Industries / Agriculture

IoT and data infrastructure
for large-scale
agricultural operations.

We build cloud-backed sensor pipelines, cross-farm dashboards, and data infrastructure for grain, oilseed, livestock, and horticulture operations managing multiple sites.

Designed for intermittent connectivity, device heterogeneity, and the operational realities of remote farm environments.

The problem

Your sensors are collecting data.
Nobody can see it across sites.

IoT sensors log data locally on farm equipment. When equipment fails, that data is gone permanently. There is no backup, no cross-farm visibility, and no way to compare performance across sites without someone driving out to check.

Agricultural IoT fails differently from industrial IoT. Remote paddocks have no wired infrastructure. Cellular coverage is intermittent. Equipment runs on battery or solar power. Sensors from different vendors speak different protocols. Standard IoT architectures break in this environment.

Operations managers need a single view across all sites that works on a phone with variable connectivity. Alert conditions like grain temperature exceeding threshold or moisture levels outside range need to fire before someone discovers the problem during a physical site visit.

What we build for agriculture
01

IoT Sensor Ingestion Pipelines

AWS IoT Core or equivalent cloud ingestion connecting MQTT, Modbus, and HTTPS sensors across multiple sites. Store-and-forward architecture at the device level so data survives connectivity gaps.

AWS IoT CoreMQTT / Modbus / HTTPSStore-and-forward bufferingPer-device certificate auth
02

Cross-Farm Dashboards and Alerting

Real-time dashboards accessible from any device, including mobile on variable connectivity. Configurable alerts for temperature, moisture, soil conditions, and device connectivity loss via SMS and email.

CloudWatch / GrafanaSMS and email alertsMobile-optimised viewsPer-site and portfolio views
03

Historical Data Storage and Analysis

Dual-path storage: hot path for real-time operations, cold path for historical analysis and seasonal comparisons. Parquet on S3 with Athena for ad-hoc queries without idle infrastructure cost.

DynamoDB / TimescaleDBS3 + AthenaParquet compressionSeasonal trend analysis
04

Protocol Normalisation and Device Management

Protocol adapters that normalise data from heterogeneous device fleets into a common schema. Device shadow for remote configuration. Adding a new sensor type means adding one adapter, not rebuilding the pipeline.

Multi-protocol ingestionDevice shadow / registryFirmware-aware normalisationTerraform-managed infra

Your farm data
deserves real infrastructure.

Talk to us about your operation's data and IoT challenges.