Why an LLM cannot do your grant search
A chat session can summarise a solicitation beautifully. What it cannot do is watch, remember, or check — and those are the three things that decide whether you miss money.
This is a fair question and it deserves a straight answer, because we build a product in a category where "just ask an AI" is a legitimate alternative for part of the job.
Here is the honest boundary.
What a chat session is genuinely good at
Summarising a long announcement. Explaining a mechanism you have not used before. Drafting a first pass at specific aims. Turning your science into the vocabulary a reviewer expects. These are real, and if you are not using an LLM for them you are working harder than you need to.
What it structurally cannot do
It has no clock. It answers when asked. It cannot notice that a deadline moved last Tuesday, because nothing is running when you are not there. Grant deadlines move, solicitations get amended, and nobody emails you.
It has no memory of your situation. Each session starts over. It does not know which twelve opportunities you already reviewed and rejected, which officer you emailed in March, or what you said to them. Being told about the same grant for the fourth time is not intelligence.
It has no live database. This is the one that costs money. Asked for grants in your area, a model will produce a confident, plausible list. Some of it will be real. Some of it will be programmes that existed two years ago, and a number of the deadlines and award ceilings will be generated rather than retrieved. A fabricated deadline is worse than no deadline, because you plan around it.
The test to run yourself
Ask any model for open funding opportunities in your indication with deadlines and award ceilings. Then check each one against the agency's own page.
The failure mode is not that everything is wrong. It is that most of it is roughly right, which means you have to verify all of it — and the verifying was the work.
What we do differently
We read the real records — Grants.gov and NIH RePORTER — and every figure we print links back to its source. Where an agency does not state an award ceiling we print "not stated" rather than filling the gap, because a number invented to make a card look complete is exactly the failure above.
Then we keep reading after you close the tab: what is new, what moved, what entered the two-week window. And we check eligibility against your company, so "can we even apply" is answered before the writing starts.
An LLM does the parts of this that are about language. The parts that are about a clock, a memory, and a real database are not a prompt problem.
Stop finding out too late.
SixthGrant reads every federal solicitation daily, checks each one against your company, and tells you what changed.
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