Most industrial IoT projects aren't greenfield builds — they're adding sensing to equipment that's been running for years and was never designed to be instrumented. Rivix scopes hardware, firmware and the cloud pipeline together, so the model isn't the bottleneck — data quality and sensor coverage are handled first.
Ruggedized enclosures and sensor selection built to survive a plant floor, shaping component choice from the first schematic.
Explore hardware →Edge-level pre-processing so raw sensor data doesn't have to make the round trip to the cloud to be useful.
Explore firmware →Fleet-wide telemetry and predictive-maintenance pipelines that scale from a pilot to the full plant.
Explore cloud →Facilities-facing dashboards that turn sensor data into decisions, not just charts.
Explore mobile & web →On-device or cloud, chosen by the constraint — real-time detection at the edge, fleet-wide pattern detection in the cloud.
Explore AI & Intelligence →Bearing wear shows up in vibration and acoustic signatures; motor winding degradation shows up in current draw and thermal signatures; a pump cavitating shows up in pressure and flow anomalies. Sampling rate follows the physics — a high-frequency vibration signature needs fast sampling to resolve, while slow thermal degradation tolerates infrequent sampling.
Programs typically start with anomaly detection against a healthy baseline, since no failure labels are needed yet, then move to a supervised failure-prediction classifier once real labeled failure data accumulates from the field.
Equipment with a PLC controller often exposes operational data over Modbus, and newer or upgraded installations may support OPC-UA. Where that's not available, non-invasive sensors — current clamps for motor load, external vibration/acoustic sensors, surface-mount temperature sensors — add sensing without an electrical connection into the equipment.
Target IP/NEMA rating gets decided early, since it drives thermal derating, sealed/gasketed connector selection and capacitive-touch-vs-exposed-button tradeoffs. Conformal coating protects solder joints; BGA and fine-pitch components need underfill or mechanical support in high-vibration designs; EMI shielding and filtering matter near motor drives and VFDs.
Pulled from our case-study library — same AI/human split shown in full, industry context included.
The platform combines occupancy and environmental sensing across a building to understand space utilization and provide intelligence for more efficient HVAC operation.
A battery-free environmental sensor detects room conditions and occupancy while minimizing energy consumption and maintenance requirements.
Predictive maintenance fails more often from insufficient or low-quality data than from model choice.
Read the article →The model is the easy part — sensor coverage, data quality, and organizational trust decide whether the program survives past the pilot.
Read the article →The requirements that make a device survive a plant floor need to shape component selection from the first schematic.
Read the article →The first decision is whether to add sensors at all, or just read the data the equipment already has.
Read the article →Yes — this is one of our most common standalone engagements: adding predictive maintenance or on-device inference to hardware and firmware that already ships.
Both, chosen by the constraint — real-time and privacy-sensitive workloads run on-device; fleet-wide pattern detection runs in the cloud.
Yes — the first decision is always whether to add sensors at all, or just read the data the equipment already has.
FreeRTOS, Zephyr and bare-metal on the RTOS side; BLE, Wi-Fi, LoRa, Zigbee and cellular on connectivity, chosen based on your power and range constraints, not by default.