Nanoprecise

Nanoprecise is an industrial predictive and prescriptive maintenance platform that combines wireless machine-health sensors, AI and physics-based condition analytics, and newer agentic reliability software to detect equipment faults, diagnose likely causes, and estimate remaining useful life.

1. Machine monitoring: MachineDoctor is a wireless 6-in-1 sensor that measures triaxial vibration, acoustic emissions, temperature, true RPM, humidity, and magnetic flux. Wi-Fi and cellular versions transmit machine-health data for analysis, while edge processing and configurable sampling support continuous monitoring without a permanently wired installation.
2. Diagnostics and prediction: RotationLF combines AI, physics-based models, and signal processing such as CEEMDAN to detect anomalies, identify affected components and fault modes, suggest corrective actions, and estimate remaining useful life. It is sensor-agnostic and can also analyze compatible data from existing monitoring hardware rather than requiring MachineDoctor for every deployment.
3. ReliabilityOS and integrations: launched in June 2026, ReliabilityOS adds agentic AI that combines sensor patterns with asset history, maintenance notes, operating context, and site knowledge to produce evidence-backed investigations for human review. Its open architecture supports CMMS, APM, enterprise copilots, and MCP-connected workflows, while Nanoprecise also documents open-API connectivity with ERP, SCADA, and cloud data platforms.
4. Asset and commercial fit: Nanoprecise is used across rotating equipment including motors, pumps, gearboxes, compressors, bearings, turbines, generators, fans, and related industrial assets. Standard public prices are not listed; commercial terms and fees are defined through customer orders, with scope depending on the required hardware, analytics, integrations, deployment, and support.

Nanoprecise is most relevant to industrial reliability and maintenance teams that want automated machine-condition monitoring to progress beyond anomaly alerts into fault diagnosis, remaining-life estimation, and context-rich reliability decisions.