Built on real production systems.

The challenge

Australian dairy farms generate enormous amounts of operational data — milk yields, herd health, feed schedules, vet visits, equipment logs — but most of it lives in disconnected spreadsheets or paper records. Farm managers make decisions with incomplete information, often hours after an issue first appeared. Field workers spend 40% of their time on data entry rather than animal care. And no farm could send sensitive operational data to a cloud AI service.

What we built

AgriTwin is a digital twin platform that integrates all farm data sources into a single live model. At its core is our self-learning agent factory — a meta-system that builds and tunes specialised AI agents from operational briefs without requiring code changes. New agents for “vet scheduling” or “feed plan optimisation” were live in days.

Field workers interact through a voice-commanded interface. They ask questions and issue commands in natural language — while agents handle the underlying data queries, scheduling, and record updates automatically. No app, no screen, no keyboard required.

• →Self-learning agent factory — build agents from operational briefs
• →Voice-commanded interface for non-desk field workers
• →Real-time herd health monitoring with anomaly detection
• →Automated feed plan optimisation from yield data
• →Vet scheduling and farm operations coordination

How we deployed

100% on-premise. All model inference runs on hardware at the farm or at the farm management office. No data leaves the property. No cloud services are called during operation. We ran open-source models fine-tuned on agricultural domain knowledge — achieving the same output quality as cloud models at approximately 5% of the ongoing operating cost.

Results