Overview
AgriWise is a digital agriculture platform I built solo. It connects soil and crop guidance, mandi prices, a cultivation calendar, a marketplace, and loan and scheme navigation.
The problem
Indian farmers had no single platform connecting soil data, market prices, weather, and government financial schemes — just fragmented, unreliable sources.
The approach
A full digital agriculture ecosystem: AI-powered soil and crop recommendations, real-time pricing and 30-day forecasts across 2,500+ mandis, a cultivation calendar with weather alerts, a supply-chain marketplace, and loan/scheme navigation — in 22 Indian languages.
The product, live
The pages worth opening. Each one is the real product — click through.
01HomeThe farmer path: soil, prices, cultivation, supply, and finance.
02Soil analysisEnter a soil test and get a crop recommendation.
03MarketPrices from 2,500+ mandis, with alerts and a live table.
04CultivationA calendar for the season, with weather in the plan.
05Supply chainInputs, equipment, and buyers in one marketplace.
06FinanceLoans, insurance, and the schemes that pay for the season.
07WeatherForecasts and risk alerts for a specific place.What it does
What to grow
Soil and crop recommendations, plus a cultivation calendar with weather alerts.
What it is worth
Prices and 30-day forecasts across 2,500+ mandis, next to a supply-chain marketplace.
How it gets paid for
Loan and government-scheme navigation, in 22 Indian languages.
How I worked
Treated it as several domains that had to feel like one product: agronomy, markets, weather, and finance.
Shipped module by module until a farmer path crossed all of them.
Used a model only where it changed a recommendation, not as a layer on every screen.
Challenges
Fragmented sources
Prices, weather, and schemes did not live together. A forecast is useless if tomorrow's action is still unclear.
Eight modules, one product
The language, the navigation, and the data had to stay shared or the platform would feel like eight websites.
Where AI helps
The hard call was where a model added signal and where it was noise on top of data that was already enough.
What shipped
Live at agriwise-mauve.vercel.app
8 integrated modules — soil intelligence, 2,500+ mandi price forecasts, cultivation planning, and financial-scheme navigation.
- Next.js
- Tailwind CSS
- Turbopack
What I learned
How to scope and ship a genuinely multi-domain product — agronomy, commodity markets, weather, and government finance — as one coherent platform, and where AI adds real signal versus where it's just noise.
