Sound Familiar?

Challenges we hear from Agriculture teams

We've built for this exact problem set. Here's what we typically hear before we start working together.

Crop stress is visible from the sky, but you're walking the fields

By the time a field scout identifies a problem, the optimal intervention window has passed.

Fertiliser and pesticide use is volume-based, not data-driven

You're applying inputs uniformly across heterogeneous fields. Some zones get too much. Others get too little.

Yield forecasts are based on last year's memory

Planning procurement, storage, and logistics on anecdotal estimates leads to wastage or shortage every season.

Advisory services can't reach every farmer at the right time

Your agronomy team can't be in every field at the right growth stage. Extension coverage is thin.

Live Builds

What we've built for Agriculture

Every case below is live in production. IP fully transferred to the client on delivery. We don't retain access or use your data for model training.

Drone Vision AI7.1

Drone-Based Crop Health Monitoring Across 10,000 Acres

An agritech company managing input advisory for 10,000+ acres across 3 districts had no scalable way to monitor crop health between ground visits. We built a drone vision AI system that processes multispectral imagery to generate NDVI maps, classifies stress zones by type (water, nutrient, pest), and auto-generates plot-level advisory reports that agronomists can review and send to farmers within hours of a drone flight.

Outcome: Crop stress detected avg. 11 days earlier. Input usage reduced 23% through zone-targeted application. Yield variance between plots reduced 31%.

11 days

Earlier stress detection

23%

Input reduction

31%

Yield variance reduction

Drone imageryNDVI analysisStress classificationPlot-level advisory

Yield Forecasting7.2

AI Yield Forecasting & Market Advisory for FPO Networks

An FPO network representing 5,000+ farmers had no data infrastructure to forecast yield or advise on market timing. Members were selling at harvest price regardless of market conditions. We built a yield forecasting model trained on satellite imagery, soil data, and historical yield records, combined with a market price prediction layer and WhatsApp advisory push.

Outcome: Average net realisation per quintal improved 18% through better market timing. FPO collective bargaining position strengthened with supply forecast data.

18%

Net realisation improvement

5,000+

Farmers on platform

Advisory delivery

Satellite dataYield modellingMarket predictionWhatsApp advisory

Better farm decisions start with better data from above.

Ready to build something like this?