Agriculture & Smart Farming

Edge-first Kubernetes for precision agriculture

Agriculture happens in the field, not the data center. We build Kubernetes platforms that extend to edge locations, handle IoT scale, and work when connectivity doesn't.

Talk to an engineer See our capabilities

1000s

Edge devices managed

<50ms

Edge processing latency

99.9%

Platform reliability

40%

Infrastructure cost savings

Capabilities

Purpose-built for agriculture

Edge Computing at Scale

Agriculture happens in the field, not the data center. We deploy Kubernetes to edge locations for real-time processing of sensor data, imagery, and equipment telemetry.

Edge KubernetesDisconnected operationLocal ML inferenceFleet management

IoT Data Pipeline

From soil sensors to satellite imagery, agriculture generates massive data streams. We build scalable pipelines that ingest, process, and analyze IoT data.

Sensor ingestionTime-series storageStream processingData aggregation

Precision Agriculture ML

Deploy machine learning models for crop health, yield prediction, and equipment optimization — on infrastructure designed for agricultural data science.

Image analysisYield predictionEquipment optimizationWeather integration

Remote Operations

Agricultural operations span vast areas with limited connectivity. We design systems that work offline and sync when connected.

Offline-first designSync when connectedLow-bandwidth optimizationSatellite connectivity

Related Case Study

Improving real‑time data analytics with Kubernetes

Enterprise Data Platform

The Challenge

A large enterprise needed to process high-volume real-time data streams for analytics and decision-making — a challenge mirrored in precision agriculture where sensor data, telemetry, and imagery must be processed at scale.

Why This Matters for Agriculture

Same real-time data pipeline architecture applies to IoT sensor streams
Kubernetes auto-scaling handles seasonal data volume fluctuations
Edge-to-cloud data flow patterns proven at enterprise scale

Results

Real-time

Data processing

10×

Throughput increase

99.9%

Pipeline uptime

Auto

Scale on demand

Read the full case study →

Improving Real-Time Data Analytics with Kubernetes

FAQ

Frequently asked questions

Rural operations often have limited or unreliable connectivity. We design edge systems that operate independently, store data locally, and synchronize when connectivity is available. This includes satellite connectivity options for truly remote locations.

Yes. We build infrastructure for agricultural imagery analysis — from ingestion to ML inference. This includes multispectral analysis, NDVI calculations, and change detection at scale.

We implement fleet management for edge Kubernetes clusters — centralized configuration, rolling updates, monitoring, and remote troubleshooting. You get visibility into thousands of devices from a single pane of glass.

Agriculture has peak seasons. We design infrastructure that scales with your operations — high capacity during planting and harvest, cost-optimized during off-seasons.

Technology Partners

AWS Microsoft Azure Google Cloud Red Hat Sysdig Tigera DigitalOcean Dynatrace Rafay NVIDIA Kubecost

Insights

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US-based team · All US citizens · Continental United States only