AI / Solutions
Indonesia's agriculture economy runs on smallholders with mobile phones. The most powerful place to put AI is the one that puts a good agronomist in every farmer's WhatsApp, photo, text, or voice, in Bahasa and regional languages. We build agricultural AI for agritech, cooperatives, food-supply operators, and government-backed programs: yield prediction from soil and satellite, field-specific advice over messaging, pest and disease detection from phone photos, supply-chain orchestration from field to buyer.
Indonesian agriculture is a scale problem, not a capability problem. Smallholder farmers already have phones, already use WhatsApp, already send photos. What they don't have is the agronomist-on-call that can turn a blurry leaf photo into planting guidance, weather data into a pest-risk alert, or a soil sensor reading into a fertilizer schedule. We build that layer, multilingual, photo-capable, voice-capable, season-aware, for the agritechs, cooperatives, and government-adjacent operators working to lift Indonesian yields without lifting complexity onto the farmer.
Four phases from the first field data to a production system that scales across cooperatives and districts.
We map the farmer's year. Which crops, which regions, which pain points, yield variance, pest pressure, weather risk, market-access gaps? Which data sources are already available: soil sensors, satellite, weather APIs, farmer chat logs, cooperative records? Which channels do farmers actually use, WhatsApp, IVR, photo uploads?
A six-week pilot on one crop or one region or one channel. Typically: photo-based pest detection on WhatsApp, yield prediction for a specific crop, or multilingual agronomy guidance on IVR. Pass/fail on farmer engagement, advice accuracy (agronomist-reviewed), and retention across a season.
Evaluation harness: advice accuracy against agronomist ground truth, language quality across Bahasa + regional dialects, channel reliability at field-condition network latency, outcome measurement where possible. Partnerships with agronomy experts and extension workers to validate at ground level.
Handover to your agritech, cooperative, or program-operator team. New crops, new regions, new languages added as modules. You get the model retraining pipeline, the channel integrations, the outcome dashboard, and an ongoing relationship for seasonal evolution.
Four disciplines that together put a good agronomist in every farmer's WhatsApp.
Machine-learning models over soil data, weather history and forecast, satellite imagery, and farmer-provided field records. Output: field-specific planting plans, expected yield windows, and risk flags. Delivered as scheduled WhatsApp messages or cooperative dashboards.
Phone-photo-based classification of pest and disease indicators. Trained on Indonesian crop varieties, Indonesian field conditions, and Indonesian smartphone photo quality. Treatment recommendations delivered in Bahasa + regional languages.
WhatsApp-native conversations in Bahasa, Javanese, Sundanese, and other regional languages. Text, voice notes, photo uploads, IVR voice for farmers with low literacy. Grounded on your agronomy knowledge base with citations.
From field to buyer: harvest window prediction, price signals, cooperative coordination, buyer matching, contract and payment tracking. For agritechs and cooperative operators looking to close the loop between production and market access.
The proof is in Indonesian fields, already measured, already recognized internationally.
We built the vision and extraction models behind a smallholder-facing agronomy system. Farmers submit photos, voice notes, or text in Bahasa and regional languages; the system returns field-specific advice on planting, nutrition, and pest management. In 2025 the platform took silver at the Salesforce Tech4Good Awards.
We built AI-powered soil monitoring, yield forecasting, and supply-chain orchestration for an Indonesian agricultural platform, turning field-level data into daily decisions for farmers and buyers, with sustainability at the core of the model design.
The Better Earth Ventures and Climate-KIC Agritech ClimAccelerator Singapore program selected Indonesian-market agritechs with AI-powered agriculture solutions in 2025, evidence that agricultural AI built for Indonesia is drawing capital, mentorship, and validation at a regional level.
The channel logic behind every deployed agritech AI in Indonesia, why text, voice, and photo over WhatsApp beat app-native interfaces at smallholder scale, and what good UX looks like on a 3G connection.
Practical lessons from deploying multilingual agronomy AI, Javanese and Sundanese handling, IVR voice for low-literacy users, tokenizer choices that survive code-switching and poor connectivity.
Three structural models for deploying smallholder AI at scale, agritech direct-to-farmer, cooperative-mediated, and anchor-buyer-led. What each model does well, where each breaks, and how to pick.
The engineers and language leads who take satellite and soil data to a farmer's WhatsApp.
Tell us the highest-leverage moment in the farmer's year, planting choice, pest diagnosis, harvest timing, buyer matching, cooperative coordination. We'll scope a six-week pilot with real farmer-side engagement on WhatsApp, with advice reviewed against agronomist ground truth.
Start a project