Physna utilizes geometric deep learning algorithms to search, compare, and analyze 3D models, enabling precise identification and evaluation of geometric features. This technology addresses the challenge of efficiently managing and retrieving complex 3D data in industries such as manufacturing and design.
Funding
$84.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
TGFounders
Product
Problem
Managing and retrieving complex 3D models is challenging for organizations, especially when dealing with a lack of standardization and the absence of bill of materials (BOM) data. This can lead to duplicated design efforts, inefficient supply chains, and difficulties in part identification and sourcing.
Solution
Physna offers a geometric search engine that uses AI and geometric deep learning to analyze, compare, and identify 3D models. The platform automatically catalogs 3D models based on their unique geometry, enabling users to search for parts and components without relying on metadata or BOMs. Physna facilitates part standardization, automates supply chain insights, and helps users avoid redundant designs by quickly identifying existing models. The system can be used as a standalone solution or integrated into existing CAD, PLM, PDM, and ERP systems via API.
Target Audience
Physna targets supply chain, manufacturing, and engineering organizations that need to efficiently manage, search, and analyze large volumes of 3D models.
Features
- Geometric deep learning algorithms for 3D model analysis and comparison
- Automated part identification and sourcing, regardless of naming conventions
- Similarity visualization for identifying duplicate parts and matching to suppliers
- AI-driven predictions on part cost, manufacturability, performance, and materials
- Automatic catalog creation of 3D models without requiring a bill of materials
- Integration with CAD, PLM, PDM, and ERP systems via API