IoT Software Development Company
Deploy Smarter Connected Devices with AI at the Edge
We build the firmware and cloud infrastructure to manage fleets of thousands of connected devices securely.
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4.9/5
26 client verified reviews
Unlock the power of AIoT with intelligent connected solutions. We build AI-powered edge computing, predictive maintenance, and smart device analytics platforms.
IoT Device Firmware
C/C++ firmware for microcontrollers with MQTT/CoAP connectivity and OTA update capability.
IoT Cloud Platforms
Device management, data ingestion, rule engines, and dashboard visualization for connected devices.
Edge Computing Applications
On-device ML inference, data preprocessing, and local decision-making for latency-sensitive use cases.
LoRaWAN/NB-IoT Integration
Low-power wide-area network connectivity for battery-powered sensors in agriculture and smart cities.
Device Provisioning Systems
Zero-touch provisioning, certificate management, and secure bootstrapping for manufacturing.
Bluetooth Low Energy Apps
Mobile apps for device pairing, configuration, and data sync with BLE peripheral devices.
Industrial IoT Gateways
Protocol translation between Modbus/OPC-UA and cloud platforms with local buffering.
IoT Security Implementation
Secure boot, encrypted communications, and over-the-air key rotation for embedded devices.
Time-Series Data Processing
High-volume sensor data ingestion with time-series databases (InfluxDB, TimescaleDB) for analytics.
IoT Software Development Company
Transform industries, enhance efficiency, improve decision-making, and enable new opportunities for connectivity and automation.
We Build Using Market Standards
MQTT / MQTT-SN, CoAP, LWM2M, Zigbee / LoRaWAN, X.509 Certificates
We bring together developers and professionals who know connected devices
Building IoT software requires more than knowing Python, React, AWS, or AI. Our team includes professionals with direct experience across device firmware, edge inference, fleet provisioning, and telemetry pipelines.
Our forward deployed engineers build production AI every day for our clients and use our primitives to accelerate time to market. That's the difference between a team that's used AI and one that ships it.
Juan Pablo Lorandi
CTO, Azumo · 25+ years of software architecture experience.
Certified Claude Architect
What domain experience changes in development
IoT domain expertise reduces the translation gap between your device and platform teams and the engineers building the software. We understand the firmware, provisioning, and telemetry data the application has to support.
Understand how firmware, provisioning, and cloud dashboards fit together across a device fleet.
Translate anomaly detection and sensor thresholds into on-device machine learning models.
Build secure boot, X.509 certificate provisioning, and key rotation into every device.
Connect MQTT, LoRaWAN, and Modbus/OPC-UA feeds into time-series data pipelines.
Bandwidth costs and latency make it impractical to send every sensor reading to the cloud for processing.
We deploy TinyML models directly onto microcontroller units. These models detect vibration or temperature anomalies locally, triggering alerts without needing constant cloud connectivity.
Predictive Maintenance
Machine learning that forecasts equipment failures enabling proactive maintenance and preventing downtime.
Anomaly Detection
Real-time AI that identifies unusual patterns performance issues and security threats.
Edge Intelligence
Lightweight models that process data locally on devices enabling instant decisions.
Energy Optimization
AI that analyzes consumption and optimizes energy use reducing costs and carbon footprint.
Computer Vision
Systems that inspect products detect defects and ensure quality in manufacturing.
Asset Intelligence
ML that tracks equipment predicts maintenance and optimizes utilization.
We can also build intelligent workflows with AI Agents
We follow a structured, IoT-first AI development process that prioritizes reliability, edge performance, and operational continuity
IoT AI Discovery
We understand sensor systems edge computing and operational technology. We design AI that improves reliability and operational efficiency.
Sensor Data Integration
We integrate data from IoT devices sensors industrial equipment and control systems into AI platforms.
AI Model Development
Our engineers develop IoT AI including time series models anomaly detection and edge-optimized algorithms.
Operational Testing
We test AI in real environments validating accuracy latency and reliability before production deployment.
System Integration
We integrate AI with IoT platforms edge devices and operational systems ensuring real-time performance.
Continuous Model Refinement
Post-deployment we monitor AI performance equipment health and operational metrics refining models for accuracy.
Leaders prefer us for AI development
We hire engineers who understand the protocols and compliance requirements that govern your business.
Their team consistently brings thoughtfulness, professionalism, and ownership, making them a valued extension of our internal team.
.avif)
Behind every huge business win is a technology win. So it is worth pointing out the team we've been using to achieve low-latency and real-time GenAI on our 24/7 platform. It all came together with a fantastic set of developers from Azumo.
.avif)
We’ve been working with Azumo since our founding. Their team has been great to work with. We built out a massive AI based data platform with their help. They can handle just about anything.
The work was highly complicated and required a lot of planning, engineering, and customization. Their development knowledge is impressive.
They know a lot about the products they build on and were very responsive. Their project manager broke down barriers and explained all the intricacies of the custom software development effort in a way that was easy to understand.
We hire engineers who understand the protocols and compliance requirements that govern your business.
We invest in our AI native nearshore software engineers and it shows.
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IoT Software Development for AI at the Edge and in the Cloud
Device fleets, sensor networks, and telemetry pipelines require AI built by teams who understand the constraints of embedded systems and the scale of cloud processing.