NTAR Growing Faster Than NVDA and AI

Nextech3D.ai (CSE: NTAR) or NEXCF is one of the fastest growing publicly traded AI companies. In their most recent quarter: NTAR saw its revenue grow by 155.03% year-over-year. In comparison, C3.ai (AI) saw its revenue grow by 0.13% year-over-year and Nvidia (NVDA) saw its revenue decline by 13.22% year-over-year.

NTAR is positioned as the leader for 3D modeling in the global $5.5 trillion e-commerce ecosystem. The company's 3D modeling success and ability to meet market demand is being fueled by NTAR achieving significant breakthroughs in Generative AI. In October 2022, privately held AI start-up Stability AI achieved a US$1 billion valuation in its latest VC funding round due to their successful launch of its Stable Diffusion AI technology. Stability AI is expected to significantly increase its valuation in their new upcoming funding round to US$4 billion, and what NTAR is doing is much more important. In March, NTAR filed a patent titled: "Generative AI for 3D Model Creation from 2D Photos using Stable Diffusion with Deformable Template Conditioning."

Stability AI is one of the world's hottest AI companies due to its use of Stable Diffusion AI technology to generate 2D images from text prompts. NTAR is much more advanced than Stability AI because NTAR is creating 3D Models from 2D Photos.

Stability AI's 2D images from text prompts are randomly generated with mixed results, because they can look like literally anything! NTAR must generate 3D Models that look like the 2D Photos, which makes it a much more complicated type of AI technology!

There are hundreds of millions of products sold on e-commerce web sites like Amazon (AMZN) that will need to convert to 3D Models! Already in 1Q 2023, NTAR delivered 20,000 3D Models to AMZN, which is only the tip of the iceberg! The more 3D Models that NTAR creates, the better all of their future 3D Models from 2D Photos automatically become!

NTAR's machine learning and computer vision AI technology employs diffusion models to analyze datasets and uncover patterns in the data. Over the last several years, NTAR has been building tens of thousands of high-quality, fully textured, 4K photo-realistic 3D assets, with millions of individual parts. These 3D parts get harvested into NTAR's "3D parts library", synthetically rendering them from random views, and using them to train new diffusion models that are able to autonomously reconstruct 3D mesh parts from reference photos!

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