Launching today

GeoPard Agriculture AI Assistant

Launching today

A reasoning agronomist behind every acre

16 followers

A reasoning agronomist behind every acre

16 followers

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GeoPard AI plans precision ag work the way an agronomist would. Ask one question and it reads your field data (soil tests, yield, satellite, topography), asks about nutrient targets and thresholds, shows every coefficient, and validates the plan against agronomic rules before generating. You approve, the prescription lands on the machine (John Deere, CNH, AGCO). Grounded in your own knowledge base, never trains on your data. Benchmarked on 503 CCA-style questions, all scores published.

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Launch tags:Artificial Intelligence•Data & Analytics•Farming

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Hi Product Hunt! Dmitry here, founder of GeoPard. The problem: precision ag has plenty of data and maps, but the decision layer is still a human bottleneck. Most "ag AI" is a chatbot bolted onto a map. What we built: a reasoning assistant that plans like an agronomist. It asks the right questions (nutrient target, yield goal, thresholds), shows every coefficient it uses, dry-run validates the equations, and only then builds the prescription. You approve, and it lands on the machine: John Deere, CNH, AGCO. What makes it different: Grounded in YOUR knowledge base: manuals, protocols, trial rules. Cited in every answer. Your data never trains any model. We publish our benchmark scores: 503 CCA-style agronomy questions, per-category results from 77.5% to 100%, including the ones we are still improving. The question set stays private to keep future scores meaningful. Works for a solo grower and a 300,000 ha (1M acres) agriholding. White-label for dealers. And via MCP you can bring your own AI: ChatGPT, Claude, or custom agents. Free trial at app.geopard.tech, 2-min video on the page. One question for you: what field decision would you never hand to an AI? Genuinely curious where people draw the line. I'll be here all day. Ask me anything: agronomy, the benchmark, validation pipeline, MCP.

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2d ago

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p/geopard-agriculture-ai-assistant

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2d ago

We publish our AI's exam scores, including the one we failed. Should this be standard?

We build GeoPard AI, a reasoning assistant that plans farm prescriptions (fertilizer, seeding) from field data. Launching here Friday (tomorrow).

Before launch, one decision we made that I'd love this community's take on: we test the assistant on 503 CCA-style agronomy questions (the exam human crop advisors take) and publish every category score. 100% precision ag, 95.8% soil and water, 93.8% crop management, 92.9% nutrient management, and 77.5% pest management, the one we haven't cracked yet.

The question set stays private so it can't leak into training data and inflate future scores. But the results, including the weak one, are public.

Our logic: if an AI recommends what to put on someone's field, they deserve to know exactly where it's strong and where it isn't. Black-box confidence is how you lose farmers forever.

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