




Watch our founder Sean Blagsvedt share insights from the AI Agriculture Landscape, covering architectures, evaluations, and investment recommendations for global impact organizations. He outlines how AI can be scaled responsibly to meaningfully serve smallholder farmers worldwide and help them thrive.






FarmerAI is a multilingual, AI-powered agricultural advisory chatbot built by Gooey.AI in partnership with Opportunity International to support smallholder farmers in Kenya, Malawi, and Ghana. Accessible via WhatsApp, SMS, voice notes, and images, FarmerAI delivers on-demand, localized farming advice grounded in vetted agricultural knowledge, helping farmers get timely guidance on crops, livestock, and inputs—often reducing wait times from days to seconds.
Farmer.Chat is a multilingual, AI-powered farmer advisory service built by Gooey.AI and Digital Green to deliver trusted, hyperlocal agricultural guidance to smallholder farmers via WhatsApp, voice, and web. Grounded in vetted extension content and farmer knowledge, it enables farmers to ask questions in their own language and receive real-time, practical advice on crops, inputs, and climate challenges, bridging critical gaps in access to agricultural expertise.
Gooey.AI partnered with the Gates Foundation to tackle the challenge of building AI that works for low- and middle-income farmers in low-resource settings. By developing and evaluating speech, data, and AI pipelines and deploying advisory bots on WhatsApp and non-smartphone devices, Gooey helped scale agricultural AI in the field. This work has reached roughly 168,000 users across Farmer.Chat, DigiFarm, and Malawi, while also shaping ecosystem-wide decisions on where to invest for impact.
As part of our work with the Gates Foundation, we assessed the performance of leading LLMs on agricultural intelligence by comparing their responses to a golden Q&A set of common smallholder farmer questions. Models including Gemini 3, GPT-5.2, KissanAI, LLaMA-4 Maverick, Sarvam.AI, GPT-4o, and AgriLLM (Qwen-3) were evaluated against expert-validated English datasets from Clear Global and Opportunity International.















