Spiio - IoT Precision Agriculture Platform

Spiio builds precision agronomy solutions using wireless in-ground soil sensors that measure moisture, temperature, salinity, and light levels in real-time. Their platform helps agriculture professionals make data-driven decisions.

We joined to accelerate development of their IoT platform, focusing on data pipeline optimization and microservice architecture improvements for handling high-volume sensor data streams.

Working with IoT systems presents unique challenges: unreliable connectivity, high data volumes, real-time processing requirements, and edge computing considerations.

What We Built

• Enhanced data pipelines processing data from 1,000+ wireless soil sensors
• MQTT-based sensor ingestion with RabbitMQ/Celery for reliable message processing
• Time-series data management in InfluxDB for high-volume IoT storage
• React dashboards for visualizing sensor data and agronomic insights

Key Achievements

• Improved data pipeline reliability for 1,000+ sensor network
• Boosted ingestion throughput with microservice patterns and Kubernetes scaling
• Built analytics tools enabling agronomy professionals to visualize hourly field data
• Strengthened cloud resiliency with automated deployment and monitoring

Technical Challenges Solved

• High-volume time-series ingestion — 40,000+ data points per hour from distributed sensors
• Unreliable connectivity handling — buffering and retry strategies for field sensors
• Real-time aggregation — efficient querying and visualization of time-series data

Backend

• Node.js
• Python
• React

IoT & Messaging

• MQTT Protocol
• RabbitMQ
• Celery

Data Storage

• InfluxDB (Time-Series)
• MongoDB
• Firestore

Infrastructure

• GCP
• Docker
• Kubernetes
• CI/CD