of work. Every run is a full panel: compliance and solicitation checks, a reference audit, three independent reviewers, three rounds of deliberation, and a chaired consensus. Each stage is its own frontier-model call, not a single pass over your document. The most expensive part is the deep literature search: it queries the current literature on every run rather than relying on what a model happens to remember, and that search is a real cost we pay each time. We also move to newer frontier models as they are released instead of settling on an older, cheaper generation to protect our margin. For a proposal that represents years of work, and at a fraction of what a human grant consultant charges for the same read: we would rather be worth the price than be the cheapest.
What if I'm not happy with my report?
Contact us at hello@grantpanel.ai with the documents you submitted (proposal and solicitation) and the report you received. Since your files are deleted after each run, we'll need you to re-send them. We will review what happened, and if the report fell short, refund you or credit your account.
Can my university license GrantPanel?
Yes. Research-development teams can contact hello@grantpanel.ai for institutional access, privacy review, and purchasing.
Is GrantPanel affiliated with a funding agency?
No. GrantPanel is an independent preparation tool. It is not affiliated with, endorsed by, or connected to any funding agency or foundation.
Volume pricing, security review, or a question.
Research-development offices, IT security teams, and PIs with a deadline all reach us the same way. We answer within one business day.
- hello@grantpanel.ai
- Response time
- One business day, usually sooner
- For security teams
- Data-flow summary, subprocessor list, and retention schedule on request
Put your proposal in front of our panel first.
Upload your proposal and the agency's solicitation, and get a full simulated panel review in about 5–15 minutes.
$79 per proposal, up to 3 runs. No subscription required. See pricing →