Industrial IoT software development, built on the data your plant already generates

We work with OPC-UA, MQTT, Modbus TCP, and REST APIs as the primary connectivity methods. OPC-UA is preferred for modern equipment as it carries rich metadata alongside process values. MQTT works well for high-frequency sensor data. Modbus TCP covers the large installed base of older PLCs. For machines that pre-date digital interfaces, we assess whether signals such as motor run status or fault relay outputs are accessible and design an I/O bridge accordingly.

Overall Equipment Effectiveness is the product of availability, performance, and quality. The software calculates each component from live machine signals and operator inputs rather than manual shift reports, so the number is current and accurate rather than a day-late calculation.

IoT monitoring provides the data layer that predictive maintenance models run on. The monitoring platform captures and stores time-series condition data per machine. Predictive models are trained on that historical data to identify patterns preceding specific failure modes, then run against the live data stream to flag when a machine's condition is trending toward failure.

Yes. The monitoring layer is designed to feed and read from the systems you already run. We push production counts and downtime events up to MES and ERP, and open work orders in a CMMS such as SAP PM or IBM Maximo when a condition threshold is crossed. The normalised data model also serves as the foundation for a digital twin if you plan to simulate line changes later.

We scope in phases. A first monitored line or cell, covering connectivity and a live OEE dashboard, starts around $25,000. A full single-plant rollout with downtime tracking, alerting, and predictive-maintenance data grows to about $60,000, depending on the number of machines, the protocol mix, and whether edge computing is needed. Multi-site expansion after the first plant costs less per site.