Compares a set of the latest sovereign and private models for their responses being similar to our golden QnA of common small shareholder farmer questions and answers (in English) from ClearGlobal and Opportunity Intl. What makes a good golden eval QnA? Click here to read the case study.

Takes a precise African location as lat/long as input and returns a JSON object representing the soil's "nitrogen_total", "phosphorous_extractable", "potassium_extractable", "pH" and "texture_class" using the ISDA Soil API. When used with small shareholder advisory tools, this provides the basis of personalized fertilizer recommendations that shown to yield season on season yield improvements of 16-46%. Read more on Gooey.AI Agriculture work

AI-powered voice agronomist helping African farmers with locally relevant crop and fertilizer advice. It uses Omnilingual ASR, farmer data and ISDA soil analysis to understand each farm and deliver practical recommendations through voice or text.

Aimed at Kenyan farmers and extension agents, Farmer.CHAT can analyze crop photos, provide precise weather, price and soil analysis based on the user's lat /long. It understands audio messages in most languages, including English and Swahili. More info and technical walkthroughs can be found here