Your path to success:
Our project process

With us, it only takes a few steps from data provision to predictive machine maintenance. Thanks to uncomplicated installation, extensive onboarding and clear standardized procedures, you will gain initial insights in a very short time and train your models efficiently.
Dank unkomplizierter Installation, einem umfangreichen Onboarding und einem standardisierten Vorgehen, erzielen Sie in kürzester Zeit erste Erkenntnisse und trainieren Ihre Modelle effizient.
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Successful project workflow with aiomatic

This is what distinguishes project work with aiomatic

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Clear goals & structured approach

Timely milestones and provision of step-by-step instructions
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Comprehensive onboarding & continuous support

With workshops and regular meetings, we steer your project specifically towards success
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Highest safety standards

Your data is stored in the ISO/IEC 27001-certified Microsoft Azure cloud and managed in accordance with GDPR and EU AI regulation.
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“We do not only offer a standardized, ready-to-use product for Predictive Maintenance, but also a holistic approach that helps companies optimize their production processes and grow sustainably.”

Lena Weirauch, CEO & Co-Founder of aiomatic
Photo: Henning von Holdt

The 4 phases to success

An overview of our project process

1

Preparation phase: Data collection

The establishment of data connection is done independently with standardized instructions from aiomatic. Our solution supports various digital interfaces - without additional hardware.
2

Preparation phase: Configuration

In this phase, the data channels are pre-sorted and the machine structure is set up. The aim is to define hierarchical levels of your machine (sub-areas, individual components) on which the health score is to be calculated.
3

Introduction of the application: Optimization cycles​

The models are continuously trained with new data and optimizations are discussed with us in bi-weeklies. Events during plant operation, such as maintenance, faulty production, etc. are recorded and taken into account in training.
4

Introduction of the application: Scaling

A large part of the experience from the pilot plant can be transferred to structurally identical systems, which results in significant cost savings.

The biggest potential for you

Success factors for a quick & successful project start

For easy and fast implementation of our software and maximum added project value, we recommend these basics:
Machine data is already being digitally recorded periodically or on an event-based basis, at least every hour.
Machine data can be made available continuously via a digital interface.
IT staff from your team (possibly also shop floor IT or automation engineers) are available for implementation.
"The set-up with aiomatic was surprisingly straightforward. The seamless integration into existing workflows and the intuitive operation of the dashboard make the solution a real success that quickly delivers added value for our team."
Ben Thurnwald
Managing Director
"The future of maintenance is difficult when it comes to skilled labor. With the AI-based solution from aiomatic, however, this looks much brighter. The team can now focus on other activities, while monitoring and early warning alerts are provided by aiomatic."
Andreas Weber
Technical maintenance manager
“aiomatic is pioneering. For example, the application of AI to real-time data has already predicted imminent warehouse damage for one of our systems. As a result, we were able to act early and avoid an unplanned downtime of more than 8 hours. ”
Dirk Schlamann
Lead Maintenance
"Thanks to the early warning from aiomatic, we were able to detect a gearbox failure in time. As a result, we were able to maintain operations over the weekend with reduced output and prepare for the necessary repair, instead of being surprised by a sudden imminent failure."
Ben Thurnwald
Managing Director
“The aiomatic solution offers a fast and reliable way to achieve a zero-defect goal in the area of predictive maintenance compared to traditional methods.”
Christian Bittel
Inhouse-Consultant
Support

Frequently asked

Here you will find answers to the most common questions about the process of a Predictive Maintenance project.

What are the different solutions offered by aiomatic?

In addition historical data analyses Do we offer one-day Workshops to plan your individual maintenance strategy If you are unsure whether your database is sufficient, we would be happy to provide a Proof of Concept with you so that you can test our software in your live environment. For long-term use of our software, our scalable SaaS model the right choice for you.
What steps are required for data collection?
1. Identify the machine or process that you want to monitor
2. Deploy the virtual machine and provide VPN access for our support
3. Release URLs to connect. Install the IoT Edge software environment.
4. Connect to the OPC UA server of the pilot plant and provide OPC UA information.
5. Set up the network connection between the virtual machine and the data source, including possible port sharing.
6. Use our detailed instructions, which also take into account special requirements such as a proxy.
What human resources are required on the company side?
For successful implementation, you need a plant operator, a person responsible for maintenance/repair, and an IT contact person for network approvals. In addition, a contact person from production may be required to provide OPC UA information.
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Any further questions?

Our mechanical engineer Joel is happy to help you!
Get advice now!
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