AI in agriculture — built for farms that need yield, not buzzwords

Why ai in agriculture finally works

For years the agritech pitch was the same: satellites, sensors, drones, dashboards. Most pilots died because the farmer couldn't use the dashboard and the dashboard couldn't handle bad weather data. The version of ai in agriculture we build now is different — it works on phones, in vernacular, with imperfect inputs.

Our agribusiness clients are large estates, FPOs, and processors who care about three things: yield per hectare, input cost per kg, and crop-loss exposure. Everything we ship maps to one of those.

What we build for agriculture

Production-deployed AI in our agriculture portfolio includes:

• Yield prediction from satellite imagery + weather + soil — accurate enough for procurement teams to plan around.
• Pest and disease detection from phone-camera images, in multiple Indian languages, with treatment recommendations.
• Irrigation optimisation using soil-moisture inference where physical sensors aren't deployed.
• Crop-loss assessment for insurance — drone imagery + computer vision for parametric and indemnity products.
• Farmer-facing voice agents via TalkTaro — extension advice over a phone call in the farmer's language.

Building for the field, not the lab

The hardest part of AI in agriculture isn't the model — it's the data pipeline. Cloud cover, sensor drift, irregular sampling, and language constraints break most academic models on contact. We design for it from day one.

Every system we ship has a fallback for missing data, runs on weak connectivity, and outputs in the language the user actually speaks.