Senzary in production today across 8 countries. P&G, AES, ENI, Clarios, Cotemar, Alkhorayef, UPSA, JAX, Corpacero. The same platform — different modules per department.
Problem: Reactive maintenance burning OT budget across multi-country energy generation rotating equipment.
Outcome: Vibration, acoustic, thermal on rotating equipment. Reactive → predictive in 90 days. Now installed in the AES Azure platform with single sign-on.
Problem: Production-line conveyor failures and bucket elevator monitoring across multiple shifts.
Outcome: Predictive maintenance on 300+ production motors. The flagship US RotaryIQ deployment. Live data, fleet-level health scoring, prediction-to-failure timeline per machine.
Problem: Worker safety on H₂S-exposed drill site. Manual XLS-based monitoring. No real-time location for evacuations.
Outcome: Live in production right now. Every shift, 350 employees and contractors. Real-time location, H₂S exposure tracking, evacuation muster. Replaced manual XLS and radio-based monitoring with live, defensible data.
Problem: Baghouse health monitoring in oxide rooms + rotating equipment reliability. EPA exposure risk.
Outcome: Continuous differential-pressure and motor-health monitoring. EPA-defensible audit trail. Manufacturing reliability + air quality compliance on one platform.
Problem: High-value soldering machines and personnel tracked across shore-to-offshore transfers. Asset accountability across vessel boardings.
Outcome: Real-time asset and personnel tracking across shore and offshore platforms. Vessel boarding compliance. Tools tracked at every stage from warehouse to job site (RFID, BLE, LoRaWAN).
Problem: Passenger experience + facilities operations. Smart restrooms, asset tracking, satisfaction polling.
Outcome: Smart restroom occupancy and supply monitoring. Ramp asset tracking. People counting for capacity planning. Passenger satisfaction polling integrated with operations.
Problem: Critical AHU (Air Handling Unit) imbalance impacting production reliability. Need predictive maintenance to schedule repairs during technical shutdowns.
Outcome: Identified AHU imbalance through dashboard. Planned repair during next technical shutdown. Minimum production impact. Thousands of dollars saved.
Problem: Gearbox loose-coupling on lamination train axis risked catastrophic stoppage and operator safety.
Outcome: Identified gearbox loose-coupling. Fixed without stoppage. Saved $32K in revenue/repair/labor. Zero casualties. +$20K saved by reusing IoT network for other projects.
Problem: Multi-site visibility across energy services operations. Existing SCADA + CMMS silos couldn't be unified.
Outcome: On the path AES, ENI, Cotemar are moving to — one operational picture, one operating system. Beyond SCADA, beyond AVEVA, beyond silos.
90 days. Pays back on the first failure avoided. Additive — your SCADA, AVEVA, and CMMS stay where they are.