AI for Grain Farmers: An Honest Field Report

I've watched software get sold to farmers for twenty years

If you've farmed for any length of time, you've sat through the pitches. Precision ag was going to change everything. Then it was digital farm-management dashboards. Then sustainability scorecards. Every wave showed up promising to make your operation simpler and more profitable, and most of it was built and sold to farmers from the outside in — by people who'd never had to get a crop in the ground before a rain.

So I understand the reflex. When somebody tells you "AI is going to transform your farm," the right response is to cross your arms and ask them to prove it.

That's the spirit I want to write in here. I'm not going to give you a brochure. I'm going to give you the honest scorecard after real seasons of farmers using our platform — the places it genuinely earns its keep, and the places it still falls short. Because a field report that only lists the wins isn't a field report. It's an ad.

How I'd grade any farm AI

Before I tell you what I've seen, here's the rubric. When a farmer asks me whether some AI tool is worth the trouble, I'm really asking four questions on his behalf:

Does it save real time? Does it make or save real money? Does it cut risk? And — the one almost everything fails on — does it actually know your operation, or is it guessing?

That last question is where most "AI for farming" falls apart before it starts. So let me start there, with the shortcomings, because that's the part you can't get anywhere else.

Where it still falls short

The biggest shortcoming in farm AI isn't ours — it's the tools most farmers reach for first. If you open a general AI tool like ChatGPT and ask it a question about your farm, it will give you a confident, well-written answer that has no idea your farm exists. It doesn't know your fields, your soils, your bins, your contracts, or your basis. It can recite the textbook nitrogen rate. It cannot tell you what to do on your ground, because it has never seen your ground. That's not advice. It's a magazine article with your name typed at the top.

Grounding the AI in your actual operation is the whole leap, and it's what we built Clevis to do. But I'd be lying if I told you our version is finished, so here's where even ours still has limits today.

First, a word on what does and doesn't flow in. Today you can upload a PDF or a single-page Excel sheet and Clevis will pull the data in just fine. What you can't do yet is hand it one big multipage workbook with your entire operation on it and have everything flow automatically — for now, you connect your data and build it up as you go. I know a lot of you have years of records sitting in one large spreadsheet, so I'll be straight about it: that all-at-once bulk import is the limitation I'm least worried about, because we're only a month or two from it being possible. But today it's a real gap, and I'd rather tell you than let you find out.

Second, accuracy on scanned documents is very good, but it is not perfect. When you snap a photo of a scale ticket or an input invoice, the AI reads the numbers off it — and once in a while, a number comes back a little off. We made a deliberate choice about how to handle that: every value pulled from a document comes with a confidence percentage attached, and it waits for you to review and confirm it before it ever hits your ledger. We would rather ask you to click "confirm" than quietly drop a wrong number into your books. An honest tool tells you when it isn't sure.

None of that is the kind of thing a company eager to close a sale puts in writing. I'm putting it in writing because the trust is the product.

Where it earns its keep

Now the other side of the ledger — and there's a lot on it.

The thing that's been most striking to watch is how farmers respond to our management zones. Plenty of AI tools can spit out a decent agronomic recommendation. But there's a difference between a recommendation and a good recommendation, and the difference is what sits at the center of the math. Most tools are quietly answering one question: how do I grow the most corn? Clevis is built to answer a different one: how do I grow the most crop at the best profit per acre?

Those two questions don't give the same answer. The back corner that you've poured inputs into for years because it bothered you to see it yield light — the most-bushels question says keep pushing it. The profitability question, run across our algorithmic management zones, often says that acre has been quietly losing you money, and your dollars would do more somewhere else. Watching a farmer see that for the first time — not as a theory, but on his own ground, in his own numbers — has been one of the most rewarding parts of this whole thing. Margins are thin enough that the difference between those two questions is the difference between a good year and a break-even one.

The second win is less glamorous and just as valuable: it takes the secretarial work off your plate. Scan a grain contract, and Clevis logs the terms. Upload a scale ticket at the elevator, and watch those bushels report and adjust across your entire operation — landing against the right contract, updating your stored bushels, posting the income — without you re-keying the same numbers into three different places on a Sunday night. I've watched farmers do this for the first time and just kind of sit back, because that hour of paperwork they'd budgeted for the evening was suddenly already done.

It shows up in marketing, too. One client got a pricing trigger as corn slid off a recent high — fired because Clevis knew his operating budget, knew the bushels in his bins, and knew the basis at the elevators within his trucking radius. He locked in a contract that morning instead of noticing the move a day late. And across the board, the time savings are real: one grower told me he was getting back close to ten hours a week of paperwork during planting. That's a full workday he spent in the field, or at home, instead of at the kitchen table.

In each of those cases, compare it to the old way. The old way was checking quotes when you remembered to, scouting when the calendar allowed, and re-entering the same figures by hand across a stack of spreadsheets. The new way isn't magic. It's just that the work gets done, on time, with profitability kept at the center of it.

And the agronomy is just getting started

A lot of what I've described already leans on the agronomy that's baked into Clevis today — the management zones, the profitability math underneath them, the pest and disease pressure it can flag from a photo in the field. That's live right now.

But I'll tell you plainly: the agronomic features we have scheduled to release this season are the ones I'm most excited about on the entire roadmap. I've spent this whole piece pointing out where we still fall short, so I hope you'll take it seriously when I say I think this next wave is going to change what a farmer should expect an agronomic tool to do. I'm not going to describe it before it ships — I'd rather show you working software than sell you a slide. But it's close, and it's coming.

The old way versus the new way

If you read why I built this, you know the pattern I kept seeing in five years of consulting: a 2,500-acre operation has to make the same eight or nine decisions well every year that a 15,000-acre operation does, except the big operation has a team to think each one through and the mid-size farmer makes every call himself, in the truck, between phone calls.

That's the gap this closes. Not by replacing your agronomist, your banker, or your own judgment — but by giving you a second brain that knows the whole operation and keeps asking the profitability question while you're busy keeping the planter running.

Who it's for — and who it isn't

So here's the honest fit.

If you want a tool that actually knows your farm and optimizes for profit per acre instead of bushels for their own sake, it's a strong fit. If you want something that takes the recordkeeping off your plate so you can spend the season farming instead of filing, it's a strong fit.

If what you're after is something to drive the planter or replace walking your own fields, that's not what this is, and it's not trying to be. And if you're not willing to spend the few minutes it takes to confirm a scanned number before it hits your books — well, that's a feature, not a bug, and it's there on purpose.

The honest bottom line

Net it out and the scorecard comes out well ahead — time saved, money made and protected, risk cut, and, most importantly, an AI that's grounded in your operation instead of guessing about it. The shortcomings are real, but they're the kind I can name specifically and, in the case of bulk import, put a date on. That's the difference between a tool built farmer-up and one sold corporate-down.

If you farm corn or soybeans in the Midwest and you want to see where your own numbers land, you're welcome to try it — and you're welcome on the morning brief whether or not you ever pay us a dollar. That's free, that's honest, and that's how we'd like to start the conversation.