AI-Powered Farmer Intelligence for Inclusive Credit, Advisory, and Climate-Resilient Agriculture

Across Africa, over 60% of the population depends on smallholder agriculture, yet more than 70% of smallholder farmers remain excluded from formal financial services due to lack of collateral, credit history, and verifiable farm records. Traditional credit models rely on fixed assets and historical banking data, automatically excluding women and youth, who make up over 50% of the agricultural labor force but own less than 20% of titled land. At the same time, public extension systems serve fewer than 1 in 5 farmers, leaving most without timely agronomic advice. Climate shocks now account for up to 30–40% yield losses annually in rain-fed systems, while rising compliance requirements such as EUDR are restricting market access for farmers without traceable production data. As a result, farmers face delayed or denied credit, low-quality inputs, preventable crop losses, and exclusion from higher-value markets. Lenders, buyers, and governments lack real-time, farm-level data to assess risk, productivity, and sustainability, increasing financing costs and limiting scalable intervention. Shamba Records addresses this gap by applying AI to last-mile farm data. Through machine learning, localized language models, predictive analytics, and blockchain traceability, we convert fragmented farm activity into verifiable, actionable intelligence, enabling inclusive credit scoring, personalized advisory, early risk detection, and transparent farm-to-market records. This unlocks productivity, financial inclusion, and climate resilience at scale.

Shamba Records is a data-driven agri-fintech platform on a mission to close the $300 billion credit gap for smallholder farmers in Africa. We transform fragmented farm data into verifiable digital identities and financial profiles. Our integrated platform combines an AI-powered credit scoring engine, digital farmer wallets, cooperative management tools, and blockchain-enabled traceability. We target women and youth, who are disproportionately excluded from formal finance. By leveraging alternative data—satellite imagery, mobile money history, and transaction records—our AI models de-risk lending, enabling financial institutions to offer loans to previously "invisible" farmers. Our solution drives a 30% targeted increase in farmer income by facilitating access to inputs, climate-smart advisory delivered in local languages, and premium markets. We are building transparent, investable agricultural value chains from the ground up.