Gaining an edge through AI
in engineering and on the shop floor

AI Engineering: From Feasibility Study to Scaling

As a trusted engineering consulting partner for global industrial companies, we understand both the challenges and the solutions needed to safely and effectively integrate disruptive technologies into production. Together, we identify relevant use cases with tangible business impact, implement them using innovative technologies, and thus create the foundation on which you can continue to modernize your production processes at your own pace.

Expertise in production processes
Matching data with use cases
Data modeling
Solution development
Testing & deployment

AI in Focus: Our Solutions

Analysis of production data

The Challenge:

To avoid rework and downtime costs in production, it is crucial to quickly identify the root causes.

The Solution:

We developed a no-code solution that allows time-based production data to be imported, described, and aggregated. A generated feature ranking highlights which factors are important and supports production managers in deriving appropriate actions.

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Chatbots in production

The Challenge:

Maintenance operations involve high communication and organizational effort, as well as a dependency on subject-matter experts.

The Solution:

Technical documentation becomes easier to use through chatbot integration. Even new experts can quickly and independently contribute to finding solutions on the shop floor.

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Automated process planning in logistics

The Challenge:

Error-prone process in the provision of individual manufacturing parts.

The Solution:

Integration of algorithmic route optimization into an existing planning solution.

Monitoring of pneumatic clamps

The Challenge:

The large number of clamps in body construction makes regular inspections impractical, leading to a reactive maintenance approach that can cause unplanned stoppages and lower OEE (Overall Equipment Effectiveness).

The Solution:

Based on Siemens Industrial Edge technology, motion data from pneumatic clamps is described, collected, and made available for ML models. These models detect and classify anomalies in the data and predict defects, including their most likely causes. Alerts are issued when maintenance actions are required.

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Quality control in the paintshop

The Challenge:

Faulty painting processes frequently cause offline rework.

The Solution:

We developed and tested a quality-oriented AI warning and recommendation system for the early detection and prevention of rework.

Optimized inline inspection with synthetic data

The Challenge:

Error data for AI-supported inline inspection is often only available after series production starts, which delays the complete capture of all possible defect types. Rare defects can only be trained and analyzed if they occur in sufficient quantity.

The Solution:

The use of synthetic data reduces the need for real defect data and enables earlier system deployment even before series production begins.

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What our customers & partners say

“A precise quality assurance is essential to ensure the highest standards in production. Together with inpro, we developed innovative software at Volkswagen for generating synthetic image data to train AI-based quality inspection systems for complex automotive parts. The result is a forward-looking solution with the potential to sustainably optimize our inspection processes.”

Marvin Kresimon , Volkswagen Production | Digital & AI Technologies

Get to know us – as a customer, partner or future colleague!

We look forward to an exciting exchange of experts.

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