Next-Generation Industrial AI:
Up to 1000x Faster
0% Cloud Dependency for Machine Learning
Predict, optimize, and explain operational decisions locally, in milliseconds.
Three Silos. One Engine.
One local engine turns the same data stream into maintenance, optimization, and Digital Twin intelligence.
Predictive Maintenance
Early-warning health alerts based on physical signal drift, catching failures weeks before they stop production.
Operational Optimization
Real-time set-point recommendations for energy and yield: boiler gas savings, chiller efficiency, process tuning.
Digital Twin
A real-time, on-premise model of your process that correlates sensor relationships to pinpoint the root cause.
The Unfair Advantage: Up to 1000x Faster Machine Learning
The Problem
Conventional Machine Learning can depend on GPU infrastructure, batch processing, and cloud round-trips.
Our Tech
Event-driven signal inference. Lightweight trigger-logic activates compute only when sensor relationships deviate from the physical normal.
The Result
Up to 1000x faster model execution. Multiple Digital Twin agents run on a standard industrial PC, no cloud-scale compute required.
To paraphrase Archimedes: “Give us enough data and enough compute, and we will simulate the world.”
From White-Box Super Model to Operational Action
Raw signals become a clear decision your team can act on.
White-Box Super Model
Every model is transparent and physics-aware. You can see exactly which sensor relationships drove the conclusion, no black-box scoring.
Analytical Insight
The engine explains the cause: "Discharge pressure is drifting because inlet temperature and valve position are out of their normal relationship."
Operational Recommendation
A concrete action for the shift team: what to adjust, which asset to inspect, and the expected impact on energy or uptime.
How IoTGPT stacks up against traditional industrial AI platforms, point by point.
Latency
IoTGPT: Sub-second local inference, insights arrive while the event is still happening.
The alternative: Minutes to hours of cloud round-trips and batch processing.
Data Volume
IoTGPT: Processes sparse physical anomalies from the data you already collect.
The alternative: Requires massive raw data lakes and months of data engineering.
Hardware
IoTGPT: Runs on your existing industrial PCs. Zero new sensors or gateways.
The alternative: Demands edge-compute racks, GPU clusters, or new sensor rollouts.
Security
IoTGPT: Fully on-premise or air-gapped. Your data never leaves your network.
The alternative: Dependent on 24/7 internet connectivity and third-party cloud servers.
Immediate Value, No Training Lag
Physical-relationship mapping lets the engine understand how pressure, temperature, and flow relate from day one, so actionable insights can arrive within 48 hours of connection.
Don't sell dashboards, sell insights.
Deploy an engine that explains the solution.
Request the 1000x Benchmark for Your Machine Line