Industrial IoT

Custom Industrial IoT Development

We design and build complete industrial IoT platforms: from device management and data pipelines to real-time dashboards and predictive maintenance. MQTT, CoAP and HTTP protocols. Cloud infrastructure on AWS IoT Core or Azure IoT Hub.

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The industrial IoT challenge

Connecting devices is just the beginning. Extracting value from the data is the real challenge

Most IoT projects fail not because of sensor technology, but because of the platform that connects them. Data ingestion that doesn't scale, dashboards that don't respond in real time, late alerts, incompatible protocols between manufacturers. Industrial IoT needs a software architecture designed from day one to handle millions of messages, thousands of devices and imperfect connectivity conditions.

At Soamee we've spent years building IoT platforms for clients operating connected devices in real-world environments. We're not talking about lab prototypes: we're talking about solar devices in agricultural fields that must reliably transmit data for years without maintenance, water monitoring systems feeding corporate sustainability platforms, and industrial sensor networks generating millions of records per day.

Our approach is to build the data platform that connects your devices with your business decisions. This includes the complete pipeline: telemetry ingestion via MQTT or HTTP, real-time processing with alert rules, time-series database storage, APIs for integration with existing systems, and dashboards that show what matters, when it matters.

We work with the leading cloud services for IoT, including AWS IoT Core, Azure IoT Hub and Google Cloud IoT. We choose the right platform based on your scale, latency, cost and compliance requirements. And we design the architecture so you can start with 100 devices and scale to 100,000 without redesigning.

100K+

Manageable devices

<100ms

Data latency

TLS/mTLS

End-to-end security

MQTT/CoAP

Supported protocols

Capabilities

Complete industrial IoT platform

Every component designed for production: scalable, secure and with integrated monitoring.

Device Management

Registration, provisioning and complete device lifecycle management. OTA firmware updates, fleet grouping, certificate management and health monitoring. Device Shadow/Twin for state synchronisation between device and cloud, even when the device is offline. Compatible with any hardware supporting MQTT, CoAP or HTTP.

Data Pipelines

Ingestion pipelines designed to handle millions of messages per second. Telemetry data is ingested via MQTT broker (AWS IoT Core, EMQX, Mosquitto), processed with real-time rules (Lambda, Kinesis) and stored in time-series databases (TimescaleDB, InfluxDB, Amazon Timestream). Automatic transformation, enrichment and routing based on data type.

Real-Time Dashboards

Device data visualisation with real-time updates via WebSocket. Heatmaps, time-series charts, status indicators and visual alerts. Dashboards customisable by role: operator, engineer, manager. Designed for 24/7 control room screens and mobile devices in the field. See our real-time dashboards solution.

Edge Computing

Data processing at the edge to reduce latency and transmission costs. AWS Greengrass, Azure IoT Edge or custom solutions with Docker containers on industrial gateways. Business logic running locally: data filtering, aggregations, anomaly detection and automatic actions without cloud dependency. Critical data processed in milliseconds, not seconds.

Predictive Maintenance

Machine learning models trained with historical data from your devices to predict failures before they happen. Analysis of vibration, temperature, energy consumption and other variable patterns to detect progressive degradation. Predictive alerts enabling planned maintenance and avoiding unscheduled downtime.

IoT Security

End-to-end security from device to dashboard. Mutual TLS (mTLS) authentication between devices and broker, managed X.509 certificates, data encryption in transit and at rest. Granular access control with per-device, group and action policies. Complete audit of all communications. Industrial standards compliance (IEC 62443).

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Architecture

Cloud architecture for industrial IoT

Each layer designed to scale independently. Managed services where it makes sense, custom components where they add value.

Device Layer

Sensors, actuators and gateways connected via MQTT, CoAP or HTTP. SDKs for major microcontrollers (ESP32, STM32, Raspberry Pi). Automatic provisioning with X.509 certificates.

Ingestion Layer

Managed MQTT broker (AWS IoT Core) or self-hosted (EMQX). Ingestion of up to millions of messages/second. Topic-based routing and business rules. Buffer with Kinesis/Kafka for traffic spikes.

Processing Layer

Real-time processing with AWS Lambda, Kinesis Analytics or Apache Flink. Anomaly detection, temporal aggregations, data enrichment and alert triggering. Batch processing for historical analytics.

Presentation Layer

REST and WebSocket APIs for dashboards and applications. Time-series databases (TimescaleDB, Timestream). Grafana, custom React dashboards or integration with existing client tools.

Protocols & Technologies

Native multi-protocol support

We don't impose a protocol. We choose the right one for each use case and integrate them into a unified platform.

AWS IoT Core Azure IoT Hub MQTT CoAP EMQX Mosquitto LoRaWAN NB-IoT TimescaleDB InfluxDB Amazon Timestream AWS Greengrass Apache Kafka Kinesis Lambda DynamoDB Grafana React Node.js Python

Case Studies

IoT platforms in production

Real IoT projects managing connected devices, processing data in real time and delivering measurable business value.

Spherag

IoT platform with solar-powered devices for agricultural monitoring. Soil, weather and irrigation sensor data ingestion via LoRaWAN. Complete AWS pipeline with real-time processing, automatic alerts and farmer dashboards. Result: 30% reduction in water consumption across monitored farms.

-30% water consumption

WaterScan

Digital platform for sustainable corporate water management. Integration of multiple water consumption data sources, processing and sustainability report generation. Scalable cloud architecture supporting accreditation and reporting according to international sustainability standards.

Sustainability platform

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FAQ

Frequently asked questions about industrial IoT development

What IoT protocols do you support?

We work with the three main industrial IoT protocols: MQTT (the most common, ideal for devices with limited connectivity), CoAP (for ultra-constrained devices over UDP) and HTTP/REST (for gateways and devices with more resources). We also integrate industrial protocols such as Modbus, OPC-UA and BACnet through translation gateways. Protocol choice depends on hardware, available connectivity and latency requirements for each project.

How many devices can the platform manage?

The architecture we design scales horizontally. With AWS IoT Core, for example, there is no practical limit on the number of simultaneously connected devices. The platforms we build are tested to manage from tens to hundreds of thousands of devices. The data pipeline is sized based on expected message volume, with auto-scaling to absorb traffic spikes without performance degradation.

Does it integrate with our existing systems and ERP?

Yes. We design the platform with open APIs from the start. IoT data can be integrated with any enterprise system: ERPs (SAP, Oracle, Microsoft Dynamics), CRMs, MES systems, SCADA or BI tools. We support integration via REST API, webhooks, messaging (Kafka, RabbitMQ) or direct database connectors. We also build custom connectors when the target system doesn't have a standard API.

What happens when a device loses connectivity?

IoT devices are designed to work under imperfect connectivity conditions. We implement Device Shadow (AWS) or Device Twin (Azure) to maintain the last known device state in the cloud. Data is stored locally on the device during disconnections and synchronised automatically when connectivity is restored. MQTT supports persistent sessions and different QoS levels to guarantee message delivery. Additionally, edge computing allows critical logic to run locally without cloud dependency.

How long does it take to develop an industrial IoT platform?

It depends on complexity and scope. A functional MVP with data ingestion, basic dashboard and alerts can be ready in 8-12 weeks. A complete platform with device management, edge computing, predictive maintenance and multiple integrations may require 4-6 months. We work in 2-week sprints with functional demos, so you start seeing value from the first month. The approach is always iterative: launch fast, measure and evolve.

Let's start

Let's build your industrial IoT platform

We help you design the architecture, choose the right protocols and build an IoT platform that scales with your business.

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