AI dairy farming has achieved its most ambitious implementation to date. It’s not a Silicon Valley research lab or a European agricultural technology campus. In villages in the Indian state of Gujarat, 3.6 million female milk producers are currently being served by an AI assistant named Saraben.
Amul, the world’s largest dairy cooperative, has launched what it calls ‘Amul AI’. It is a platform built on 50 years of cooperative data and designed to provide personalized guidance in their native language, 24 hours a day, to every farmer in the network.
Amul was launched just ahead of India’s AI Impact Summit 2026 and is supported by the Ministry of Electronics and Information Technology (MeitY) and EkStep Foundation. This is a test case for whether the kind of AI being discussed in boardrooms and policy forums around the world can actually reach the last mile.
Introducing Sarlaben: AI Dairy Assistant
Sarlaben is powered by one of the most comprehensive agricultural data repositories in India. This can be accessed via the Amul Farmer mobile app (already downloaded by over 1 million users on Android and iOS), as well as through voice calls for farmers using feature phones and landlines.
The system is integrated with Amul’s Automated Milking System (AMCS) and the Pashudhan application and can provide personalized cow-specific guidance.
What makes Amul AI different from most agricultural chatbots is the scale of its training data. The platform was built on a digital backbone that manages over 200 million (2 billion) milk procurement transactions annually, veterinary treatment records from over 1,200 doctors covering nearly 30 million cows, approximately 70 million artificial inseminations performed annually, ISRO satellite imagery for fodder production mapping, and cattle census conducted every five years.
Every animal in the system has a unique ID and individual records such as feed intake, disease history, and milking status are recorded. “Amul AI provides reliable and verified information directly to farmers, instantly and in a farmer-friendly language,” said Jayen Mehta, managing director of Gujarat Cooperative Milk Marketing Federation (GCMMF), which markets the Amul brand.
He described how the platform uses decades of structured data and integrates it with operational systems to help farmers make timely decisions that improve livestock productivity and income.
India’s productivity paradox
India is the world’s largest milk producer, with milk production expected to reach 347.87 million tonnes in 2024-25, more than double the United States’ production of 102.7 million tonnes, according to the Ministry of Animal Husbandry and Dairying. Although India leads the world in production, its milk production per cow remains among the lowest in the world.
The reason is structural. India’s dairy sector is characterized by small herd sizes, poor feed quality, limited access to veterinary care in rural areas, and a widespread lack of awareness about modern breeding and husbandry practices. Amul’s network spans over 18,600 villages in Gujarat, providing farmers with over 3.5 million liters (35 million liters) of milk every day.
However, information asymmetry has long been a bottleneck. Farmers faced with sick animals in remote villages in the middle of the night have little recourse. Amul AI is designed to fill that gap.
The platform, which will initially be available in Gujarati, the main language of the cooperative’s farmer base, is built on the government’s Basini multilingual framework and could in principle be extended to India’s 20 languages, potentially widening Amul’s presence in 20,000 villages across 20 states.
cooperation model
The technology story here is inseparable from the institutional story. Amul’s collaboration, built over 50 years under the first White Revolution, created the data infrastructure that makes Amul AI possible.
Many private agritech startups work in the opposite direction, first collecting data and then developing products. Amr already had the data. What was needed was a way to make it viable at the farmer level.
Experts tracking the dairy technology sector consider this to be important. Sreeshankar Nar, founder of dairy technology startup Brainwired, points to three specific challenges that Amul AI can meaningfully address: farmer awareness, access to quality veterinary guidance, and connectivity to grazing and feed resources.
“If AI can integrate local dialects of Indian languages, India can achieve White Revolution 2.0,” Nair said, pointing to the transformative potential of vernacular AI in areas where not all farmers speak the same dialect.
Saswata Narayan Biswas, director of the Anand Institute of Rural Management (IRMA), an institution closely related to Amul’s founding principles, frames Amul as an AI embedded in a collaborative framework. It will be “not a technology upgrade, but a vehicle for comprehensive rural transformation.”
For Biswas, the specific capabilities that Amul AI brings – predictive disease detection, estrus tracking, optimized feed formulation, and localized weather risk advisories – are capabilities that Amul has been building over the years. AI will accelerate and democratize them.
Scale and future testing
The launch received support from the highest levels of government. Gujarat Chief Minister Bhupendra Patel launched the platform and confirmed that it will be unveiled at the 2026 AI Impact Summit. The cooperative has recognized MeitY and the EkStep Foundation, an open digital infrastructure nonprofit, as partners in building the AI layer.
Farmers not affiliated with Amul can also access general dairy and livestock information through the app. At its current scale, Amul AI already covers more cattle (nearly 30 million head) than most national veterinary databases around the world.
As with most AI deployments at a population scale, the harder question is whether the tools will help the people who need them most. The farmers who are most likely to benefit first – those who are already familiar with smartphones and already connected to Amul’s digital system – may not be the ones with the greatest information gap.
The rollout of Basini-enabled dialect support, the adoption rate of feature phone users who rely on voice calls, and whether AI-driven recommendations lead to measurable yield gains will determine whether this is a true White Revolution 2.0.
Amul built an AI system based on half a century of real cooperative transactions, real animals, and real farmers. Such infrastructure is perhaps the most reliable foundation for large-scale AI dairy farming. Whether that promise is fulfilled depends on execution and whether Sahlaben’s voice is heard in the final miles. The one that has always been the most difficult to overcome.
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