Grazing Guide - An AI guide for regenerative grazing

Inspiration

I’m a shepherd in southern France trying to restore degraded ecosystems.

After studying Holistic Management, I realized the hardest part wasn’t understanding the principles—it was making good decisions with scattered information. Grazing plans lived in spreadsheets, ecological monitoring in PDFs, weather in different apps, and many observations stayed only in my head. I had the data, but no practical way to connect it all and guide my decisions.

I first wanted to build a grazing-planning app, but I had no programming experience.

Then I discovered ChatGPT.

It wasn’t good enough on its own—it didn’t know my farm—but I immediately saw its potential.

So I started building.

With no coding background, just GPT and Codex.

Today my mission is to build the assistant I wished I had in the field, and eventually help other farmers make better decisions and regenerate their land.

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What it does

The Grazing Guide is an data driven AI assistant for regenerative grazing.

Instead of acting like a generic chatbot, it brings together farm records, grazing history, ecological monitoring, recovery periods, weather, and the farmer’s goals before making suggestions.

The goal isn’t to replace the farmer’s judgement. It’s to help farmers make more holistic decisions by assembling evidence, performing deterministic calculations, identifying uncertainty, and using AI only where interpretation adds real value.

Eventually, the assistant will not only answer questions but also help farmers build and manage complete Holistic Planned Grazing plans.

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How I built it

I built the project using ChatGPT and Codex despite having no software-development background.

ChatGPT helps me think through product design, architecture, and implementation. Codex analyzes the repository, writes code, runs tests, and helps me iterate much faster.

Everything currently runs locally. I test new features on my own farm, then use that real-world feedback to improve both the application and the AI assistant.

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Challenges I ran into

The biggest challenge wasn’t generating text—it was building an AI assistant that farmers can actually trust.

Farmers rarely answer with perfect sentences. They say things like:

“Yes, that’s the right herd, but they’re actually in Paddock X.”

The assistant has to understand what is being confirmed, what is being corrected, validate the paddock, preserve the useful information, and continue the conversation naturally.

Building that conversation architecture while avoiding hallucinations has been the hardest—and most interesting—part of the project.

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Accomplishments that I’m proud of

• Building a complete grazing planner with maps, dashboards, and an integrated AI assistant.
• Creating an evidence-first conversation architecture instead of a generic chatbot.
• Designing planning that can be paused, resumed, or left naturally.
• Building a conversation engine with 184 automated tests while protecting real farm data.
• Building all of this without a software background while running a sheep farm.

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What I learned

The biggest lesson is that useful AI isn’t just about better prompts.

For real decision support, AI needs structure. Farm data, deterministic calculations, conversation state, and uncertainty all need to come before AI interpretation.

I also learned that the hardest part isn’t answering questions—it’s understanding what the farmer actually means.

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What’s next for Grazing App

The next step is building a conversation engine that understands confirmations, corrections, references like “that paddock” or “keep the herd but change the date”, and data driven planning decisions.

Beyond that, I want to combine the conversational assistant with a visual Holistic Planned Grazing workspace: an interactive farm map and grazing timeline where farmers can see herd movements, recovery periods, grazing plans, and AI-supported recommendations together.

My long-term vision is an AI partner, on the phone and desktop, that helps farmers regenerate ecosystems while keeping humans firmly in control of every decision.

Built With

• ai
• codex
• css
• fastapi
• gpt-5.6
• html
• javascript
• leaflet.js
• openai
• pydantic
• python
• regenerative