/case-studies/moonsyst
agritech · iot + ml · mobile 2020-

Moonsyst.

Smart rumen bolus for dairy and beef farmers.

An IoT sensor that lives in a cow's rumen for years, plus an ML platform that turns biosignal noise into hours-accurate predictions of health events including oestrus and calving.

python pytorch react-native aws iot

At a glance

// the client

A livestock-tech company building sensor hardware and software for dairy and beef farmers across Europe, the Americas, Asia and Australia.

// the challenge

Turning raw rumen biosignal into decision-grade predictions accurate enough to be the system of record for breeding and birthing.

// the outcome

Hours-accurate predictions of oestrus and calving as well as other health events. Now the operational standard for the company.

The problem

What wasn't working.

[ 01 / 06 ]

Existing livestock monitoring depended on visual observation, breeding records, and external sensors that lost signal in barn environments. Farmers were missing oestrus windows and being surprised by calving — both expensive failures. Moonsyst had built the sensor; we partnered to build the ML platform that made the sensor decision-grade. Success looked like predictions farmers could plan a Tuesday around — accurate to within hours, calibrated, surfaced before the event.

Approach

What we did.

[ 02 / 06 ]
edge-light, cloud-heavy
The bolus does minimum work; the platform does the rest. Real-time on-device inference is cost-prohibitive for a sensor that lives in a rumen for years.
regular retraining
Models are regularly retrained on fresh, meticulously preprocessed data. No cow gets confused with another cow.
time-aware models
Handle sensor drift, calibration windows, and the long sparse signal patterns characteristic of bovine biology.
human-in-the-loop
The platform suggests; farmers decide. We didn't automate decisions a vet should make.
// where AI helped
Scaffolding, integration code, synthetic test data generation, model evaluation harness. Feature selection and sensor noise handling stayed engineer-led.

What we built

Four components.

[ 03 / 06 ]
01 · ml-pipeline
ML pipeline

Temperature, pH, motion, and rumination feature extraction. Oestrus and calving classifiers with calibrated confidence.

python · scikit-learn · pytorch
02 · backend
Backend

Sensor ingestion, per-animal state, alerts pipeline, integrations with third-party farm-management systems.

fastapi · postgresql · clickhouse
03 · mobile
Mobile

Farmer-facing alerts, herd overview, calving timeline. Designed for one-handed use in barn conditions.

react-native · ios · android
04 · cloud
Cloud & ops

Batch retraining, real-time inference, observability across 10,000+ deployed sensors.

aws · docker

Outcomes

What it shipped as.

[ 04 / 06 ]

"Alerts were spot on every time, which gave me great confidence to spend more time off-farm without the worry of missed heats."

— farm operator · ireland

"Cows recover faster when they are caught by the system and treated early."

— farm operator · ireland

"Moonsyst is now the system our breeding manager checks before he opens his eyes in the morning."

— farm operator · ireland

The team

Who shipped it.

Team of six, two senior and four medior engineers: ML lead, backend lead, frontend & mobile lead, DevOps.

engagement: dedicated team · ongoing

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