Industries / Agriculture
We build cloud-backed sensor pipelines, cross-farm dashboards, and data infrastructure for grain, oilseed, livestock, and horticulture operations managing multiple sites.
The problem
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.
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.
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.
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.
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.
Case Study
Real-time cross-farm visibility from any device. Data loss from equipment failures eliminated. Client IT team operates independently using the Terraform codebase.
Read case study →Insight
Farm IoT data pipelines fail for reasons that don't appear in industrial IoT textbooks. Intermittent connectivity, power constraints, and device heterogeneity require different architectural decisions.
Read insight →We build store-and-forward architecture at the device level. Sensors buffer data locally (on the device or a nearby edge gateway) when connectivity drops, then automatically sync when the connection resumes. The ingestion layer handles out-of-order records without treating them as errors. This is fundamentally different from industrial IoT architectures that assume continuous connectivity.
Yes. Large agricultural operations typically run equipment from multiple vendors purchased over 10 to 15 years, communicating over MQTT, Modbus, proprietary cellular protocols, and HTTP. We design the ingestion layer with protocol adapters as first-class components. Each adapter normalises incoming data to a common schema. Adding a new device type means adding one adapter without rebuilding the pipeline.
The dashboard provides a portfolio view across all sites with drill-down to individual devices. It is accessible on mobile with variable connectivity, designed for field managers checking conditions in bright sunlight. Configurable alerts fire via SMS and email for grain temperature, soil moisture, device connectivity loss, and other conditions your operations team defines.
A typical engagement runs 8 to 12 weeks depending on the number of sites and device types. This includes device audit, IoT Core provisioning and onboarding, pipeline build, dashboard and alerting configuration, and full handover with Terraform documentation. Our agricultural IoT case study connected 40+ devices across 3 sites in 10 weeks.