Inline quality control supported by decentralized AI

Detect quality issues early and save costs and resources by using decentralized AI without sharing sensitive data.

The right Quality

Produce exactly the quality your customers are looking for. The specification of a part can include geometric specifications, physical properties, service life, quality, sustainability, price specification. With Katulu, you can use Produce-To-Model to optimize a part for its future use. The model you train with Katulu FL Suite describes the correlation between the part and how it will function in the final product. This ensures that you meet exactly the quality that your customer needs and spend exactly the resources required, giving you a price and cost advantage.

Cost-Effective Quality

With the Katulu FL Suite you can train an adaptive decentralized quality system that uses energy and material resources optimally for each customer. Faulty products are detected early and excluded from the production process. With Katulu, process adjustments can be made on the basis of live data during ongoing production to significantly reduce waste.

AI without sensitive Data

To achieve excellent results in AI-based production, a great deal of data from as many different industrial users as possible is required. Instead of collecting this data in a centralized way, Katulu takes a different approach with Katulu FL Suite : the data remains in the production environment and is processed locally. This ensures compliance with data regulation and customer privacy. Katulu allows you to train machine learning models in a decentralized way across organizational boundaries without sharing sensitive data.

AI-based Injection Molding

Learn how process cability in injection molding is optimized across company boundaries with federated learning by Katulu - without data transfer from production sites.