Maintenix AI ingests live sensor streams from IoT-enabled machines, PLCs, and SCADA systems; runs LSTM, Isolation Forest, and Autoencoder anomaly detection; and forecasts component failures days in advance. Work orders trigger automatically — before the breakdown.
Where Maintenix AI fits — capital-intensive operations where unplanned downtime carries six-to-seven-figure consequences.
Calendar-based maintenance replaces components that don't need replacing. Reactive maintenance fixes things after they break — usually at 2am on a Friday. Neither approach uses the data the equipment is already streaming.
Maintenix AI uses that data. Real-time vibration, temperature, pressure, and energy signals flow into anomaly detection models calibrated to each asset class. The system learns what "normal" looks like for your equipment, then flags deviations days before they become failures. Remaining Useful Life (RUL) forecasts drive maintenance schedules; automated work orders flow directly to your CMMS, SAP PM, or Maximo.
The economic logic is simple: it costs less to fix a bearing showing early signs of failure than to fix the bearing, the shaft, and the production lost while you do it.
Layered design, production tooling, native Azure integration. Every component is one we use in shipping client systems — not a theoretical reference stack.
Outcome ranges are illustrative — based on structural economics of the problem and what comparable production systems achieve. Actual results depend on baseline maturity, data quality, and integration depth.
Our products are designed to compose. Maintenix AI works standalone, but most enterprise engagements combine three or four — built on a shared data foundation and a single Azure tenant.
Tell us about your current setup and the outcome you'd want from Maintenix AI. We'll come back within one business day with a path forward.