Predictive maintenance and industrial IoT, built for the plant floor.
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.
One team across the stack for plant-floor IoT.
Hardware, firmware and the cloud pipeline get scoped together, so data quality and sensor coverage are handled before the model ever becomes the bottleneck.
- Embedded Hardware
- Firmware
- Cloud & Device Platforms
- Mobile & Web Apps
- AI & Intelligence
Where AI accelerates
- Component selection
- Driver scaffolding
- Data-processing & feature-extraction scaffolding
- Dashboard/query scaffolding
Where it stays human
- DFM review
- Watchdog/health-monitoring threshold tuning
- Model selection & accuracy/recall tradeoffs
- Production monitoring & alert thresholds
The full stack, scoped for a plant-floor deployment.
Embedded Hardware
Ruggedized enclosures and sensor selection built to survive a plant floor, shaping component choice from the first schematic.
Explore hardware →Firmware
Edge-level pre-processing so raw sensor data doesn't have to make the round trip to the cloud to be useful.
Explore firmware →Cloud & Device Platforms
Fleet-wide telemetry and predictive-maintenance pipelines that scale from a pilot to the full plant.
Explore cloud →Mobile & Web Apps
Facilities-facing dashboards that turn sensor data into decisions, not just charts.
Explore mobile & web →AI & Intelligence
On-device or cloud, chosen by the constraint — real-time detection at the edge, fleet-wide pattern detection in the cloud.
Explore AI & Intelligence →What actually shapes a predictive-maintenance architecture.
Matching sensors to failure modes
Sensor choice follows the physics of the failure, not a default kit.
- Bearing wear → vibration & acoustic signature
- Winding degradation → current draw & thermal signature
- Cavitation → pressure & flow anomalies
- Sampling rate follows signal speed — fast for vibration, sparse for thermal drift
Modeling before labeled failures exist
Programs run in two phases, not one model from day one.
- Phase 1 — anomaly detection against a healthy baseline, no failure labels needed
- Phase 2 — supervised failure-prediction classifier once field failure data accumulates
Retrofitting equipment never built to be instrumented
The first question is always whether the data already exists somewhere.
- PLC-controlled equipment → often exposes data over Modbus, sometimes OPC-UA
- No protocol access → non-invasive add-ons: current clamps, external vibration/acoustic sensors, surface-mount temperature sensors
Ruggedization decisions that shape the schematic
Target IP/NEMA rating gets decided early — everything else follows from it.
- Drives thermal derating, sealed connector choice, touch-vs-exposed-button tradeoffs
- Conformal coating protects solder joints; underfill/mechanical support for BGA and fine-pitch parts in high-vibration designs
- EMI shielding & filtering near motor drives and VFDs
Real builds in industrial and building IoT.
Pulled from our case-study library — same AI/human split shown in full, industry context included.
Building-Wide Occupancy & HVAC Optimization Platform
US-based building-management / energy-efficiency technology companyThe platform combines occupancy and environmental sensing across a building to understand space utilization and provide intelligence for more efficient HVAC operation.
Battery-Free Ambient Room & Occupancy Sensor
US-based smart-building / IoT startupA battery-free environmental sensor detects room conditions and occupancy while minimizing energy consumption and maintenance requirements.
What we've written about sensors, retrofits and plant-floor IoT.
Predictive Maintenance 101: What Data You Actually Need
Predictive maintenance fails more often from insufficient or low-quality data than from model choice.
Read the article →Predictive Maintenance for Industrial Equipment
The model is the easy part — sensor coverage, data quality, and organizational trust decide whether the program survives past the pilot.
Read the article →Designing Ruggedized IoT Hardware for Harsh Industrial Environments
The requirements that make a device survive a plant floor need to shape component selection from the first schematic.
Read the article →Retrofitting Legacy Industrial Equipment with IoT Sensors
The first decision is whether to add sensors at all, or just read the data the equipment already has.
Read the article →What people ask before scoping an industrial IoT deployment.
Can you add AI to equipment or products we've already built?
Yes — this is one of our most common standalone engagements: adding predictive maintenance or on-device inference to hardware and firmware that already ships.
Is this on-device or cloud AI?
Both, chosen by the constraint — real-time and privacy-sensitive workloads run on-device; fleet-wide pattern detection runs in the cloud.
Do you retrofit sensors onto equipment that was never built to be instrumented?
Yes — the first decision is always whether to add sensors at all, or just read the data the equipment already has.
Which RTOS / wireless stacks do you work with?
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.