Physna

About Physna

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.

```xml <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. </problem> <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. </solution> <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 </features> <target_audience> Physna targets supply chain, manufacturing, and engineering organizations that need to efficiently manage, search, and analyze large volumes of 3D models. </target_audience> ```

What does Physna do?

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.

Where is Physna located?

Physna is based in Columbus, United States.

When was Physna founded?

Physna was founded in 2015.

How much funding has Physna raised?

Physna has raised 84900000.

Location
Columbus, United States
Founded
2015
Funding
84900000
Employees
33 employees
Major Investors
Tiger Global Management

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Physna

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Executive Summary

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.

physna.com3K+
cb
Crunchbase
Founded 2015Columbus, United States

Funding

$

Estimated Funding

$50M+

Major Investors

Tiger Global Management

Team (30+)

No team information available.

Company Description

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.

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

Target Audience

Physna targets supply chain, manufacturing, and engineering organizations that need to efficiently manage, search, and analyze large volumes of 3D models.

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