Raw sensor data becomes real-time decisions on-prem, where the data lives. One foundation, engineered per industry.
Deep-tech operations generate more sensor data than any team can read, and the most critical decisions can't wait on a round-trip to a data center. Turbnetic puts the model next to the machine: low latency, full data sovereignty, and resilience even when the network drops.
Each vertical is a purpose-built product on the same core: physics-aware models running on the operator's own infrastructure. Start with energy, expand into every deep-tech domain.
Predictive maintenance & fleet intelligence for gas-turbine and powerplant operators. Connects to existing SCADA & historians and detects failures weeks before they happen.
Edge AI for clinical monitoring and medical-device intelligence. Anomaly detection at the bedside. Patient data stays inside the building, fully compliant.
Anomaly detection and process optimization for manufacturing, mining and plant machinery, turning legacy sensor networks into a live decision layer.
Autonomous sensing and yield intelligence for precision agriculture. Edge inference in the field, no connectivity required.
We adapt the edge-AI foundation to new industries, anywhere sensor data outpaces the humans reading it.
From auto-selected models to on-device compression: the full pipeline that lets a physics-aware model run next to the machine. Offline, private, in milliseconds.
Per-asset AutoML picks and tunes the right model for each machine's signature. No data-science team required.
Quantization and pruning shrink models up to 8x so they run on low-power edge NPUs without losing accuracy.
Cross-correlates vibration, thermal, acoustic, pressure and electrical channels into one health state.
Sub-15 ms predictions right at the machine. No cloud round-trip, no downtime when the link drops.
Nightly on-prem retraining keeps every model sharp; raw data never leaves the site.
One deployable runtime spanning ARM, x86 and dedicated NPU accelerators. Install in hours.
In 1945 Picasso drew the same bull eleven times, removing more with each pass: muscle, then mass, then detail, until a single unbroken line remained. We build the same way: models distilled, interfaces stripped, systems reduced until only the signal is left.
Eleven passes. Raw data in, one signal out. The decision that remains is the product.
Tell us about your assets and data, and we'll show you what a purpose-built edge-AI product looks like for your operation.
Or see it live · Turbofan →