Matchmaking Supply and Demand for Parts Manufacturing w/ Markus Seibold

Sourcing & supply chainMakerVerse AI/ML modelsreduced quote time from 3 days to instant

MakerVerse uses machine learning to analyze patterns in geometry, material, and finish to build statistical models that generate instant quotes without manual engineering, reducing quote time from days to seconds.

“What you rely on. Instead, based on artificially intelligent and machine learning, you understand patterns and geometries. You understand patterns of combination of material, technology, finish, and geometries. And then you basically build statistical models, where you say if I run a 1000000 data points and I always find these correlations, then this pattern on this material in this finish costs you, I don't know, X dollars.”

Sourcing & supply chainMakerVerse AI matching layer

MakerVerse applies an AI layer to procurement data to account for part history, supplier performance, and external supply chain disruptions, providing recommendations on where to source parts to improve decision effectiveness.

“And when you then can rely on also artificial intelligence that helps you take into account, Both part related, past delivery performance related, but also industry factors, and then make you recommendations on where to source which part, then I think you can just be a lot more effective, not necessarily efficient, but effective in taking the right supply chain decisions.”

Sourcing & supply chainMakerVerse AI translation

MakerVerse uses AI to translate unstructured customer requests (like emails with CAD files) into structured data, bridging the gap between buyer workflows and the platform without requiring complex IT integration.

“And so if I find a way of bridging, between The buyer's workflow, and my workflow, without the effort of a lot of IT integration, simply by putting basically translation pieces based on AI in between, I can basically say, I give you access to my technology, but based on the way how you are used to work.”