Build Notes #5: The Unglamorous Week — Wiring Up Real Farm Data

This was written eight days before YieldAI Global shipped, so every “pre-MVP, no users” line below describes where we were in July 2026, not where we are now. YieldAI Global went live on 15 July 2026 and is available in India, the USA and Canada at yieldaiglobal.com.

The more important correction is about the word “live”. This post describes India’s AGMARKNET feed as live, which is accurate — it updates daily. It does not make that claim about the USA or Canada, and it should not be read as doing so: USDA NASS and Statistics Canada publish farm prices monthly. A later edition of this newsletter is entirely about finding that claim stated too broadly in six other files, and then in our system prompt. The original wording stands below.

AGRIVISION Build Notes — building YieldAI Global in public, pre-MVP, one honest update at a time.

There’s a version of an AI-for-agriculture update that’s all model and magic. This isn’t that one. This week was spent on the least glamorous, most important layer of the whole product: the actual data a farmer’s answer has to be built on. If the data is wrong, a fluent AI just tells a confident lie — and in farming, a confident lie can cost someone a season.

So before any of the clever parts, I wired up the boring, trustworthy foundation. Here’s what that actually looked like.

YieldAI Global is launching first in three countries — the USA, India, and Canada — and each has its own official price source. So I connected to the government feeds directly:

No middlemen, no scraped numbers of unknown origin. If YieldAI tells a farmer a price, it should trace back to an official source — and now it does.

I went in assuming I could plug into a clean feed for everything. That’s not how the real world works.

There is no API anywhere for government farming schemes — the subsidies and support programs farmers most need help navigating. There’s no live feed for India’s Minimum Support Prices either. The honest, industry-standard answer is that this data has to be curated carefully by hand and kept up to date deliberately.

So I did exactly that: 33 government schemes across India, the USA, and Canada — each one’s benefit, eligibility, and how-to-apply checked against the official source — plus a crop reference covering 33 crops with the exact names each government system expects. Tedious? Yes. But it’s the difference between the AI thinking there might be a scheme and here is the real scheme, what you get, and how to apply.

A few honest lessons from the week:

Still pre-MVP. No users, no revenue. This week didn’t produce a flashy demo — it produced a foundation I trust. That trade is one I’ll make every time at this stage.

Next up: putting the AI brain on top of this data so it can answer a real farmer’s question, in their own language, grounded in these numbers — and being just as honest with you about how that goes.

If you work in agriculture, extension, or agronomy and you can tell me where this data model is naive, I genuinely want to hear it. That’s the whole point of building in the open.