Why I'm Building AGRIVISION AI (and Why I'm Telling You Before It's Done)

A note added after the fact: since this essay was written, YieldAI Global has shipped. It is now live and available in India, the USA, and Canada at yieldaiglobal.com, with a free trial — AI crop advice, live government market prices, weather, and government-scheme guidance in the farmer's own language.

I'm leaving the original post below exactly as it was written, pre-launch, because that honesty is the whole point of building in public. Everything under this line reflects where we were then, not where we are now.

I'm Vijesh Reddy Golamari, the founder and CEO of AGRIVISION AI. This is the first post on our blog, and I want to start it the same way I intend to run the whole company: by being precise about where we actually are.

AGRIVISION AI (our legal entity is Agrivisionai Inc, a Delaware C-Corporation) was founded in May 2026 and is headquartered in Detroit, Michigan. We are one founder — me — plus a small group of advisors. We are pre-MVP. Our flagship product, YieldAI Global, is in active development and has not shipped to a single production user. No customers, no revenue, no funding announced. If you came here expecting a launch, I'd rather disappoint you now than oversell you later.

So why write anything at all before there's a product to point at? Because I've decided to build this in public, and building in public only counts if you show up at the awkward early stage, not just on launch day. This essay is the founding document: what we're building, why I left a comfortable career to build it, and what we have and haven't done so far. I'd rather you judge us against an honest baseline.

Across India, Africa, Latin America, and Southeast Asia, a single agricultural extension worker is often responsible for advising roughly 1,200 farming households. Twelve hundred. Usually with no real-time data, no model in their hand, and no tooling beyond a phone and their own hard-won experience. When a pest shows up in a field, the person best positioned to give advice at that exact moment is almost always working blind.

That number is the thing that kept nagging at me. An extension worker isn't a user you scale to one at a time — they're a lever. Make one of them meaningfully better at their job, and the effect lands on a thousand farms at once. That math is genuinely why I started the company. The same AI capabilities I'd been building for well-served corners of the economy could, in principle, point at the people growing the world's food. It felt indefensible not to try.

I won't pretend the field is empty. There's an active agritech and digital-advisory space — government extension programs, point apps for weather, prices, or pest ID. My bet isn't a single clever feature. It's grounding: a system built specifically for the extension worker's actual workflow, where the advice is anchored in real agronomy instead of whatever a model feels like saying.

YieldAI Global is the product we're building first: an AI crop intelligence platform designed for agricultural extension workers and the farming households they serve. The mission of AGRIVISION AI is broader than one product — AI-first tools for global agriculture, designed for more than 40 countries and multilingual by design — but YieldAI Global is where that mission has to prove itself.

The shape we're building toward is a voice-first, multilingual assistant for the field: AI crop advisory grounded in agronomic sources, live weather and pest-and-disease alerts, real-time market-price intelligence, and the ability to turn an extension worker's spoken field notes into a structured visit report. We're designing it around specialized agents — a crop advisor, market intelligence, weather, and field visit — rather than one monolithic chatbot, because the workflows are genuinely different.

Beyond YieldAI Global, we have a roadmap I'll describe honestly as a roadmap, not a product line: CropVision (a planned vision module for crop and disease imagery), AgriSense (planned IoT sensing), and FarmOS (an early-stage operations concept). These are directions, not deliverables. I'd rather name them as ideas than dress them up as features that exist.

After about five years building production AI, I've stopped being impressed by fluency. The hard part of this isn't getting a model to say something plausible about a crop — it's being right, by voice, in a farmer's own language and context, where a confidently wrong answer can cost someone an entire season. Fluent and correct are not the same thing, and in agriculture the gap between them is measured in lost harvests.

So reliability is being treated as a first-class engineering problem, not a polish step. The technical core we're building is a routed multi-model stack with retrieval-augmented generation grounded in agronomic primitives from sources like ICAR, FAO, and state agriculture departments, plus vision for pest and disease identification. And we're building a continuous evaluation harness that grades outputs on separate tracks — factual accuracy, safety, and cultural appropriateness — because 'sounds right' is the failure mode I most want to avoid.

This is also why I keep emphasizing that we're being careful before we ship. It would be easy to put up a demo that looks magical in a controlled setting. It's much harder to earn the trust of someone whose livelihood depends on the answer. That second thing is the only thing worth building.

I was born in Hyderabad, India, in 2000, and moved to the United States in January 2023 for a master's degree. Over roughly five years I've built production AI systems across the industry: LLM evaluation and red-teaming, open-weight model fine-tuning and CLIP multimodal work, enterprise generative AI and retrieval-augmented generation, and end-to-end machine learning. That experience is exactly what I'm now pointing at agriculture.

The honest reason I left is that I kept building incredible systems for problems that were already well-served. At some point you have to ask whether you want to make the comfortable parts of the world slightly more optimized, or take the same tooling somewhere it has never been. I chose the second.

And the timing isn't arbitrary. The ingredients for a credible AI advisory — strong multilingual models, retrieval grounding, affordable multimodal vision — have only recently matured enough to be trustworthy for high-stakes agronomic advice. Meanwhile the field gap hasn't moved: one worker, roughly 1,200 households, almost no tooling. The distance between what's now technically possible and what's actually deployed on the ground is the whole opportunity.

Here's what I'm committing to. I'll share progress one capability at a time, including the parts that don't work yet. I won't post fake metrics, invented testimonials, or a user count we don't have. When something is a hypothesis — like our monetization, which is genuinely still an open question — I'll call it a hypothesis. The fastest way to lose the trust of the extension workers and agronomists I most want in the room is to perform certainty I haven't earned.

A note on identity, because it matters for being findable and credible: there are a couple of unrelated projects that share a similar name in India and Ghana. To be unambiguous — this AGRIVISION AI is Agrivisionai Inc, a Delaware C-Corporation headquartered in Detroit, Michigan, founded in May 2026, with one sole founder and CEO, me, Vijesh Reddy Golamari. We're not affiliated with any other similarly named project.

If any of this resonates — especially if you're an extension worker, an agronomist, a researcher, or a journalist who wants to track a pre-MVP build honestly — I'd genuinely like to hear from you. The site is agrivisionai.org, I read everything sent to hello@agrivisionai.org, our code lives at github.com/agrivisionai-org, and we share progress through AGRIVISION Build Notes on LinkedIn and on X at @yieldaiglobal. We're at the very beginning. That's exactly why it's a good time to come help shape it.