Personomic relies on 3D printing and parametric variants for custom-fit bicycle saddles

The Stuttgart start-up Personomic tackles a classic bike-fitting challenge with additive manufacturing: instead of offering saddles as a one-size-fits-all product, they are tailored to user profiles through a short digital onboarding process. The basis is a combination of ten questions about anatomy and riding style plus a self-measurement via an A4 imprint captured with a smartphone. From the data, an algorithm selects one of 32 variants formed from eight widths and four firmness/damping levels. The goal is more even pressure distribution in the sit-bone area and a reproducible fit across different use cases.

Manufacturing takes place in Stuttgart via 3D printing, after which the components are finished and, on request, laser-engraved. The manufacturer emphasizes a single-material, adhesive-free construction to facilitate circularity, as well as the use of 100% green electricity and European supply chains. According to the company, riders can try the saddle risk-free: if the chosen combination doesn’t fit, an exchange is offered until the configuration is right. At market launch, the price is €249 as an autumn offer.

Technically, Personomic uses a parametric product architecture rather than fully bespoke manufacturing. According to the company, internal evaluations and collaboration with Velometrik—a bike-fitting provider specializing in saddle pressure mapping—showed no relevant comfort advantage over the finely graded variant logic, while lead times drop significantly. The solution emerged from the Mass Personalization performance center at the University of Stuttgart and targets road, gravel, trekking, and MTB.

For the AM industry, the approach is another example of how 3D printing links short development and supply chains with individualized specifications. Instead of one-off custom builds, defined parameter spaces are used that can be scaled industrially. Whether the promised pressure relief holds up in everyday use will be shown by practical tests with different rider types; the optional fine-tuning via the comfort guarantee provides a secure framework for this evaluation.