How We Work

From Your Data to
Deployed AI Software

A structured, low-risk process for evaluating and deploying edge AI prediction technology on your existing equipment. Start small, validate fast, scale with confidence.

Four Steps to Deployment

1
Understand

Technical Discussion

We begin with a focused discussion to understand your process, existing sensors, and what you want to predict or detect. Together we identify which approach fits your challenge best.

No commitment required. This step helps both sides evaluate whether our technology is the right fit.

Duration1-2 meetings (online or on-site)
Your effortShare process overview and available sensor list
OutputFeasibility assessment and proposed demo scope
2
Validate

Rapid Benchmark

Using a sample of your real process data, we develop a working AI model and benchmark it against your target metric. Our technology trains in seconds — you see concrete results within days, not months.

This is the moment you see whether the technology works on your specific process, with your actual data.

DurationUp to 1 week (depending on data readiness)
Your effortProvide a representative data sample (time-series format)
OutputBenchmark report with accuracy metrics and model performance
3
Customize

Proof of Concept

Once the benchmark confirms feasibility, we develop a production-grade AI model optimized for your specific process conditions, sensor configuration, and target hardware. This includes hyperparameter optimization, validation across operating conditions, and performance documentation.

Duration2-8 weeks
Your effortProvide full dataset and domain context
OutputOptimized AI model, performance report, deployment plan
4
Deploy

Edge Deployment

The trained model is compiled into lightweight software designed to run directly on your existing equipment — industrial PCs, PLCs, SCADA systems, or microcontrollers. No cloud subscription, no GPU, no new hardware. Your data stays on-site, and inference runs in sub-millisecond time.

DeliverableDeployable AI software for your target hardware
InferenceSub-millisecond on standard industrial devices — no GPU required
ScalingAdapt a proven model to new units, lines, or sites with minimal additional data

Project Outcomes

The goal of each project is deployable AI software — not a dashboard, not a cloud subscription.

Trained AI Model

Optimized for your process data. Compiled as lightweight software ready to run on your target device.

Performance Report

Accuracy metrics, validation results, and comparison against baseline methods. Clear evidence that the model works.

Integration Documentation

Technical documentation for integrating the AI software into your existing control system or data pipeline.

Why This Process Works

Low entry barrier

Start with a non-binding discussion and a small data sample. No infrastructure setup, no long-term contract to begin.

Fast time-to-evidence

Our AI trains in seconds. You see benchmark results on your own data within days — not the months typical of deep learning projects.

No vendor lock-in

The result is software that runs on your hardware. No cloud dependency, no ongoing subscription required for inference.

Scalable by design

Once a model is proven, it can be adapted to new units, lines, or sites with minimal additional data — reducing rollout time from months to days.

Ready to Start?

Tell us what you want to predict, detect, or forecast. We can assess feasibility together and identify a concrete next step — typically a rapid benchmark on your data.

Contact Us → Have questions? See our FAQ →
Impressum
Published by: Entrox Systems, Kehl, Germany.
Represented by: Tamon Nakano, Kinzigstraße 59, 77694 Kehl, Germany. Email: info@entrox-systems.com