Edge AI prediction on the hardware you already have.
No cloud, no GPU, no black box.
Industrial AI without the platform overhaul
A heavy way in
- Large upfront investment
- High annual subscription fees
- Added infrastructure (cloud, GPU, servers)
Start on what you already have
- Small upfront investment
- A fraction of the license cost
- Runs on your existing hardware
Two ways to deploy
In your own equipment
Quality estimation and predictive maintenance run directly on the controllers of your production equipment — on microcontrollers, PLCs, industrial PCs and other devices you already operate.
Spin-out from
DLR German Aerospace Center
Entrox Systems is a German AI company, a spin-out from the DLR German Aerospace Center. We develop edge AI prediction software based on reservoir computing, bringing technology from the research lab to real-world industrial use.
Four solutions
Soft Sensors & Virtual Sensors
Predict what cannot be measured: real-time estimation of hard-to-measure values from existing sensor signals.
Predict hard-to-measure values
in real time, at the edge
Industrial sites generate diverse time-series data from sensors and equipment. Our AI, built on reservoir computing, learns patterns from even small amounts of time-series data, enabling real-time prediction of hard-to-measure values, anomaly detection, and remaining life forecasting.
Deployable as software on your existing equipment — from industrial PCs to microcontrollers. No special hardware required.
Edge AI
Runs on small, low-cost devices. No cloud connection required — your data stays on-site.
Learns from limited data
Builds accurate models from small datasets — no need to wait years to accumulate training data. Adapts immediately to new equipment and new products.
Built for high-frequency equipment
Inference runs in sub-millisecond time — fast enough to keep pace with high-speed machinery and tight control loops, from slow processes to vibration above 10 kHz. Models train in seconds, not months.
Custom optimization
From a single proven core, each product, grade, and operating condition gets its own optimized model — so accuracy holds up across high-mix, low-volume production.
Explainable AI
Model structure is fully visible as mathematical expressions. Prediction rationale can be traced and verified.
Hybrid with domain knowledge
Combine existing physics equations or empirical formulas with AI that corrects the residual error.
How our AI is delivered
Delivered as software that runs on your existing hardware. No extra devices, no cloud connection.
Delivered as code
The trained model is delivered as code, typically C, Python, or similar, generated to suit your target and toolchain. We provide it as source files or as a pre-built library. With no operating-system dependency at the constrained end, the same model runs on bare-metal and RTOS-based microcontrollers and scales up through PLCs, edge gateways, and industrial PCs to Linux edge servers. No GPU, no accelerator, no added hardware.
Our team
Supported by
Customers & Partners
We optimize our edge AI prediction technology for the specific needs of our customers and partners.
Research record
The research group we came out of is based at DLR, the German Aerospace Center, conducting international collaborative research. Published in IEEE, Elsevier, Springer, and other leading journals. An integrated development pipeline from research to real-world deployment.
Start with a feasibility study
We run a feasibility study using your real data to demonstrate effectiveness in your actual environment. Ultimately, you get deployable AI software optimized for your specific process, running on your existing devices. Get in touch to discuss your use case.
