AI for the systems
that keep the lights on.
Real-time monitoring and predictive maintenance. Stable workloads on the hardware already in your substations and control rooms.
[ The Constraint ]
Uptime is not negotiable.
Grids, treatment plants, and rail networks run 24/7. Downtime is a public safety event. The AI needs the same reliability as the SCADA it sits alongside.
These sites are OT-isolated. The hardware is what was procured three years ago. No GPU cluster budget. No SRE team. AI runs on what is already there, or it does not run.
[ Use Cases ]
AI that runs like infrastructure.
Fig 1.1
Grid monitoring and load prediction
Predict demand spikes and identify equipment stress before failure. Process SCADA telemetry on substation compute.
Fig 1.2
Water quality and treatment
Continuous quality analysis from sensor arrays. Predict chemical dosing. Detect contamination early. Runs on existing plant hardware.
Fig 1.3
Transport and rail
Predictive maintenance on rolling stock and signals. Process vibration data at lineside cabinets. Reduce unplanned disruptions.
Fig 1.4
Asset condition monitoring
Transformers, pumps, valves. Condition-based maintenance from existing sensors. Extend asset life. Reduce outages.
[ Why Sector88 ]
Designed for OT environments.
OOM protection
Never crashes the host. Graceful degradation under memory pressure. SCADA untouched.
Runs on existing hardware
Memory tiering runs large models on whatever is already in the substation. No GPU cluster.
Disconnected by default
Inference runs indefinitely without connectivity. OT isolation respected at every level.
Deploy on your infrastructure.
Tell us your hardware and your workloads. We will show you what runs on what you already have.