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Our team includes startup founders, Ph.D researchers, mathematicians, athletes, and musicians. Lots of photographers, too.
We push the boundaries of what’s possible, not just for our technology, but for ourselves. We ship fast, learn from our mistakes, and don’t take ourselves too seriously (just ask Ryan Gosling). We’ve grown revenue by 1,200% since 2018, but we’re just now hitting our stride. Where we go from here is up to you!
"It works so well that one of Topaz Labs’ biggest challenges is convincing customers that the examples on its site are real."
"Topaz taught an AI to accurately sharpen and clarify images even after they've been enlarged by as much as 600 percent."
"Topaz Labs’ Gigapixel upscaling doesn’t just look at neighboring pixels; it looks at whole sections of images at a time."
Deep learning inference is usually far slower than comparable processing methods, but users love fast.
The experimental nature of deep learning defies the traditional software development lifecycle.
The "black box" nature of deep learning models means we often can't predict or explain certain unexpected results.
Customers have hundreds of hardware combinations which significantly impacts how well our products perform on their computers.