Predict package: predictive maintenance with AI

The best predictive maintenance software to identify machine problems early on and prevent failures:
Real-time insights into machine conditions: AI health analytics and alerts
Optimize maintenance cycles, avoid failures & reduce maintenance costs
Ready for immediate use & scalable: No additional hardware
Dashboard mockup

When should you choose the Predict package?

Integration icon
You want to gain real-time insights into your machines' health
Integration icon
You want to detect potential faults at an early stage
Integration icon
You want to
optimize your processes & save maintenance costs

Your benefits at a glance

Easy & fast implementation: Seamless integration with existing IT systems
Early fault detection: Avoid breakdowns
Cost savings: Reduce maintenance costs & extend the life of your machines
Hohe Skalierbarkeit: Algorithmus problemlos auf andere Maschinen erweiterbar
Predict
15€
per month & data channel
Nonrecurring setup fee: €2.500*
Minimum duration: 24 months
Plus additionally hardware (if required)
*Data connections that are not part of our standard setup are connected individually at a daily rate of €960. Additional required hardware is not included in the price.

Features of our predictive maintenance software

Visualization of historical and real-time sensor data
With our dashboard, you can keep track of important machine KPIs such as vibration, temperature or speed.
Context-dependent precise machine monitoring
When monitoring, our software takes actual operating conditions, such as product recipes, into account.
AI-powered fault detection & forecasting
Our AI detects relevant discrepancies and alerts you to potential errors before problems arise.
Individually configurable alerts
You receive automated email alerts in real time as soon as a machine's Anomaly Index drops.
Which maintenance solution is the right choice for you?
Find out in just 2 minutes how well your company is already positioned -
and which solution perfectly fits your requirements.
Answer the questions now and take your maintenance to the next level.

Which machines are suitable and what errors can our software detect?

Use cases:
Extrusion
Compactor
Packing machine & in-line coating machine
Milling, conveyor belt
Production line, agitator, continuous furnace
Typical mistakes:
Bearing & transmission damage
Wear, clogging, contamination
Pressure & temperature issues
Sensor misalignment
Production-related material removal

Your dashboard within the Predict package

All features of the Monitor package
Model overview
Display of Anomaly Index
Konfiguration von Warnmeldungen
Dashboard mockup
How it works:

Predictive maintenance system for your machines

Evaluation of the data basis:

Review of existing machine data for completeness and accuracy to ensure a solid basis for analysis

Fast implementation:

Integration of machine controls through digital interfaces such as OPC UA for quick and easy installation.

Realtime-Overview:

Visualization of data in real time on our dashboard, which allows instant analysis and monitoring

Smart maintenance:

Early fault detection using our AI software, which automatically sends alerts to initiate proactive maintenance measures.

Continuous optimization of models:

The AI-based software is constantly evolving by learning from the collected data.
The self-learning technology enables long-term optimization of the maintenance strategy.
Ensuring energy supply and efficiency
Requirements on energy suppliers are increasing — traditional maintenance is reaching its limits. In our white paper, we explain how AI optimizes processes and redefines machine monitoring in the energy sector.
Why traditional machine monitoring is reaching its limits
How AI monitoring effectively protects complex components from failures
What to consider when getting started with AI-based monitoring
Ready for the future of energy supply?
Discover efficiency potential now.
Monitoring of energy systems
Thank you We have received your message and will get back to you as soon as possible!
Unfortunately that didn't work! Please check that all mandatory fields have been filled out!