AI for industrial operations.
You operate a physical system with a lot of sensor data: a plant, a facility, a fleet of distributed assets. The control layer underneath is doing its job, keeping equipment safe, holding setpoints, responding to alarms. The decisions sitting above that layer are harder. What's actually driving the outcomes in the data, when to do the expensive thing tonight rather than tomorrow, where the operation is drifting before it shows up in the numbers. These are the decisions humans currently make from dashboards, or that nobody is making at all because the data is too messy.
The systems keep evolving. New equipment, new product runs, drift in the existing equipment, new operational priorities. The intelligence layer above the control system has to absorb that, not break under it.
What this looks like
- Diagnostic analysis of operational data. Working the sensor history, maintenance records, and operational events the way a senior operations engineer would: forming hypotheses, testing them against the data, finding what's actually driving the outcomes. Including the recurring problems that nobody has been able to close yet.
- Predictive intelligence layered above the existing control systems. Forecasting what's coming, load, price, demand, weather, and using those forecasts to inform how the plant should operate over the next hours and days. The control layer underneath retains final authority. We send recommendations, not commands.
- State estimation beyond raw sensor readings. What the sensors say is not always what the operator needs to know. We build models that translate raw data into the operational variables that decisions actually depend on.
- Anomaly detection on accumulated baselines. Once enough operational history exists, surface deviations from normal with operational context, not just statistical flags.
- Optimization under operational constraints. Many decisions are constrained by safety, contracts, equipment limits, production schedules. Optimization that respects those constraints rather than ignoring them.
Where this work lands
The pattern fits a range of industrial operations.
- Manufacturing and process: reduced scrap, faster diagnosis when something drifts, faster new product introduction.
- Energy operations in volatile electricity markets: scheduling consumption, generation, and storage against price signals while respecting operational, contractual, and equipment constraints.
- Similar industrial systems with sensor data and operational stakes. Refrigeration operations at scale share many of the same properties: sensor-rich, energy-intensive, with operational consequences when conditions drift.
Why this approach
The existing control layer (PLC, SCADA, historian, equipment-specific controllers) is necessary, and we work with it, not against it. SWNG sits above that layer. We provide the predictive and analytical intelligence the deterministic layer can't, while the deterministic layer retains the authority it needs to keep equipment safe and operations running.
This is harder than it sounds. The data is messy in operationally specific ways: calibration drift, sensor outages, mode changes, missing context. The decisions are constrained in ways that aren't always documented. The plant continues operating regardless of whether the AI is online. The work requires senior engineers with both modern AI fluency and operational comfort, which is an uncommon combination.
The intelligence and models built up over time stay with the operation. They accumulate as the operator's own asset, not a vendor's.
Relevant engagement: the high-precision polymer manufacturing engagement.
Email contact@swng.ai.